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S E P T 1 0 A U G 1 1
Oxford-Man Institute of Quant i tat ive F inance
A n n U A l R E P o R T
The Oxford-Man Institute would like to
acknowledge the extraordinary support
of Man Group plc that has generously
provided our core funding for the period
2007-2015, and more generally for its
wider support of the University of Oxford
including an endowment for the post of
Man Professor of Quantitative Finance.
w E l c o m E
In this report I am pleased to be able to share with you details
of the research interests of the Institute’s members, associate
members and students. OMI is fast gaining a reputation as a
global centre of excellence in the study of quantitative finance
and alternative investments, and the work of our colleagues in
this field continues to draw keen interest from academics and
industry practitioners around the world.
In the subsequent pages you will have an opportunity to learn
more about our members, and this year we have included
extended discussion on the research of three colleagues; Tarun
Ramadorai (page two) discusses his interest in international
financial contagion, whilst articles on Ben Hambly (page six) and
Kevin Sheppard (page four) explain their respective research
concerning the volatility of commodities and financial markets.
OMI’s events programme is an important facet of the Institute,
enabling members and students to better position themselves
to contribute to the development of quantitative finance. Over
the past year we have hosted numerous seminars, conferences
and workshops, as well as a Summer School. We also initiated
a series of thematic workshops preceded by relevant tutorials
to provide our students with the opportunity to broaden and
increase their knowledge of specific subjects. Details of some of
these events can be found in this report.
The past academic year has not only proven to be very
successful, but it has been a time of change for OMI. In July our
founding Director, Professor Neil Shephard stepped down and
Professor Terry Lyons was appointed as his successor.
I speak on behalf of all my colleagues when I say it is an
emotional moment to see Neil stepping down as Director, and
we are all very grateful for the work he has completed over the
last four years. His vision and leadership have been instrumental
in securing OMI’s current status and success. We are very
pleased that he will continue to be involved in the Institute in
his new role as leader of Financial Econometrics and Statistics.
We are also delighted to welcome Terry Lyons as the new
Director. Terry has been with OMI since its inception and has
played an active role in the Institute’s life, supervising students,
organising seminars, serving on the Executive Committee and
integrating the stochastic analysis group. I know that Terry
is delighted to be the Institute’s new Director and is looking
forward to building on OMI’s global reputation.
It is very difficult to capture the contributions, scientific value,
dedication and academic stature of both Neil and Terry, but we
have included a dedicated insert in this report which we hope
will provide some insight into both Directors’ involvement in
the Institute.
I would like to take this opportunity, on behalf
of all my colleagues, to thank Man Group Plc
for their continued funding and support
– without which, the progress highlighted
herein would not have been possible.
Thaleia Zariphopoulou
man Professor of Quantitative Finance
August 2011
Welcome to the fourth Annual Report from the Oxford-Man Institute (OMI), highlighting
current research activities and the progress that has been made developing interaction
among research disciplines.
1
INTRO
TA R U n R A m A d o R A i
The old maxim has never seemed more accurate:
when the US sneezes, the rest of us catch a
cold. What starts out as a blip in an obscure
derivatives market can quickly spiral into a
financial crisis that affects everyone, from the
biggest governments to the poorest citizens.
We need to understand how these panics
spread to prevent them in the future. And that’s
where thinkers like Tarun Ramadorai come in.
An economist by training, Ramadorai is a Reader at the Saïd
Business School and a member of the Oxford-Man Institute.
He spends his professional life thinking about questions that
combine theoretical complexity with immediate relevance to
policy and regulation.
The study of international financial contagion has been one of
his major interests in recent years. He wants to understand how
a crisis moves from market to market, often striking in areas with
no obvious connection to the original source of the problem.
After the financial turmoil of recent years, it’s an immensely
topical subject. It helps explain how panic rippled out from a
downturn in the US housing market to strike at great swathes
of the international financial system, endangering major banks
and insurers, and ultimately even disrupting governments’
ability to borrow.
Ramadorai believes he’s uncovered some of the hidden conduits
which transmit financial stress around the world. One of them
turns out to be emerging market investment funds based in
major financial centres. When there are unexpected losses in
these centres, frightened investors pull money out of all risky
assets, including emerging market funds. Funds must liquidate
assets to raise the cash to pay these withdrawals, so stock
exchanges thousands of miles away suffer a wave of selling,
even though the original trouble was completely unconnected.
Because these markets tend to be comparatively illiquid, with low
daily trading volumes, this selling has a disproportionate impact.
“You wouldn’t expect that India and China would suffer so badly when London and New York did, but that was what happened during the last crisis,” Ramadorai comments. “Paradoxically, this kind of fire-sale activity seems to hit bigger and more liquid emerging markets the hardest – precisely because of their liquidity, fund managers try to sell more assets there.”
The research even suggests ways to predict where the risk is
greatest, by examining where emerging market investors own
a lot of assets in common. For example, if UK-based funds that
are heavily invested in India also own lots of shares in Egypt,
then both will suffer when London does, while other countries
nearby may remain relatively untouched.
“It’s a real concern; the idea of spreading your investments between different markets is to reduce risk through diversification, but it turns out that by buying into these markets, these investors are actually creating a new source of correlation between them,” says Ramadorai.
The effects are predictable and tradeable. The research suggests
an investor who followed the strategy rigorously could have
made high risk-adjusted returns over recent years. The findings
may be even more relevant to regulators as Ramadorai believes
restricting these vectors of financial contagion could help them
control the spread of panic in a crisis.
His other long-term interest lies in hedge funds. These are
among the big financial success stories of recent decades;
they aim to beat the market with flexibility and sophisticated
trading techniques. Once thought of as the preserve of the
ultra-rich, in recent years hedge funds’ popularity has grown
rapidly to take in pension funds, asset managers, university
endowments and even private savers.
This widening appeal has brought vast amounts of new cash
into the industry, but it also creates risk. Wealthy hedge fund
investors can probably look after themselves, but newer
investors may be less sophisticated and need
more protection from regulators.
containing contagion
2
Hedge funds are no longer peripheral market players; they are
a major component of the international financial system. A
big hedge fund collapse could now have serious consequences
for the wider economy. But there’s still little information
available about what these funds are doing to get their
returns. What risks are they running, and how well do their
investors understand them? Ramadorai and his co-author have
developed a way to find out.
“Hedge funds are very frequent traders, but their reporting is almost entirely voluntary and even those that do provide information usually only do so quarterly,” Ramadorai explains. On top of that, they need to report only long positions; short bets go undisclosed. “We wanted to get a sense of what they do over much shorter periods, like a single day on which the markets crash,” he adds.
The solution is to use statistical analysis to analyse readily
available data on general market trends alongside the much
lower-frequency data on hedge fund returns, in order to
understand the relationships between them. “We end up
with the best possible explanation of how their returns vary
with general market movements, and with other factors like
liquidity, volatility and the availability of leverage,” Ramadorai
explains. Testing the method’s predictions on the few funds
that do provide daily updates on their activities seems to
confirm its accuracy.
What do the results tell us? One clear conclusion is that when
the market becomes volatile, hedge funds generally retreat
from risky assets and switch into safer ones such as short-
maturity bonds – just like most other investors.
This casts some doubt on their claim to be providing vital
liquidity in a crisis, buying when others are fleeing for safety. It
also suggests that those who invest in hedge funds to diversify
their portfolio’s risks could get a surprise if real trouble appears.
Another surprising discovery is that while analysis based
on monthly data might suggest hedge funds have low risk
exposures, higher-frequency analysis shows that at other times
they are exposed to much greater risk. Broadly, they seem to
run the most risk right after they have reported, and the lowest
just before doing so.
More generally, Ramadorai is sceptical of most hedge funds’ claims
to be able to beat the market consistently. Some can, but only a
minority, and only by taking clear risks, he argues. These funds are
quickly spotted by smart investors and deluged with money; early
success often proves hard to replicate at larger scales.
That success comes at a price, too; fees are steep, and have
continued to rise in the last decade. When this is taken into
account, returns don’t look as impressive. “Our research shows
that hedge fund families that have done well in the past do
seem to be able to charge higher fees,” Ramadorai notes. “But
is their performance really better? In general there seems to
be no real difference, after fees, between the best and worst-
performing funds.”
There’s an argument that hedge funds need closer supervision. If
they had to report what they’re doing more often and in more
detail, it would be easier for regulators to ensure they’re not
creating build-ups of risk that could threaten the whole system.
It’s a delicate balance; too much oversight could threaten the
agility that’s among hedge funds’ main strengths. Too little could
mean the next one to implode takes its bank counterparties
down with it, or imperils a major pension scheme.
Ramadorai argues the problem is being addressed, at least
partly. “There’s certainly a case for better reporting, but I think
this is beginning to happen already – hedge funds are gradually
being drawn into the regulatory net,” he says.
All this research is being put to good use. Earlier this year
Ramadorai joined the newly-formed European Securities and
Markets Authority (ESMA), becoming a member of the Group
of Economic Advisors of its Committee for Economic and
Markets Analysis.
Do regulators need to take further steps to stop the spread of crisis? Are there new indicators they should be monitoring for signs of trouble? It’s still early days for ESMA, but before too long the questions Ramadorai and his colleagues discuss at its biannual meetings could become big news for the whole industry.
3
TA R U n R A m A d o R A i
FEATURE
k E v i n S h E P PA R d
When journalists and commentators say
markets are ‘volatile’, they don’t mean it in a
good way. Technically, of course, when a price
rockets upwards it’s being just as volatile
as when it crashes, but you’d never know it
from reading the financial press. Could this
intuitive feeling that volatility is in itself a
bad thing point to a deeper insight? Kevin
Sheppard thinks so.
He’s a financial econometrician, interested in measuring how
financial markets behave at ever-greater levels of detail. That
involves painstaking attention to tick-by-tick financial data,
as well as plenty of computational firepower. His office at the
Oxford-Man Institute (OMI) hosts numerous humming servers
and hard drive arrays – a necessity if you want to tangle with
datasets describing tens of millions of trades a day.
“You hear a lot about volatility in the news, and it’s always a bad thing,” Sheppard explains. “People say the markets are volatile when they’ve fallen. They blame volatility when they lose, but they don’t give it any credit when they win. I did
some research on this, and I couldn’t find a single positive use
of the term in the financial press.
From a statistical point of view, it’s 50-50 – volatility could mean something is rising or falling, but I wondered if there’s more truth to the idea that volatility is associated more with negative returns – if volatility really could be bad for society as a whole.”
He set about finding out by analysing high-frequency data to
extract a different metric, called ‘realised semivariance’, which
is like volatility but also includes information on the direction
prices are moving. Sheppard and fellow OMI researcher Andrew
Patton calculated it in a variety of different markets using prices
sampled every five minutes.
They were particularly interested in sudden, discontinuous
movements, known as jumps, in an asset’s price, which
generally happen when unexpected new information becomes
available – for example, when a central bank changes interest
rates. By definition, such a price jump forms a spike in volatility.
The researchers looked at any five-minute periods where a price
rose or fell significantly relative to the five-minutes immediately
before or afterwards.
This allowed them to decompose volatility into two components
– the everyday volatility that forms a constant backdrop to
all market activity, and the ‘jump volatility’ that’s created by
unexpected news. The use of realised semivariance provided
further guidance by separating out good jumps, where the price
heads upwards because, say, a share buyback is announced, and
bad ones, where long-term investors become worse off.
“We were trying to understand how the market processes these events, when there’s a major positive or negative surprise, and what the implications are for future volatility,” Sheppard explains.
The results were eye-opening. “We found there’s a huge
asymmetry between how good and bad surprises are received.
The impact of good news is transitory, usually lasting a few
days at most, whereas the effects of bad news lasts up to three
months. So when people consistently associate volatility with
bad consequences, they’re right!”
In fact, after a few initial flickers, good news actually seems to
reduce volatility. Investors are happy; not only have they made
money from the good news, but their portfolio’s returns are
smoother for the next few days. Bad news, by contrast,
tends to leave sustained turbulence in its wake. This
kind of asymmetry only appears when looking at
sudden jumps in the market; smaller, steadier price
movements have similar effects whether they are
up or down.
good volatility, bad volatility
4
Until recently, such phenomena were barely visible, because
most people focused on daily price data. It’s only by zooming
in to watch how the market reacts to information over much
shorter periods that researchers can start to understand its
behaviour. “Working with high-frequency data is like having a
microscope,” Sheppard says. “It lets us see what’s going on in
the market in a level of detail that was once impossible.”
He has built on this work by creating sophisticated models that
use this ‘realized’ data to forecast volatility – more accurately
than previous models, over many time horizons, and in asset
classes ranging from US equities to currencies and emerging-
market stocks. The financial industry is slowly adopting such
models, which should help traders and investors manage their
risks more effectively. As assets’ liquidity improves, the models’
range of possible applications will grow still further.
High-frequency econometrics has been growing steadily in
popularity since the mid-1990s. In part this has been made
possible by the widespread availability of rich and detailed
financial datasets. Vastly increased trading volumes on
exchanges all over the world also help. More markets now have
enough liquidity for effects like these to be measurable – it’s
hard to get a detailed idea of a stock’s volatility if it trades just
a few dozen times a day.
Sheppard cites a day during the recent financial crisis when the
S&P 500 index rose by about 10%, then crashed 15% before
rallying in the last hour to close almost unchanged. Examining
only daily price data misses the dramatic swings in the price,
and a risk manager could come away with the impression it was
an uneventful day’s trading.
Likewise, as we emerged from the most turbulent period of the
recent crisis, volatility fell significantly. But someone using only
daily data wouldn’t have noticed until much later. Due to the
noise that using this data introduces into analysts’ calculations,
to get a statistically meaningful result it’s necessary to look back
over a much longer period. This in turn means it takes much
longer to spot emerging trends.
Using more frequent data means analysts can look at two or three days’ worth of prices, rather than a couple of months. In early September 2008, their view wouldn’t have been clouded by the lingering presence of August’s (relatively low) volatility; they would have seen what was going on in the present far more clearly and been able to act promptly as volatility changed.
These findings could be important to risk managers at banks,
hedge funds and other financial institutions. For example, they
could give traders in the options market, who are essentially
betting on changes in volatility, a more up-to-date idea of the
dangers they face.
More specifically, they could have helped financial institutions
assess their risks more accurately in the run-up to, and the wake
of, the crisis. It’s possible some could have weathered it better
as a consequence. One investment bank claimed afterwards
that for two days in a row during the crisis, market movements
were so extreme that they qualified as ‘six-sigma events’. By
definition, these events, when prices move more than six times
as violently as usual, practically never happen. To have two in a
row is almost inconceivable.
The bank was trying to claim it couldn’t possibly have foreseen the events that lost it so much money – they were one-in-a-trillion freak events. But is this true, or was it just looking at the wrong things?
Sheppard suspects the latter. “If you look at how volatility
was changing, those events don’t look all that surprising at
all,” he says. The bank only believed they were so incredibly
unlikely because it didn’t understand how volatility was rising,
which was due to the fact that the data its risk managers were
using wasn’t frequent enough and hid new developments in a
dangerous way.
“In normal times, a 10% drop in the market is very rare,” Sheppard comments. “But in the middle of a crisis it’s not rare at all.” If his work helps drive home that message, he’ll have done us all a favour.
5
k E v i n S h E P PA R d
FEATURE
b E n h A m b ly
Commodities are among the most fertile
fields for innovation in finance today, and Ben
Hambly is helping to create the mathematical
framework that makes it all possible.
Hambly, a Professor of Mathematics and associate faculty
member at the Oxford-Man Institute (OMI), is a specialist in
the mathematics of probability. Throughout his career he has
sought out new and exciting financial markets in need of
firmer theoretical underpinnings. The burgeoning trade in
commodities and the exotic derivatives that are springing up as
a result are the latest area to attract his interest.
Once thought of as stolid and uninteresting compared to more
fashionable assets like equities and credit, commodities have
seen an upsurge in appeal in recent years. New participants
have poured into markets such as metals, oil and electricity, and
interest is increasing at a pace.
“Things are developing incredibly fast,” Hambly says. “Already there are lots of fascinating problems to study, and areas where we don’t yet have a good mathematical model of what’s going on.”
One reason for the peak in interest is the enormous volatility
that commodity markets offer and the opportunities they create
for agile traders. A five percent price move over the course of
a day is considered violent and exceptional in stocks, bonds or
foreign exchange, but it’s nothing in commodities. Wholesale
electricity prices can spike tenfold over just a few hours.
This is partly because what’s being traded isn’t just a financial
instrument, but something that’s needed in the real world. If
someone doesn’t get the shares they want to buy immediately,
it’s no catastrophe. But if the wholesale power market fails to
connect buyers with sellers, homes go dark and factories shut
down. Likewise, if the price of government bonds spike, people
might simply buy less, but if a natural disaster causes an oil
refinery to shut down, prices will rocket.
Electricity is especially volatile because it can’t readily be stored.
At any given time, the market has to find a price at which all
the power being produced can be used.
Banks are desperately trying to create bespoke products that let
traders profit from violent price swings, or power producers and
users manage the risks they bring. As a result financial engineering
in commodity markets is growing ever more complex.
Hambly is working on the mathematics behind these exotic
financial structures. For example, he and colleagues have
created a robust mathematical framework for pricing ‘swing
options’, which give power consumers some rights to buy
electricity at a fixed price - allowing them to hedge the risk of
sudden brief increases in their running costs, but also letting
them benefit if prices fall sharply.
Other innovations to which his methods apply include ‘tolling
agreements’ – exotic derivatives that effectively let market
participants set up as virtual power stations, turning production
on and off as conditions change and looking to profit from the
difference between fuel inputs and power output.
It’s not just the financial industry that’s driving the market’s growth; the industries that produce and consume commodities are moving in too. Energy generators are realising they need mathematical finance techniques if they’re to accurately value the power stations they own, or efficiently finance the construction of new ones.
Likewise, mining and energy extraction firms are realising
that the methods they use to value the reserves that they
plan to exploit in the future are crude, and that using more
sophisticated techniques could transform how they do business.
Hambly is giving them the tools to rethink the energy market.
the mathematics of power
6
One unsettling feature of working on the cutting edge is
that today’s hot new market can turn into tomorrow’s pariah.
Hambly’s previous research includes considerable work on
how to price complex structured credit products, whose value
depends on a large portfolio of underlying assets.
Structures like collateralised debt obligations (CDOs) and
mortgage-backed securities enjoyed explosive growth for a few
years, but plenty of investors lost out when the market crashed
in 2008 and now few new deals are done.
Hambly built a dynamic model that greatly improved on the methods being used to value these deals at the time. It used stochastic partial differential equations to understand the losses that could be expected when buying them. Unfortunately, this radical improvement arrived just when the bottom fell out of the market.
He is resigned, though. “Obviously this wasn’t quite what I
was expecting, but it’s a risk you take when you work in these
areas. In retrospect, the market became far too dependent
on simplistic models, and too divorced from the assets behind
these structures. But fundamentally default risk is still there and
in theory CDOs were a sensible way of sharing it out.”
OMI provides a supportive environment for those working at the
frontiers of knowledge. It brings together people from different
backgrounds, including academics from a variety of fields as
well as finance professionals. A recent OMI seminar on energy
markets, for example, provided Hambly with the opportunity
to exchange insights with fellow academics and senior quants,
as well as traders from investment banks and hedge funds. For
Hambly it was a chance to compare models and pricing methods,
and discuss the trends that are driving the market’s development
feeds into new and interesting financial phenomena.
A major issue of concern to practitioners is the increase in the level of unhedgeable risk in the markets. “Everyone wants to manage risk, but lots of these risks are impossible to hedge,” he comments. “If you’re in the oil market, how do you hedge a political risk like the Arab spring? How could traders in natural gas have protected themselves from the technological advances in shale gas production that have sent prices diving? A lot of this is hard to deal with mathematically,” he admits. “But it’s still vital to know about it if you’re interested in these markets.”
He still works on a broad repertoire of research interests
outside finance. One long-running project draws on the
theory of fractals – mathematical constructs like the famous
Mandelbrot set, which display complex structures at all scales so
that that no matter how far you zoom in, intricate new forms
always become apparent. It turns out these are a great way to
understand how particles move through different kinds of soil.
That said, mathematical finance takes up a great deal of
Hambly’s time. His success in the field might seem a little
surprising, given that he fell into it almost by accident. In the
mid-1990s, when it was an emerging and little-studied field,
Hambly was a young lecturer at the University of Edinburgh.
Senior managers decided to position the university to take
advantage of the fast-growing field and launched a new MSc
course, which Hambly was invited to run.
Back then it was unusual for a PhD student to decide to go into
the financial industry; these days it’s become the default career
choice for mathematicians with a doctorate who don’t want to
remain in academia.
“I had to learn a lot, very quickly!” Hambly recalls. “But it wasn’t long before I realised that many of the problems in mathematical finance were extremely interesting. It’s the combination of challenging mathematics and potential financial impact that makes this such an exciting area to be in.”
7
b E n h A m b ly
FEATURE
mike gilesis Professor of Scientific Computing at the Mathematical Institute where he is a member of the Mathematical and Computational Finance Group. He read mathematics at Cambridge before completing a PhD in Aeronautical Engineering at Massachusetts Institute of Technology (MIT).
He was an Associate Professor at MIT before moving to Oxford
in 1992 to join the Computing Laboratory. After working closely
with Rolls-Royce for many years developing computational fluid
dynamics techniques, he moved into the development of Monte
Carlo and finite difference methods in computational finance,
which led to his transfer to the Mathematical Institute in 2008.
In 2007 he was named ‘Quant of the Year’ by Risk magazine,
together with Paul Glasserman of Columbia Business School,
for their joint work on the use of adjoints for the efficient
calculation of Monte Carlo sensitivities.
More recently, he has developed the multilevel Monte Carlo
method for the pricing of financial options, and is active in
the exploitation of GPUs (graphical processing units) for high
performance computing in a variety of application areas.
georg gottlobis a Professor of Computing Science. His research interests are database theory, web information processing and theoretical computer science. At the Oxford-Man Institute, he researches data exchange, semantic database and web querying, and automatic web data extraction for betting and quantitative finance.
He was a Professor at the University of Technology, Vienna
from 1988-2005, where he still holds an adjunct appointment.
Before that, he was affiliated with the Italian National Research
Council in Genoa, Italy, and with the Politecnico di Milano, Italy.
He has received the Wittgenstein Award from the Austrian
National Science Fund, is a Fellow of the Royal Society and
the Association of Computing Machinery, a member of the
Austrian Academy of Sciences, the German National Academy
of Sciences, and the European Academy of Sciences Academia
Europaea in London.
greg gyurkojoined OMI in 2007 as one of the first student members of the Institute. He obtained a DPhil at the University of Oxford and is currently a Departmental Lecturer in the Mathematical Institute, where he is a member of the Mathematical and Computational Finance Group.
Greg is the course director of the MSc in Mathematical and
Computational Finance, and is actively involved in organising the
Practitioner Lecture series and the Mathematical Finance Internal
Seminar series. Greg’s research interests relate to the theory and
applications of Rough Paths Theory, as well as the development
and software implementation of probabilistic numerical methods
for approximating solutions to stochastic differential equations
and certain types of partial differential equations.
ben hamblyis a University Lecturer in Mathematics at the Mathematical Institute where he is a member of the Mathematical and Computational Finance Group and the Stochastic Analysis Group. He has a PhD from the University of Cambridge and previously had lectureships in Edinburgh and Bristol. He is Co-editor in Chief of Applied Mathematical Finance.
His research interests in mathematical finance are in the modelling
and pricing of financial derivatives. In particular he has worked on
electricity spot price models and the pricing of complex derivative
contracts in energy markets. He is also interested in credit markets
and the pricing of large portfolio credit baskets contracts. His
other research interests include random walks and diffusion
in random and fractal environments, rough paths, branching
processes, random matrices and particle systems.
mike
greg
geo
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m E m b E R S
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ben
vicky hendersonis a Senior Research Fellow at OMI and is affiliated with the Mathematical Institute. Previously a Reader in the Finance Group at Warwick Business School, Vicky held positions at Princeton University, ETH Zurich, and spent six months at the Isaac Newton Institute, University of Cambridge.
Vicky’s research area is mathematical finance with an emphasis
on derivative pricing in incomplete markets, particularly via
the utility indifference approach. She has worked on optimal
stopping problems relating to American option exercise with
partial hedging which have been applied to problems in real
and executive stock options. Recently, Vicky has studied optimal
stopping problems under prospect theory, the results of which
help explain disposition effects in financial markets. Vicky has
been involved in major conference organisation for the Isaac
Newton program and the 2010 Quantitative Finance program
at the Fields Institute, Toronto.
chris holmesmoved to Oxford in 2004 as a Lecturer within the Department of Statistics. He holds a ‘Programme Leaders’ award in Statistical Genomics from the Medical Research Council. He was awarded the title Professor in 2007 and the Royal Statistical Society’s Guy Medal in Bronze in 2009.
Chris’ research is focussed on Bayesian methods and computation
for high-dimensional inference problems, in particular,
analysis techniques for sequential data structures arising in
bioinformatics, statistical genetics and genetic epidemiology.
Within the Oxford-Man Institute he has ongoing projects with
Mike Giles on graphical processing unit (GPU) implementation
of Monte Carlo methods for dynamic inference problems, and
Stephen Roberts on Bayesian Nonlinear Models. Chris studied
for his PhD in Bayesian Nonlinear Methods within the Statistics
Group in the Department of Mathematics, Imperial College
London. Following this he undertook a postdoc and then
lectureship within the department. In 2002 he was awarded the
Royal Statistical Society’s biennial ‘Research Prize’ for his
work in Bayesian statistics.
sam howisonis an applied mathematician working in the Mathematical Institute. He uses applications of differential equations and appropriate approximation procedures. His interests include many aspects of mathematical finance, such as derivatives pricing and models of unusual markets.
hanqing jincompleted his PhD in Financial Engineering in 2004 at the Chinese University of Hong Kong. He is a University Lecturer at the Mathematical Institute, is on the editorial board of Mathematical Methods of Operations Research and is also a member of the Mathematical and Computational Finance Group.
His research interests include portfolio selection, behavioural
finance, applied stochastic analysis and optimisation. He has
previously worked on stochastic control, portfolio selection
with transaction costs and behavioural portfolio selection. He is
currently working on time consistency of dynamic decisions.
m E m b E R S
vicky chri
s
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han
qin
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PEOPLE
sam
shin kanayais a Postdoctoral Research Fellow at the Department of Economics. He earned a Bachelor’s and Master’s degree from the University of Tokyo, majoring in economics, and a PhD in Economics from the University of Wisconsin-Madison in 2008.
His primary field is financial and time-series econometrics, with
an emphasis on nonparametric testing and estimation problems
of continuous-time economic and financial models. He is
currently working on the following projects: nonparametric
testing of the stationarity for continuous-time Markov
processes, and nonparametric estimation for mixed frequency
time series data.
gechun liangjoined the Oxford-Man Institute as a Postdoctoral Research Fellow in the Michaelmas Term of 2010. Prior to that, he was a student member of the Institute whilst completing a DPhil in Mathematics at the Mathematical Institute under the supervision of Professor Terry Lyons. He has a Master’s Degree in Mathematics from Tongji University, and studied finance as an undergraduate in Jilin University.
His research interests are mainly focused on mathematical
finance and applied probability. He is especially interested
in backward stochastic differential equations and credit risk
modelling.
terry lyonsis the newly appointed Research Director of the Oxford-Man Institute. He is the Wallis Professor of Mathematics at the University of Oxford, a Fellow of the Royal Society and one of the UK’s leading mathematicians, having made a number of contributions to stochastic analysis.
His interest in stochastic analysis relates particularly to the
control of non-linear systems driven by rough paths. Prime
examples of such systems are provided by stochastic differential
equations and stochastic systems.
His research on ‘rough paths’ has founded a new field,
stimulating an enormous amount of work, allowing
breakthroughs in many areas such as numerical analysis. He has
a deep understanding of the role of risk in financial markets
where he is known for his work on managing uncertainty in
volatility, and for developing cubature methods as new tools
allowing more efficient numerical modelling.
josé martinezis a Lecturer in Finance at the Saïd Business School. He obtained his PhD from Columbia Business School. Before joining the University of Oxford he was a Visiting Researcher at the Institute for Financial Research in Stockholm, Sweden. José specialises in capital markets, investments and investor behaviour.
His research explores the role of information sellers in financial
markets and the use investors make of their financial advice.
He is also interested in the differences exhibited by pension
and mutual fund investors and is currently working on
understanding how capable individuals are of managing their
retirement accounts.
gechun
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sergey nadtochiyis a Senior Postdoctoral Research Fellow at OMI. His research interests lie in the field of financial mathematics, specifically the applications of stochastic and functional analysis for the pricing and hedging of financial derivatives.
His current research is concerned with the construction of so-
called ‘market models’ – the financial models that are designed
to be permanently consistent with the prices of the liquidly
traded derivatives. In addition, he has done work on static
hedging; obtaining exact semi-static replication strategies for
barrier options with European-type securities in a large class of
models. Sergey’s new subject of interest is portfolio choice, he is
working on explicit description of optimal investment strategies
in the presence of untradeable risks, and/or ambiguity about
the investor’s preferences.
jan obłójis a University Lecturer at the Mathematical Institute where he is a member of the Mathematical and Computational Finance Group. Before coming to Oxford he was a Marie Curie Postdoctoral Fellow at Imperial College London.
He holds a PhD in Mathematics from the University Paris IV
and Warsaw University. His general interest is in mathematical
finance and its interplay with probability theory, and he looks
at a number of different problems where tools from martingale
theory and stochastic analysis can be applied.
Recent areas of focus include: robust pricing and hedging of
exotic derivatives via the Skorokhod embedding problem,
comparative performance of robust and classical hedging
methods, portfolio optimisation under pathwise constraints and
hedge-funds managers’ incentive schemes, inverse problems for
utility maximisation.
han ozsoylevis a Lecturer in Financial Economics at the Saïd Business School. Before joining the University of Oxford, he earned his PhD in economics from the University of Minnesota and BSc in Mathematics from Bilkent University. He has held visiting appointments at the University of California - Berkeley and Johns Hopkins University.
Han’s research primarily focuses on financial market
imperfections, such as those generated by asymmetric
information, imperfect competition, behavioural biases, and
bounded memory. He has studied information sharing amongst
stock market investors and, in particular, how social and
information networks affect asset prices and investor welfare.
He is also interested in questions related to financial fragility,
liquidity and market manipulation.
tarun ramadoraiis a Reader in Finance at the Saïd Business School. Tarun has a BA in Mathematics and Economics from Williams College, an MPhil in Economics from Emmanuel College, Cambridge, and a PhD in Business Economics from Harvard University.
He is also a Research Affiliate of the Centre for Economic Policy
Research, London. He has published papers in journals such
as the Journal of Finance, The Journal of Financial Economics
and The Review of Financial Studies. His main areas of interest
are capital markets, international finance and hedge funds.
His current research deals with two main topics: the impact
of international investment flows on equities and foreign
currencies in a range of countries; and the performance,
riskiness and capital formation processes of hedge funds. He has
taught courses on international finance, behavioural finance,
hedge funds and investment management for the Master of
Financial Economics, MBA, Executive MBA, and PhD programs
at the University of Oxford.
jan
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sergey
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steve robertsis Professor of Information Engineering at the University of Oxford. He studied physics, completed a PhD in Signal Processing and was appointed to the faculty at Imperial College London, before taking up his post in Oxford in 1999. He heads the Pattern Analysis and Machine Learning Research Group.
His main area of research lies in machine learning approaches
to data analysis. He has particular interests in the development
of machine learning theory for problems in time series analysis
and decision theory. Current research applies Bayesian statistics,
graphical models and information theory to diverse problem
domains including mathematical biology, finance and sensor
fusion. He has been awarded two medals by the IEE for papers
on Bayesian signal analysis. His current research focuses
on statistical models for sequential change-point analysis,
forecasting and decision making and decentralised multi-agent
co-ordination.
neil shephardis Head of Financial Econometrics and Statistics at the Oxford-Man Institute and a Professor of Economics at the University of Oxford. He is a Council Member of the Society of Financial Econometrics and an Associate Editor of Econometrica.
Neil is a member of the advisory boards of Research Centres
at Aarhus University and Singapore Management University.
His research interests are mainly focused on econometrics
– particularly working with high frequency data to try and
understand financial volatility and time varying dependence,
market microstructure and the role of jumps in financial
markets. He is also interested in the use of simulation to
carry out econometric inference. He was an undergraduate
at York studying economics and statistics. He has carried out
graduate work and taught at LSE. He was elected a Fellow of
the Econometric Society in 2004 and a Fellow of the British
Academy in 2006.
kevin sheppardis a University Lecturer in the Department of Economics. His research interests focus on financial econometrics. He has carried out work on estimating large dimensional time-varying covariance matrices and has recently focused on the use of high frequency data to more precisely estimate dependence amongst asset returns.
Kevin was an undergraduate at the University of Texas at Austin
and completed his PhD at the University of California, San Diego.
ruediger stuckeis a Research Fellow in Finance and Economics at the Saïd Business School. Ruediger came to the Saïd Business School in 2007 to finish his PhD, which he had previously started at Paderborn University, Germany. Prior to this, he studied business administration and computer science at Paderborn University.
His research interests cover the whole field of private equity, with
focus on the buyout industry. Affiliated areas of interest include
leveraged and structured finance, corporate valuation and
mergers and acquisitions.
mungo wilsonis a Lecturer in Financial Economics at the Saïd Business School. His research interests include determinants of expected returns, credit risk, mutual funds and portfolio allocation.
mu
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thaleia zariphopoulouis the first holder of the Man Professorship of Quantitative Finance and is a member of the Mathematical Institute. Her area of expertise is in financial mathematics, quantitative finance and stochastic optimisation. Her research interests are in portfolio management, investment performance measurement and valuation in incomplete markets.
lan zhangis a Reader in Finance. Her research focuses on market microstructure, statistical arbitrage and high frequency financial econometrics. She has developed a number of influential methods for analysing high frequency financial data, including the two-scale and multi-scale realised volatility estimators (TSRV, MSRV) to handle market microstructure.
Recently Lan analysed the general theoretical properties of
local constancy approximation in continuous semimartingales.
Her current work includes the analysis of limit order books
observed in real time, robust estimation of high frequency
quantities and its application to portfolio management and
options trading.
Lan has published widely in leading journals including
Econometrica, Review of Financial Studies, Journal of
Econometrics, Journal of American Statistical Association,
Bernoulli, and Annals of Statistics. She is an Associate Editor
of the following academic journals: Statistics and Its Interface,
Annals of Applied Statistics, and Econometric Theory. She is
on the advisory board of the International Center for Futures
and Derivatives at the University of Illinois at Chicago. She
completed her undergraduate degree at Peking University in
China and obtained her Master’s and PhD degree from the
University of Chicago.
xunyu zhouis the Nomura Chair of Mathematical Finance and Director of the Nomura Centre for Mathematical Finance at the University of Oxford. He obtained his PhD at Fudan University in 1989. He currently focuses on the mathematics of behavioural finance.
Prior to joining the University of Oxford he was Chair of
Systems Engineering and Engineering Management at the
Chinese University of Hong Kong. His general research interests
are in quantitative finance, stochastic control and applied
probability, while he has recently engaged in mathematical
behavioural finance research. He is a Fellow of IEEE and a
winner of the SIAM Outstanding Paper Prize. He is on the
editorial boards of Mathematical Finance, Operations Research,
SIAM Journal on Financial Mathematics and SIAM Journal on
Control and Optimization.
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thaleia
xun
yu
lan
bahman angoshtariis a second year DPhil student in the Mathematical Institute, University of Oxford. He holds an MSc in Applied Mathematics from the University of Twente and a BSc in Industrial Engineering from Sharif University of Technology, Iran.
His research interests lie in the application of stochastic analysis
and control theories in finance, especially in portfolio choice.
He is currently focused on identifying the optimal investment
strategy in a market with co-integrated assets. The results are
directly applicable to pairs-trading, and possible extensions to
statistical arbitrage are under investigation.
youness boutaib is a DPhil student in the Stochastic Analysis Group. Working with Professor Terry Lyons has drawn his attention to the power of the theory of rough paths.
The theory, along with giving the appropriate frame of solving
equations driven by very irregular signals (like the fractional
Brownian motion), encompasses the previous theories of
integration (Stieltjes, Young and Stratonovitch). He aims to
develop a control theory based on it that would help solve
optimisation problems of systems that are ruled by differential
equations driven by rough paths. Applications naturally include
finance and quantum physics and other older classic problems.
sylvestre burgos is studying for a DPhil in Mathematics within the Mathematical and Computational Finance Group. He holds a BSc in Mathematics from the University Paris VI, an MSc in Engineering from the Ecole Centrale Paris and an MSc in Mathematical and Computational Finance from the University of Oxford.
Sylvestre’s research interests lie broadly in the field of numerical
methods for computational finance. His research under the
supervision of Mike Giles focuses on the computation of Greeks
with Multilevel Monte Carlo simulations.
vladimir cherny is a second year DPhil student at the Mathematical Institute. His research interests lie broadly in stochastic analysis and optimisation theory with their applications to mathematical finance.
He is working under the supervision of Jan Obłój on
implementing methodology of Azema-Yor processes for
different optimisation problems in mathematical finance, such
as long-term expected utility growth rate maximisation subject
to drawdown constraint.
martin gould is a second year DPhil student in Mathematics. He holds an MASt (Part III) in Mathematics from the University of Cambridge and a BSc in Mathematics from the University of Warwick.
His primary research interest is the limit order book, and in
particular in developing a dynamic stochastic model of limit
order trading that is better able to explain the diffusive nature
of the return series in foreign exchange markets. He hopes to
be able to extend his model to gain insight into how prices
are affected by the release of macroeconomic news by central
governments and to examine how changes in limit order arrival
flows propagate through the network of different currency pairs.
ni hao is a second year DPhil student in the Stochastic Analysis Group. Ni previously completed a Bachelor’s Degree in Mathematics at Southeast University, China and a Master’s Degree in mathematical and computational finance at the University of Oxford.
She is currently working on rough paths theory with her
supervisor Professor Terry Lyons, and her research interest is the
expected signature of stochastic processes.
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richard hillsis a DPhil student in Financial Economics from the Saïd Business School, and is interested in the effect of the factors determining liquidity in financial markets. His current research is on liquidity as measured by price impact in market microstructure models (as opposed to transaction costs or bid-ask spreads), and investigating models where this price impact is random, and hence there is liquidity risk.
He has previously worked for various technology companies,
and spent two years in Credit Derivatives Technology at Morgan
Stanley. He has a MEng in Engineering from the University of
Oxford, and an MPhil in Finance from Cambridge.
arend janssenis a DPhil student in Mathematics at the University of Oxford. He holds a degree (Diplom in Mathematics) from the University of Freiburg, Germany. Arend’s research interests lie broadly in Mathematical Finance and Stochastic Analysis, where he is particularly interested in order book models.
He is also interested in the theory of rough paths and their
applications to finance. Recently, Arend has been working on
numerical solutions of stochastic differential equations driven
by rough paths.
sigrid källbladis a second year DPhil student in the Mathematical and Computational Finance Group. Sigrid works under the supervision of Professor Thaleia Zariphopoulou and her research interests are in stochastic control and portfolio optimisation.
nathaniel kordais studying for a DPhil in Mathematics at the University of Oxford under Pierre Tarrès. In 2007 he completed his Undergraduate Master’s Degree in Mathematics at the University of Oxford.
Nathan’s research is focused on the n-Armed Bandit. An n-Armed
Bandit is a simple probabilistic model of a game in which one
repeatedly chooses to play one of n arms, each of which will yield
some reward with a certain fixed, but unknown, probability.
His current interests lie in the asymptotic properties of various
strategies proposed in the literature for this game.
ada lauis studying for a DPhil in Mathematics. Her research interests include time series forecasting, spatiotemporal correlation modelling and latent Gaussian processes.
Ada obtained a BSc in Mathematics and Physics at the
University of Hong Kong and an MPhil in Physics at the
Chinese University of Hong Kong. She has submitted her thesis
on “Probabilistic Wind Power Forecasts: From Aggregated
Approach to Spatiotemporal Models”.
anthony leeis a DPhil student in the Department of Statistics. He completed Bachelor’s and Master’s Degrees at the University of British Columbia, specialising in Computer Science.
Anthony’s research interests lie broadly in computational
statistics and Bayesian inference, with emphasis on the design
and application of simulation-based numerical integration
techniques in complex, data-rich domains including those found
in quantitative finance. More specifically, he is interested in
enhancing and expanding the use of advanced Monte Carlo
methods, such as Markov chain Monte Carlo and sequential
Monte Carlo, in statistical inference.
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ni
anth
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nath
aniel
sigrid
richard
arnaud lionnetis a DPhil student at the University of Oxford. His interests in mathematics include functional analysis and probability theory and he is very interested in complex and dynamical systems, especially when they involve randomness (markets, population evolution, meteorology, etc).
More specifically he is interested in stochastic differential
equations, Malliavin’s calculus and rough paths. He specialises
in backward stochastic differential equations, which he
finds interesting for two of their fields of application: their
connections with some kinds of partial differential equations on
the one hand and some problems of mathematical finance on
the other (option pricing, risk measures).
kasper lund-jensen is a DPhil student in Economics at Nuffield College. Prior to his doctoral studies he completed a BSc in Economics at the University of Copenhagen and a MSc in Finance and Economics at the London School of Economics.
Kasper’s research interests lie in the areas of financial
econometrics and economic forecasting. Currently, his research
is focused on out-of-sample equity premium predictability and
combination forecasts.
diaa noureldin is a DPhil student in Economics. He is interested in financial econometrics, particularly modelling and forecasting volatility and dependence in financial time series.
He is interested in developing methods suitable for large
dimensional systems and high-frequency data. Diaa previously
studied for an MPhil in Economics at the University of Oxford,
and holds a BA and MA in Economics from the American
University in Cairo. In Michaelmas 2011, he will join the
Department of Economics at the University of Oxford as a
Postdoctoral Research Fellow.
cavit pakelis interested in the field of financial econometrics and, specifically, in volatility modelling. He is also interested in the nuisance parameter issue and bias reduction in the likelihood framework.
His current research focuses on elimination of bias in GARCH
panels, a model that enables univariate volatility modelling
using a panel of asset returns, as opposed to considering a
single time-series only. As such, this structure makes it possible
to model volatility using a smaller than usual number of
observations in the time-series dimension.
daniel schwarzDaniel Schwarz is a DPhil Student at the Mathematical Institute and a member of the Mathematical and Computational Finance Group. Previously he obtained a Master of Mathematics (MMath) degree from the University of Oxford.
His current research is focused on the stochastic modelling of
energy markets. In particular he has been developing models
for spot and derivative prices in carbon emission and electricity
markets and worked on the pricing of spark and dark spread
options, which are routinely used to value power plants. In
addition, Daniel is interested in the asymptotic analysis of these
models, which provides intuition for the underlying dynamics
and leads to approximations that are useful for the calibration
to market data.
michael streatfieldis interested in hedge funds and investment management. He is a third year doctorate student supervised by Tarun Ramadorai. In his research work he has been analysing the determinants of hedge fund management and incentive fees and in particular exploring how hedge fund management companies set prices for the future funds they launch.
His future research involves analysing the impact of the recent
crisis on hedge fund reporting. Prior to his DPhil, Michael
worked in the investment industry for
15 years in London and South Africa.
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How did your involvement in the Institute first come about?
I was already part of an existing interdisciplinary network in financial research at the University
of Oxford when Man Group approached us in 2006 about the opportunity to create the Institute.
I lead the team responsible for putting together the University’s pitch. Our vision for the Institute
was very closely aligned to Man’s aspirations and when the agreement was formalised, the
University invited me to be the Institute’s first Director, which I was very happy to accept.
What was your vision for the Institute when you first opened its doors in September 2007?
Our aims for the Institute were very clear when it was first founded and we continue to strive
to achieve them. We’re here to be excellent at academic work in quantitative aspects of
finance. We want to generate new ideas that are applicable to financial problems, to write
good papers and be respected by our peers around the world for what we are doing. We’re
also dedicated to training the next generation of researchers and attracting the highest calibre
of young people to come and work here.
Did you have any personal objectives when you took on the role of Director of
the Institute?
I wanted it to be more than just a space where people can focus and develop their academic
ideas. I wanted it to increase their aspirations. People can often be very self-limiting. They’ll
have ideas, but feel constrained about what they can actually achieve. I wanted the Institute
to encourage people to raise their game and to overcome barriers – to seek out opportunities.
My aim was to create an exciting place that people would want to come to.
Do you feel you’ve achieved that aim?
Absolutely - I think the strongest indicator of the Institute’s success is the calibre
of people we’re attracting to the UK from around the world. We have an
extraordinary group of post doc researchers here. They’re second to none
in the world and I’m so proud that they’ve chosen to spend the most
crucial time in their career here. Our newest recruit who joins us in
September 2011 had something like 14 offers from some of the best
universities around the globe and he’s chosen to come here. It’s a
very real indicator to the world that we’re a centre of excellence!
In July this year Neil Shephard stepped down as
Director of the Oxford-Man Institute (OMI). Neil has
played an integral role in the formation and continued
development of OMI. We spoke to him to gain a closer
insight into his aspirations for the Institute and the
reasons behind his decision to step down as Director.
OMI bids ‘au revo ir ’ to its founding Director
2
Part of your initial vision was to generate new ideas in the area of quantitative
finance – do you feel you’ve achieved that objective over the last four years?
Some very good ideas have been generated at the Institute. Two of note include Mike
Giles’ work on harnessing graphics cards to complete calculations quickly using simulated
complicated derivatives, and the software my colleague Tarun Ramadorai’s been developing
to provide a unified view of hedge fund return databases. They’re extremely exciting and
receiving a lot of recognition.
What has the Institute enabled you to achieve?
OMI has enabled me to take my work to the next stage of empirical relevance. I could have
completed my research in a more abstract, theoretical way, but it has enabled me to take it to
a more applied situation. To some degree it’s because I’ve had the opportunity to talk to Man’s
commercial team, but it’s also down to the fact that we have better data resources here, a very
good computational infrastructure with fantastic compute servers and specialist graphics card
machines, better funding and an extremely efficient administration team that I’m very proud
of. They do a great job, which leaves us to focus on our research!
What has prompted your decision to step down as Director?
My family and I are relocating to London and I feel very strongly that the Institute’s Director
should be here full-time, which will become impossible for me based in London. I also feel that
it’s time for OMI to move on and for some fresh ideas to be injected into it. I hope to be able
to contribute to the Institute’s successes in the future, but it’s time for someone else to step up
and to take it to the next level.
What would you like to see the Institute achieve in the future?
I’d like to see it go from strength to strength: to continue on its path to becoming recognised
as the world’s centre of excellence in our academic field, which I think we’re well on the way to
achieving. I’d like to see more effective use of our interdisciplinary strengths and more research
projects across disciplines, which I know my successor is keen to pursue. I’m extremely proud of
what has been achieved, but there’s a lot more that can be done and I’m happy to be stepping
down to let someone else take up the mantle.
What was the reasoning behind the collaboration
with the University of Oxford?
[Tim Wong] In 2006, we decided that we wanted to create a
collaboration with a leading research institution in the UK
or Europe to help us develop the business. We had a vision
of bringing together the commercial and academic worlds of
quantitative finance as, in our experience, these two worlds
never seemed to meet in a meaningful way. After discussions
with several top academic institutions it was clear that Oxford
was the natural choice. They had an appetite to do it, they
saw opportunity, and they had a unique expertise in the
departments and disciplines we were interested in.
Why is this collaboration so innovative?
[TW] The closeness of the academic and commercial
relationship is unique – it’s something that a lot of people
have talked about creating, but few have realised.
[Anthony Ledford] You have leading academics and leading
commercial practitioners working together on a daily
basis. It is also a multi-disciplinary environment attracting
people from different subject areas including econometrics,
mathematics, statistics, computer science and engineering.
By drawing on expertise from related fields, you bring new,
creative ideas to both practical and theoretical problems.
What have been the key commercial benefits to Man?
[AL] Man has been able to develop part of its tail protect
strategy using models that were first developed and openly
published by academics in the Oxford-Man Institute (OMI).
The initial research was focused on building a forecasting
model, but we saw huge potential in this and incorporated it
into something that was a marketable trading product. That
occurred because we have a network of quants within Man
who meet up regularly in Oxford and gain exposure to the
work of the academics there.
[TW] We’ve also brought both expertise and people from
Oxford into our electronic trading division where most of
the underlying machinery relies on market micro-structure
methods – a field of practice where Oxford has much to offer.
Has the OMI achieved what you’d hoped it would?
[TW] Yes, it has. The last four years have represented only the
first phase of the collaboration and I think we have in many
ways exceeded our objectives by achieving an ‘openness’ with
the academics.
[AL] They have really embraced this, and we are delighted by
the people we’ve come across, and also by the fact that we
have been able to recruit key talent into our business as well.
Will there be a change of direction with the
appointment of Professor Terry Lyons as the new
OMI Director?
[AL] Neil was instrumental in helping us develop the right
model of collaboration between the University and Man, and
also in persuading academics from various departments to
make this multi-disciplinary institute a reality. There will be
some change of emphasis as Neil comes from an econometrics
background and Terry comes from a mathematics background,
but the core principles of the Institute will remain the same.
[TW] The aim now is to build on this foundation and establish
OMI as the world’s leading quantitative finance institute
and also to see whether we can derive more fruits for Man
through this collaboration.
[AL] In the long run, I’d like to see the Institute’s research
encompass three things: research which is academically world-
leading, research which has wide market and industry systemic
benefit, and research which will benefit Man specifically. OMI
is established in the first area and now it’s about building our
ideas in the other two over the next period.
What are the plans for the future?
[TW] We recently presented back to the management board
to discuss funding for OMI and we are pleased to announce
that this has been renewed until 2015. We’re looking for
opportunities to expand in Oxford in ways that benefit the
wider Group. Work has been very AHL-focused up until
now, but we’d like to have representation from our other
businesses – GLG, Multi-Manager, and MSS.
Man Group ’s co l l id ing wor ldsTim Wong, CEO, AHL and Anthony Ledford, Chief Scientist, AHL discuss their vision of bringing together the commercial and academic worlds of quantitative finance, and choosing Oxford as the place for it all to happen.
4
You must be delighted to have been offered the Directorship
having been involved in OMI since its inception?
The last four years have been a very exciting time for us all. There’s been
a fantastic scale of achievement under Neil’s guidance and leadership.
From our initial work in putting together the bid, we’ve become a
substantial research institute, which independent assessment confirms
has a very wide and respected international reputation as a leading
research institute in quantitative finance.
It certainly seems to attract a very international group of academics
from varied disciplines, is that something you want to build on?
The Institute’s main objective is to address the key problems associated
with financial markets and risk in a way that has significant impact, and
I believe this requires a truly multi-disciplinary effort. It also requires
world-class researchers, which, thanks to the funding we receive from
Man, we are able to attract and recruit. The problems are often too
complex for a single discipline to resolve, but we are fortunate that my
predecessor has drawn together a wide range of individuals with the
right variety of expertise to identify and work on joint projects.
There are already a number of collaborative projects that have
been undertaken at OMI, is this something you’re keen to pursue?
One of my main goals is to create a framework that encourages and
enables collaborations to happen, which is a challenge. There is an
intrinsic contradiction between multi-disciplinary work and disciplinary
excellence. To really succeed in multi-disciplinary projects you need
people who are absolute masters of their field. But there’s a tension
between people’s need to work on their own and contributing to
broader goals. I don’t underestimate the challenge, but it’s absolutely key
to really innovative research, so it’s essential that we make the effort and
succeed in this objective.
In his first interview since his appointment as Director of the Oxford-Man Institute (OMI), Professor Terry Lyons discusses his goals for building on the Institute’s suc cessful foundation.
A multi-discipl inary challenge for the new Director
6
In his first interview since his appointment as Director of the Oxford-Man Institute (OMI), Professor Terry Lyons discusses his goals for building on the Institute’s suc cessful foundation.
A multi-discipl inary challenge for the new Director
Isn’t there a concern that this will detract from a member’s
individual research?
It’s very important that the early stage researchers have an
opportunity to build a strong disciplinary foundation. They should
not have to worry about having to prove their work as applied
to anything. I want to nurture students in their disciplines, but
let them benefit from being part of a multi-disciplinary team.
However, I see no reason why, as researchers develop - and we’re
fortunate enough to have a number of world-class researchers
across the spectrum at OMI - that we cannot identify projects
where expertise in different disciplines can be brought together to
create something that is absolutely cutting edge and I expect my
colleagues to want to jump at those opportunities.
How do you intend to achieve this multi-disciplinary focus?
The ingredients for an environment that will nurture
collaborations are rather intangible. A good collaboration can be
triggered through a casual conversation, so I think it’s important
to enable conversations to transpire on a large scale. The OMI
environment already has many aspects that are effective at
achieving this, such as our common dining area, our large number
of graduate and post doc students and faculty, along with joint
seminars, and I’d certainly like to see these develop.
I’d also like to see members get together and articulate current
projects that are already achieving something quite special, but
could benefit from involvement from other disciplines. We then
need to make sure that we use our resources to facilitate these
projects in moving forward. Through our collaboration with Man
we have tremendous resources available to us, which we should
capitalise on to ease those early stages and ensure we get quality
output. A lot of it is about making sure there are no impediments
to getting started.
OMI enjoys a unique relationship with Man Group. Aside
from the funding they provide, how does the relationship
benefit members of OMI?
To do quality research in an area such as finance, really does
require a detailed engagement between practitioners and
academics. We’re extraordinarily fortunate to have this
collaboration with Man. Sharing a physical environment is hugely
beneficial. We have lunch together and they attend our seminars:
we exchange scepticisms and use their insight to sharpen and
focus our thinking. It’s a very rewarding resource that shapes and
refines the quality of our research. They’ve also benefitted as they
have taken on a number of technicalities that we have developed
and used them in their own research.
What other aspirations do you have for the Institute as you
step into the role of Director?
I would very much like to see jointly funded research projects
with the industrial community. I also believe we should use our
strengths to get involved in projects that have a public interest,
such as effective and novel ways to understand and measure
the risk involved in positions held by banks and other financial
intermediaries – in which case we could leverage our core funding
to add considerable value to any government/research council
funded projects we undertake.
We’d like OMI to be seen as a portal for the whole of the UK’s
academic research in the area of quantitative finance. We
welcome the engagement of our colleagues and practitioners in
the UK and we’re already moving towards closer engagement with
them by taking advantage of Man Group’s fantastic new offices
and lecture theatre in London.
It’s an exciting time and I’m very pleased to be involved. It’s hard
to imagine that in just four years we could have moved so far and
engaged the quality of people and projects that are here at OMI.
Now it’s time to build on that solid foundation.
nithum thain is a MRes student in Computer Science. He holds a Bachelor’s in Mathematics from Queen’s University and a Master’s in Mathematics from McGill.
Nithum’s research interest is in algorithmic game theory,
particularly in financial and economic models that apply game
theoretic structures to practical phenomena. For his current
research, he is considering multi-agent coordination strategies.
kaiwei wangis a first year DPhil student in the Mathematical and Computational Finance Group at the Mathematical Institute. His research is focused on behavioural finance and time inconsistent problems.
sumudu watugalais interested in the areas of international finance, financial markets, contagion, and volatility. Her current work focuses on how interlinks between countries such as trade and capital flows affect markets and economies, especially during periods of financial crisis.
Her undergraduate and previous postgraduate study was in
computer science, engineering, and finance at MIT. Sumudu
worked in the finance industry, specialising in volatility and
derivatives, prior to joining Oxford for her doctoral studies.
yuan xia is a DPhil student at the Mathematical Institute. His research focuses on numerical methods in finance, and he is currently working on a Multilevel Monte Carlo method for jump processes. He is also interested in other topics in financial mathematics, such as volatility modelling.
weijun xuis a DPhil student in the Stochastic Analysis Group under the supervision of Terry Lyons at the University of Oxford. Before joining Oxford, he completed a Bachelor’s Degree in Economics and Mathematics at Shanghai Jiaotong University and a Master’s Degree in Statistics at Harvard.
His research interests lie in the area of probability. He is currently
working on the problem of inversion of signature for paths of
bounded variation. Together with Professor Terry Lyons, he has
developed methods to invert the signature for axis paths, which
can only move parallel to the axes. Now he is trying to solve the
inversion problem for general paths of bounded variation.
danyu yangis working with Professor Terry Lyons on rough path theory and its applications. She is interested in extracting nontrivial information of the path from its signature. She is currently working on the potential application of rough path theory to Harmonic analysis, especially to the convergence problem pioneered by the celebrated theorem of Carleson.
yifei zhongis a third year DPhil student in the Mathematical and Computational Finance group of the Mathematical Institute. He is supervised by Xunyu Zhou and Hanqing Jin.
He completed a Bachelor of Science Degree at Peking University
in China and a Master of Science Degree at the National
University of Singapore, specialising in Mathematical Finance.
His research is currently focused on optimal stopping time and
applied PDEs. He is also interested in behavioural finance and
time inconsistent problems.
S T U d E n T S
17
PEOPLE
kaiwei yifei
dan
yu
yuan
sum
ud
u
nith
um
weijun
horatio boedihardjo
DPhil Student at the Mathematical Institute, University of Oxford.
Research Interests: Schramm-Loewner Evolution in Riemann
Surfaces
andrea calì
Lecturer, Brunel University.
Research Interests: Knowledge Representation and Reasoning,
Database Theory, Web Information Systems, Information
Integration, Logics and Databases
tom cass
Postdoctoral Research Assistant at the Mathematical Institute,
University of Oxford.
Research Interests: Stochastic Analysis, Probability Theory and
Mathematical Finance
samuel cohen
Junior Research Fellow at St. John’s College.
Research Interests: Stochastic Analysis and Mathematical Finance
alice dub
DPhil Student at the Mathematical Institute, University of Oxford.
Research Interests: Stochastic Control, in Particular the Merton
Problem of Optimal Investment with Intermediate Consumption
thomas flury
Quantitative Research Analyst, AHL.
Research Interests: Time-series Econometrics, Financial
Econometrics and Parameter Estimation with Particle Filters
matthias hagmann-von arx
Head of Equities Strategies, AHL.
Research Interests: Non and Semi-parametric Econometrics,
Empirical Finance, Systematic Trading Strategies
tim jenkinson
Professor of Finance at the Saïd Business School, University of Oxford.
Research Interests: Initial Public Offerings, Private Equity,
Securitisation, Regulation and the Cost of Capital
nick jones
Systems Biology Fellow at the Department of Physics, University
of Oxford.
Research Interests: Non-Trivial Temporal Correlations Present in
the Complex Signals that Emerge from Natural Systems and how
these Signals Couple to Underlying Network Dynamics
dmitry kramkov
Professor at Carnegie-Mellon University, Pittsburgh and
part time Professor at the University of Oxford.
Research Interests: Computational Finance – Financial
Derivatives, Optimal Investment, Numerical and Software
Implementations of Financial Algorithms
jeremy large
Research Economist, AHL and Fellow of St. Hugh’s College.
anthony ledford
Chief Scientist, AHL.
Research Interests: Extreme Value Theory, Modelling Financial
Time Series, Automated Trading and Execution Systems, Market
Microstructure and High Frequency Trading
asger lunde
Professor of Economics, School of Economics and Management,
Aarhus University.
Research Interests: Time Series Econometrics, Financial
Econometrics, and the Econometrics of Marketing
colin mayer
Professor of Management Studies, Saïd Business School,
University of Oxford.
Research Interests: Corporate Finance, Corporate Governance,
Corporate Taxation, Regulation of Financial Institutions
michael monoyios
University Lecturer in Financial Mathematics at the
Mathematical Institute, University of Oxford.
Research Interests: Optimal Hedging in Incomplete Markets,
Transaction Costs and Singular Control, Parameter Uncertainty
in Investment and Hedging, Insider Trading and Information
Problems
A S S o c i AT E m E m b E R S
18
per mykland
Robert M. Hutchins Distinguished Service Professor,
Department of Statistics, The University of Chicago.
Research Interests: High Frequency Financial Econometrics
thomas noe
Ernest Butten Professor of Management Studies and Fellow of
Balliol College.
Research Interests: The Application of Game Theory to the
Design of Financial Securities and Corporate Governance Systems.
The Interaction Between Product and Financial Markets and the
Effect of Financial Markets on Managerial Incentives.
wei pan
DPhil Student in the Stochastic Analysis Group, University of Oxford.
Research Interests: Application of Cubature Method to Various
Option Pricing Problems
andrew patton
Associate Professor of Economics, Duke University.
Research Interests: Financial Econometrics, Forecasting,
Volatility and Dependence Models, Hedge Funds
cornelius probst
DPhil Student at the Department of Statistics.
Research Interests: Bayesian Statistics under Computational
and Temporal Constraints: Sequential Monte Carlo with Data
Streaming Methods. Topics in Computational Statistics such as
GPU Computing. High-Frequency Financial Data such as Limit
Order Book Data.
zhongmin qian
University Lecturer in the Mathematical Institute and Fellow at
Exeter College.
Research Interests: Rough Path Analysis and Non-linear Partial
Differential Equations
christoph reisinger
University Lecturer in Mathematical Finance at the
Mathematical Institute, University of Oxford.
Research Interests: Modelling of Financial Markets and the
Development, Analysis and Implementation of Efficient
Methods for Derivative Pricing
torsten schöneborn
Quantitative Analyst, AHL.
Research Interests: Market Microstructure, Optimal Trade
Execution, Optimal Investment under Transaction Costs
bernard silverman
Chief Scientific Adviser to the Home Office and a
Professor of Statistics at the University of Oxford.
Research Interests: Computational Statistics, Smoothing
Methods, Functional Data Analysis, Multiresolution Analysis in
Statistics and the Analysis of Very High Dimensional Data
suresh sundaresan
Chase Manhattan Bank Professor of Financial Institutions,
Columbia University.
Research Interests: Central Bank Liquidity Provision, Hedge
Funds, Asset Allocation
lukas szpruch
Nomura Research Fellow at the Mathematical and
Computational Finance Group within the Mathematical Institute.
Research Interests: Theoretical and Applied
Probability Theory, Stochastic Analysis and Numerical
Methods for Stochastic Processes
pedro vitori
DPhil Student in Mathematics, University of Oxford.
Research Interests: Stochastic Analysis, Optimal Control and
Mathematical Finance
jan hendrik-witte
DPhil Student at the Mathematical Institute, University of Oxford.
Research Interests: The Development of Unconditionally Stable
Finite Difference Schemes for the Numerical Solution of Non-
linear Partial Differential Equations in Finance
A S S o c i AT E m E m b E R S
19
PEOPLE
Hedge Fund Conference 19th november 2010
Organising Committee
Andrew Patton, duke University and oxford-man institute and Tarun Ramadorai, University of oxford
Speakers
David Hsieh, Duke University; Wei Jiang, Columbia University;
Philippe Jorion, University of California, Irvine; Robert Kosowski,
Tanaka Business School, Imperial College; Tarun Ramadorai,
University of Oxford; Oliver Scaillet, HEC Geneva
Adam Smith Asset Pricing Workshop25th march 2011
Organising Committee
christian Julliard, london School of Economics; Anna Pavlova, london business School; Tarun Ramadorai, University of oxford; Raman Uppal, london business School; mungo wilson, University of oxford; kathy yuan, london School of Economics
Speakers
Anisha Ghosh, Carnegie Mellon University; Christian Julliard,
London School of Economics; Alex P. Taylor, Manchester Business
School; Bryan Kelly, University of Chicago; Seth Pruitt, Federal
Reserve Board of Governors; Snehal Banerjee, Northwestern
University; Jeremy Graveline, University of Minnesota; Jules van
Binsbergen, Northwestern University and Stanford GSB; Michael
Brandt, Duke University; Ralph Koijen, University of Chicago;
Doron Avramov, Hebrew University of Jerusalem and University
of Maryland; Tarun Chordia, Emory University; Gergana Jostova,
George Washington University; Alexander Philipov, George
Mason University; Anders Anderson, Institute for Financial
Research (SIFR); Jose Vicente Martinez, University of Oxford;
Frederico Belo, University of Minnesota; Vito Gala, London
Business School; Jun Li, University of Minnesota
This event was hosted at Saïd Business School with funding
contributed from Oxford-Man Institute.
Advances in Portfolio Theory and Investment Management13th-14th may 2011
Organising Committee
ioannis karatzas, columbia and inTEch; Alex Schied, University of mannheim; Thaleia Zariphopoulou, University of oxford
Stochastic Portfolio Theory
Speakers
Erhan Bayraktar, University of Michigan, Ann Arbor; Robert
Fernholz, INTECH; Kostas Kardaras, Boston University; Vassilios
Papathanakos, INTECH; Johannes Ruf, Columbia University;
Winslow Strong, University of California, Santa Barbara
Portfolio Management under Forward Criteria
Speakers
Nicole El Karoui, École Polytechnique; Marek Musiela, BNP
Paribas; Sergey Nadtochiy, University of Oxford; Michael
Tehranchi, University of Cambridge
Optimal Execution of Trades
Speakers
Aurélien Alfonsi, ENPC; Charles-Albert Lehalle,
Crédit Agricole Cheuvreux; Alex Schied, University
of Mannheim; Sasha Stoikov, Cornell University
E v E n T S
20
The New Commodity Markets14th-15th June 2011
Organising Committee
René carmona, Princeton University and Thaleia Zariphopoulou, University of oxford
Speakers
Knut Kristian Aase, NHH; Fred Espen Benth, University of Oslo;
Álvaro Cartea, Universidad Carlos III; Umut Cetin, LSE; Hélyette
Geman, Birbeck; Ben Hambly, University of Oxford; Sam Howison,
University of Oxford; Vincent Kaminski, Rice University; Rüdiger
Kiesel, University of Duisburg-Essen; Lars Lochstoer, Columbia
University; Ronnie Sircar, Princeton University; Nizar Touzi, École
Polytechnique; Wei Xiong, Princeton University
OMI and OCCAM Joint Workshop on Stochastic Differential Equations: Numerical Algorithms and Applications8th-10th August 2011
Organising Committee
lajos Gergely Gyurko, University of oxford; lukas Szpruch, University of oxford; konstantinos Zygalakis, University of oxford
Speakers
David F. Anderson, University of Wisconsin, Madison; Radek Erban,
University of Oxford; Peter Friz, TU Berlin; Mike Giles, University of
Oxford; Desmond J. Higham, University of Strathclyde; Arnulf Jentzen,
Princeton University; Peter E. Kloeden, Goethe Universitat; Terry Lyons,
University of Oxford; Stéphane Menozzi, Université Paris VII,
Denis Diderot; Christoph Resinger, University of Oxford;
Erik von Schwerin, KAUST; Eric Vanden-Eijnden, NYU
This event was jointly funded with the Oxford
Centre for Collaborative and Applied
Mathematics (OCCAM).
E v E n T S
21
Workshops and Courses
Stochastic Portfolio Theory, 12th may 2011 Ioannis Karatzas, Columbia University and INTECH
An overview of stochastic portfolio analysis building on the work of E.R. Fernholz,
A. Banner, C. Kardara, S. Pal, V. Papathanakos, T. Ichiba, D. Fernholz and J.Ruf.
Risk measures, June 2011 Fred Delbaen, ETH, Zurich
Two sets of two lectures around the topic of risk measures. As a real expert in this field,
these lectures attracted a large audience.
new commodity markets, 13th June 2011 René Carmona, Princeton University
A general introduction to the commodity markets, emphasising the physical nature of the
interests underlying the contracts and derivatives, including discussion of the growing
role of commodity indexes, the impact of the recent regulations, and some of the newest
markets. A second lecture concentrated on specific mathematical models, their analysis and
implementations using the examples of spread options.
lmS-EPSRc Summer School, 18th- 22nd July 2011 Dr Michael Monoyios, University of Oxford
This event was jointly funded with the London Mathematical Society.
EVENTS
S T U d E n T c o l l A b o R AT i o n
22
Student members of OMI are awarded personal desk space in
the Institute, access to its computational resources, a research
allowance of £2000 per annum, as well as admission to its
common room, catering and busy conference and seminar
programme. In return, students are expected to spend around
half their working week at the Institute, but as Korda and Lee
explain, the obligation is no hardship.
“The Institute’s resources are exceptional - far better than what’s currently available through my department. I get great desk space here, a lovely double computer screen, and access to any kind of server I want to use,” says Korda. “Because its run like a business, we’ve got dedicated IT and administration teams that are extremely efficient, which makes life very easy for us.”
Lee adds, “I work in computational statistics, and it can often be very hard to get what you need computationally, but the IT department here is fantastic. If you ever need anything you always know exactly who to go to and they respond very quickly.”
Both students recognise that the funding they’ve received from
the scholarship has played a significant part in advancing their
research. It has not only provided the opportunity for them to
travel to conferences and explore collaborations, but in the case
of Korda, it has enabled him to maintain essential contact with
his supervisor. He explains, “This year my supervisor moved to
Toulouse and without the scholarship funds I wouldn’t have
been able to afford to travel to see him. Having the extra
£2,000 a year gives us more freedom and opportunity in our
research, and the fact that lunch and dinner is provided really
eases the pressure on my personal finances and my time.”
Of course, OMI’s purpose built building was designed to
encourage interaction between the Institute’s academic
researchers and the commercially focused Man Research
Laboratory. For students at the Institute, the opportunity to
gain real industry insight from the Man Group is invaluable.
As Korda explains, interaction is practically unavoidable,
“Sharing common areas and lunch and coffee breaks with the Man Group gives you exposure to people working in the industry that wouldn’t be available to you otherwise. It’s given me a chance to learn about the banking industry and what their work involves.”
The interdisciplinary nature of the Institute has also had a
major impact on both students’ research. The opportunity to
work alongside academics from different research fields within
quantitative finance has not only enhanced their personal
research, but has led to a collaboration between the two
students, that is unlikely to have materialised otherwise.
students members strike up more than just a collaboration…
Students play a significant role in the life of the Institute. Over 20 scholarships are awarded
each year to DPhil students at the University of Oxford who are researching topics connected
with quantitative finance. Nathan Korda and Anthony Lee both completed their DPhils in
the summer and have been members of the Institute for the duration of their doctorates. For
them, the Institute has provided an environment that has nurtured their understanding of
quantitative finance, broadened the scope of their research and afforded many opportunities
that would otherwise have been unavailable to them.
S T U d E n T c o l l A b o R AT i o n
23
“The interdisciplinary nature of the Institute has opened up the opportunity for me to talk to people in different fields. You’d be missing an opportunity if you just allowed yourself to focus on your research in isolation when you’ve got such high calibre people involved in similar fields, all under one roof,” says Lee.
Over the last two years Korda and Lee, a probabalist and
statistician respectively, have been collaborating on a research
project. Sharing an office in the Institute’s original building
three years ago sparked a common interest for a strategy in
the exploitation and exploration trade-off business. As Korda
explains, the opportunity to discuss his research problem
with a statistician is unlikely to have transpired outside of the
Institute, “We would not have achieved this collaboration
without being here. It’s been a slow burning project since
we first shared an office three years ago and it’s certainly
something we’ll continue to work on together when we leave.”
Korda’s personal research project has also been heavily influenced by his interaction with Lee. “My conversations with Anthony made me pay much more attention to the statistical side of my problem. So much so, I’ve decided that I’m going to an applicable statistical institute in Lille to do my post doc in September, as I have realised that it is important for me to focus much more on the statistical side of my research.”
Lee has also benefitted from the opportunity to collaborate
with Korda. He explains, “It’s been really helpful to talk in
depth with someone working in probability. It’s very beneficial
to talk to someone who has a keener attention to mathematical
rigour than I do - at times!”
Through the Institute, the two students have struck up more than just a research collaboration. Their shared love of music has resulted in them forming a jazz band, which has performed at the Institute on more than one occasion. Asked whether the band is open to members of the Institute, Korda jokes, “Some people have suggested that they’d like to sing with us, but they never put themselves forward forcibly enough!”
So, if any members do fancy a turn in front of the microphone,
now’s your chance - the Institute may have helped form a
lifelong academic association, but the band will have to disband
when Korda leaves for Lille and Lee starts his post doc at
Warwick in September 2011.
FEATURE
faculty
Long-term visitors:
Fred Delbaen, Department of Mathematics, ETH, Zurich
Ronnie Sircar, Operations Research & Financial Engineering,
Princeton University
Mingyu Xu, Institute of Applied Mathematics, Academy of
Mathematics and Systems Science, Chinese Academy of Sciences (CN)
Short-term visitors:
Marco Avellaneda, Professor of Mathematics, Courant Institute
of Mathematical Sciences, NYU
Leopoldo Bertossi, School of Computer Science, Carleton University
Francis Caron, University of British Columbia
Robert J Elliot, RBC Financial Group Professor of Finance,
Haskayne School of Business, University of Calgary, Alberta
Ioannis Karatzas, Eugene Higgins Professor of Applied
Probability, Columbia University and INTECH
Marcin Kacperczyk, Assistant Professor of Finance,
Leonard N. Stern School of Business, New York University
Eva-Maria Lütkebohmert-Holtz, Head of the Research Group
for Quantitative Finance Pricing of Risks in Incomplete Markets,
University of Freiburg
Klaus Ritter, Computational Stochastics, Department of
Mathematics, Technische Universität Kaiserslautern
Boris Rozovsky, Ford Foundation Professor of Applied
Mathematics, Brown University
Olivier Scaillet, Professor of Finance and Statistics,
Swiss Finance Institute
George Tauchen, William Henry Glasson Professor of
Economics and Finance, Duke University
graduate students
Long-term visitors:
Heather Battey, University of Cambridge
Kai Du, School of Mathematical Sciences, Fudan University
Andrea Karlová, Stochastic Informations, Institute of
Information Theory and Automation (UTIA)
Henning Marxen, Department of Mathematics, Technische
Universität Kaiserslautern
Jian Su, University of Illinois, Chicago
Jin Zhang, University of Illinois, Chicago
Short-term visitors:
Yunjiang Jiang, Department of Mathematics, Stanford University
Phillip Monin, The University of Texas at Austin
practitioners
Long-term visitors:
Tim Hoggard, visiting research fellow
Sushant Vale, Tata Consulting Services
v i S i T o R S
24
The Institute has hosted a number of visitors over the past year – many come to give
seminars while others come to work on collaborative projects with OMI members.
C“I had heard about the Institute and how well it manages to
Cintegrate the strengths of the University in Cmathematics, statistics, economics and finance,
but the reality is even better. The physical setup Iis superb; the facilities and amenities world-class. I had a good chance to participate in a series of
presentations by Ph.D candidates over in the maths Cdepartments, and a chance to discuss research issues
Cwith several faculty and visitors at OMI.”
CIoannis Karatzas
25
VISITORS
“OMI is a very good place to do research. There is a lot going on: many visitors, many seminars and plenty of
opportunities to meet people. That is what makes OMI interesting – the presence of “Practitioners” and/or “quants” enables
discussions on problems that are not always treated in academic surroundings.
Having so many visitors and university people around ensures there is always
an audience for good discussion.”
Fred Delbaen
“I was very impressed with the level of scholarship and the
research at OMI. The researchers and faculty were eager to exchange ideas and discuss
their work. It was intellectually rewarding to visit the Institute.”
Marco Avellaneda
“I immensely enjoyed my visit to OMI. I was able to meet and interact
with several researchers, either directly in my field or in allied fields. The interdisciplinary character of OMI was most rewarding. I found
the seminars by external visitors extremely interesting and helpful. For instance I learned from one speaker, the details of the “Billion Prices” project at MIT, and I was honoured to
join the dinner group for that speaker. The hospitality was warm and thoughtful.
I would truly enjoy visiting again.”
George Tauchen
“My talk at OMI was very well attended by
mathematicians of various ages. I got a very positive reaction
and enjoyed some interesting discussions on several
mathematical questions of joint interest.”
Etienne Pardoux
“My visit was intellectually stimulating and delightful, with a lunch, seminar, and meetings
with the faculty. I was impressed by the range of issues which the faculty was working on, and the
excellent research facilities and environment at OMI.”
Suresh Sundaresan
C“I had heard about the Institute and how well it manages to
Cintegrate the strengths of the University in Cmathematics, statistics, economics and finance,
but the reality is even better. The physical setup Iis superb; the facilities and amenities world-class. I had a good chance to participate in a series of
presentations by Ph.D candidates over in the maths Cdepartments, and a chance to discuss research issues
Cwith several faculty and visitors at OMI.”
CIoannis Karatzas
sam cohenCohen, S.N., Ji, S. and Peng, S. 2011. Sublinear Expectations and Martingales in Discrete Time.
Cohen, S.N. 2011. Representing Filtration Consistent Nonlinear Expectations as G-Expectations in General Probability Spaces.
Cohen, S.N., Elliott, R.J. and Siu, T.K. 2011. Backward Stochastic Difference Equations for Dynamic Convex Risk Measures on a Binomial Tree.
martin gouldGould, M., Porter, M., William, S., McDonald, M., Fenn, D. and Howison, S. 2011. Limit Order Books.
Gould, M., Porter, M., William, S., McDonald, M., Fenn, D. and Howison, S. 2011. Statistical Properties of Forging Exchange Limit Order Books.
georg gottlobGottlob, G. 2011. On Minimal Constraint Networks.
Benedikt, M., Gottlob, G. and Senellart, P. 2011. Determining Relevance of Accesses at Runtime (Extended Version).
jan hendrik witte Witte, J.H. and Reisinger, C. 2010. On the Penalisation Error for American Options in a Jump Model.
Witte, J.H. and Reisinger, C. 2010. On the Use of Policy Iteration as an Easy Way of Pricing American Options.
Witte, J.H. and Reisinger, C. 2010. A Penalty Method for the Numerical Solution of Hamilton-Jacobi-Bellman (HJB) Equations in Finance.
ni haoHao, N. and Lyons, T. 2011. Expected Signature of Brownian Motion up to the First Exit Time of the Domain.
vicky henderson Henderson, V., Sun, J. and Whalley, E. 2011. Portfolios of American Options under General Preferences: Results and Counterexamples.
Henderson, V. and Hobson, D. 2009. Risk Aversion, Indivisible Timing Options and Gambling.
Henderson, V. 2009. Partial Liquidation and the Disposition Effect.
tim jenkinsonJenkinson, T.J. and Stucke, R. 2011. Who Benefits from the Leverage in LBOs?
Jenkinson, T.J., Axelson, U., Stromberg, P. and Weisbach, M. 2010. Borrow Low, Buy High? The Determinants of Leverage and Pricing in Buyouts.
dmitry kramkovKramkov, D. and Predoiu, S. 2011. Integral Representation of Martingles and Endrogenous Completeness of Financial Models.
Kramkov, D and Bank, P. 2011. A Model for a Large Investor Trading at Market Indifference Prices.
anthony lee Lee, A., Caron, F. Doucet, A. and Holmes, C. 2011. A Hierarchical Bayesian Framework for Constructing Sparsity-Inducing Priors.
Lee, A., May, B.C., Korda, N. and Leslie, D. N. 2011. Optimistic Bayesian Sampling in Contextual-Bandit Problems.
Lee, A., Caron, F., Doucet, A. and Holmes, C. 2011. Bayesian Sparsity-Path-Analysis of Genetic Association Signal using Generalized t Priors.
anthony ledfordLedford, A. and Ramos, A. 2011. Markov Modelling of the Within Time Series Dependence. Presented at Environmental Risk and Extreme Events, Ascona.
colin mayerMayer, C. 2011. Mobile Banking and Financial Inclusion: The Regulatory Lessons.
Mayer, C. 2011. Savings as Forward Payments: Innovations on Mobile Money Platforms.
Mayer, C. 2010. Regulatory Sanctions and Reputational Damage in Financial Markets.
thomas noeNoe, T., Banerjee, S. and Bhattacharyya, S. 2011. Pumping up the SEO: The Rewards of Uninformed Speculation.
han ozsoylevOzsoylev, H., Walden, J., Yavuz, D. and Bildik, R. 2011. Investor Networks in the Stock Market. Mimeo.
wei panPan, W. Application of Cubature Method to TARN Option Pricing. Pricing Digital Option Using Cubature Method.
w o R k i n G PA P E R S
26
tarun ramadoraiRamadorai, T. and Patton, A. 2011. On the High-Frequency Dynamics of Hedge Fund Risk Exposures. Internet Appendix.
Ramadorai, T. 2010. On the Dynamics of Hedge Fund Risk Exposures.
Ramadorai, T. and Streatfield, M. 2011. Money for nothing? Understanding Variation in Reported Hedge Fund Fees.
Ramadorai, T., Jotikasthira, P. and Lundblad, C. 2011. Asset Fire Sales and Purchases and the International Transmission of Financial Shocks.
Ramadorai, T., Watugala, S. and Albuquerque, R. 2011. Trade Credit and International Return Comovement.
Ramadorai, T., Acharya, V. and Lochstoer, L. 2010. Limits to Arbitrage and Hedging: Evidence from Commodity Markets.
christoph reisinger Reisinger, C. and Giles, M. 2011. Stochastic Finite Differences and Multilevel Monte Carlo for a Class of SPDEs in Finance.
Reisinger, C. and Bujok, K. 2011. Valuation of Basket Credit Derivatives in Structural Jump-Diffusion Models.
Reisinger, C. and Gupta, A. 2011. Robust Calibration of Financial Models Using Bayesian Estimators.
daniel schwarzSchwarz, D. and Howison, S. 2011. Structural Modelling of Carbon Emission Markets.
Schwarz, D. and Howison, S. 2011. Asymptotic Analysis of Pricing Models for Carbon Emission Markets.
Schwarz, D., Carmona, R. and Coulon, M. 2011. Structural Modelling of Clean Spread Options and the Valuation of Power Plants.
lukas szpruchSzpruch, L. and Giles, M. 2011. A Note on Milstein Fundamental Theorem for Non-linear SDEs.
Szpruch, L. and Giles, M. 2011. Efficient Multilevel Monte Carlo Simulations of Non-Linear Financial SDEs without a Need of Simulation of Levy Areas.
Szpruch, L. and Mao, X. 2011. Strong Convergence and Stability of Numerical Methods for Non-Linear Stochastic Differential Equations under Monotone Condition.
Szpruch, L. and Mao, X. 2011. Strong Convergence Rates for Backward Euler-Maruyama Method for Dissipative-type Stochastic Differential Equations with Super-Linear Diffusion Coefficients.
ruediger stuckeStucke, R., Harris, B. and Jenkinson, T. 2010. A White Paper on Private Equity Research and Data.
Stucke, R. 2010. Does Private Equity Underperform or Outperform Public Equity?
Stucke, R. and Higson, C. 2010. The Private Equity Performance Puzzle.
suresh sundaresanSundaresan, S. and Wang, Z. 2011. On the Design of Contingent Capital with Market Trigger.
sumudu watugalaWatugala, S. W., Albuquerque, R. and Ramadorai, T. 2011. Trade Credit and International Return Comovement.
weijun xuXu, W. And Jiang, Y. 2010. On Number of Turns in Reduced Random Lattice Paths.
yuan xiaXia, Y. and Giles, M. 2010. Multilevel Path Simulation for Jump-Diffusion SDEs.
thaleia zariphopoulouZariphopoulou, T., Musiela, M. and Sokolova, E. 2010. Indifference Valuation under Forward Valuation Criteria: The Case Study of the Binomial Model.
Zariphopoulou, T., Leung, T. and Sircar, R. 2011. Forward Indifference Valuation of American Options, submitted for publication.
Zariphopoulou, T. and Nadtochiy, S. 2011. A Class of Homothetic Forward Investment Process with Non-Zero Volatility, submitted for publication.
Zariphopoulou, T. and Kallblad, S. 2011. On the Forward and Backward Portfolio Problem in Log-Normal Markets.
Zariphopoulou, T., Kallblad, S. and Malamud, S. 2011. Qualitative Properties of Optimal Portfolios in Log-Normal Markets.
Zariphopoulou, T. and Kallblad, S. 2011. Forward Optimal Portfolios.
lan zhangZhang, L., Li, Y., Mykland, P., Renault, E. and Zheng, X. 2011. Realized Volatility when Sampling Times can be Endogenous. In revision for Econometric Theory.
Zhang, L. 2011. What You Don’t Know Cannot Hurt You: On the Detection of Small Jumps.
Zhang, L. and Mykland, P. 2011. Between Data Cleaning and Inference: Pre-Averaging and Other Robust Estimators of the Efficient Price.
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PAPERS
sylvestre burgos
Burgos, S. Expected. 2010. Computing Greeks Using Multilevel Path Simulations, Monte Carlo and Quasi-Monte Carlo Methods, Springer Verlag, to appear.
andrea calì
Cali, A. and Pieris, A. 2011. On Equality-Generating Dependencies in Ontology Querying (extended abstract), Proc. of SEBD.
Cali, A., Gottlob, G. and Pieris, A. 2011. New Expressive Languages for Ontological Query Answering, Proc. of AAAI, to appear.
Cali, A., Gottlob, G. and Pieris, A. 2011. Querying Conceptual Schemata with Expressive Equality Constraints, Proc. of ER, to appear.
Cali, A., Gottlob, G. and Pieris, A. 2011. An Ontological Query Answering under Expressive Entity-Relationship Schemata, Information Systems Journal, to appear.
Cali, A., Gottlob, G., Kifer, M., Lukasiewic, T. and Pieris, A. 2010. Ontological Reasoning with F-Logic Lite and its Extentions. Proc. of AAAI.
Cali, A., Gottlob, G. and Pieris, A. 2010. Query Rewriting Under Non-Guarded Rules, Proc. of AMW.
Cali, A., Gottlob, G. and Pieris, A. 2010. Query Answering Under Expressive Entity-Relationship Schemata. Proc. of ER,. 347-361.
Cali, A., Gottlob, G., Pieris, A., Marnette, B. and Lukasiewicz, T. 2010. Datalog±: A Family of Logical Knowledge Representation and Query Languages for New Applications. Proc. of LICS,. 228-242.
Cali, A., Gottlob, G. and Pieris, A. 2010. Query Answering under Non-Guarded Rules in Datalog±:. Proc. Of RR,. 1-17.
Cali, A., Gottlob, G. and Pieris, A. 2010. Advanced Processing for Ontological Queries. PVLDB 3 (1),. 554-565.
thomas cass
Cass, T., Litterer, C. and Lyons, T. Rough Paths on Manifolds. (New Trends in Stochastic Analysis and Related) Topics, Worlds Scientific Press, to appear.
Cass, T. 2009. Smooth Densities for Stochastic Differential Equations with Jumps. Stochastic Process. Appl, no.5, 1416-1435.
sam cohen
Cohen, S.N. and Elliott, R.J. Existence, Uniqueness and Comparisons for BSDEs in General Spaces, in Annals of Probability, to appear.
Cohen, S.N. and Elliott, R.J. Backward Stochastic Difference Equations and Nearly-Time-Consistent Nonlinear Expectations, SIAM Journal of Control and Optimization, 49, 125-139.
Cohen, S.N., Elliott, R.J. and Pearce, C.E.M. A General Comparison Theorem for Backward Stochastic Differential Equations, Advances in Applied Probability, 42(3), 878-898.
thomas flury
Flury, T. 2010. Econometrics of Dynamic Non-Linear Models in Macroeconomics and Finance; DPhil thesis, University of Oxford.
mike giles
Giles, M., Klingbeil, G. and Erban, R. 2011. Fat vs. Thin Threading Approach on GPUs: Application to Stochastic Simulation of Chemical Reactionism, Transactions on Parallel and Distributed Systems, to appear.
Giles, M., Klingbail, G. and Erban, R. 2011. Parallel Stochastic Simulation for the Systems Biology Toolbox 2 for MAT- LAB, Bioinformatics, to appear (subject to minor revisions).
Giles, M. 2011. Approximating the Erfinv Function, GPU Compute Gems, volume 2, Morgan Kaufmann, to appear.
Giles, M., Bradley, T., Du Toit, J., Tong, R. and Woodhams, P. 2011. Parallelisation Techniques for Random Number, GPU Computing Gems, 1, Morgan Kaufmann, to appear.
georg gottlob
Gottlob, G., Sellers, A. J., Furche, T., Grasso, G. and Schallhart, C. 2011.Taking the OXPath Down the Deep Web. EDBT, 542-545.
Gottlob, G., Aschinger, M., Drescher, C., Jeavons, P. and Thorstensen, E. 2011. Structural Decomposition Methods and What They are Good For. STACS, 12-28.
Gottlob, G., Sellers, A. J., Furche, T., Grasso, G. and Schallhart, C. 2011. OXPath: Little Language, Little Memory, Great Value. WWW (Companion Volume), 261-264.
Gottlob, G., Pichler, R. and Savenkov, V. 2011. Normalization and Optimization of Schema Mappings. VLDB J, 20(2), 277-302.
Gottlob, G., Cali, A., Kifer, M., Lukasiewic, T. and Pieris, A. 2010. Ontological Reasoning with F-Logic Lite and its Extentions. Proc. Of AAAI.
Gottlob, G., Cali, A. and Pieris, A. 2010. Query Rewriting under Non-Guarded Rules. Proc. of AMW.
Cali, A., Gottlob, G. and Pieris, A. 2010. Query Answering under Expressive Entity-Relationship Schemata. Proc. of ER, 347-361.
Gottlob, G., Cali, A., Pieris, A., Marnette, B. and Lukasiewicz, T. 2010. Datalog±: A Family of Logical Knowledge Representation and Query Languages for New Applications. Proc. Of LICS, 228-242.
Gottlob, G., Cali, A. and Pieris, A. 2010. Query Answering under Non-Guarded Rules in Datalog±:. Proc. of RR, 1-17.
Gottlob, G., Cali, A. and Pieris, A. 2010 Advanced Processing for Ontological Queries. PVLDB 3 (1), 554-565.
ben hambly
Hambly, B.M., Bush, N., Haworth, H., Jin, L. and Reisinger, C. Stochastic Evolution Equations in Portfolio Credit Modelling, SIAM J. Fin. Math, to appear.
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Hambly, B.M., Biggins, J.D., and Jones, O.D. 2011. Multifractal Spectra for Random Self-Similar Measures via Branching Processes, Adv. Appl. Prob, 43, 1-39.
Hambly, B.M. 2011. Asymptotics for Functions Associated with Heat Flow on the Sierpinski Carpet, Canadian J. Math, 63, 153-180.
Hambly, B.M. and Croydon, D.A. 2010. Spectral Asymptotics for Stable Trees, Elec. J. Probab, 15, 1772-1801.
vicky henderson
Henderson, V. and Hobson, D. 2011. Optimal Liquidation of Derivative Portfolios, Mathematical Finance, to appear.
Henderson. V. 2010. Is Corporate Control Effective When Managers Face Investment Timing Decisions in Incomplete Markets?, Journal of Economic Dynamics and Control, 34 (6), 1062-1076.
jan hendrik witte
Witte, J.H. and Reisinger, C. 2011. A Penalty Method for the Numerical Solution of Hamilton-Jacobi-Bellman (HJB) Equations in Finance, SIAM Journal of Numerical Analysis, 49(1), 213—231.
chris holmes
Holmes, C. ,Yau, C., Papaspiliopoulos, O. and Roberts, G. 2011. Bayesian Non-Parametric Hidden Markov Models with Applications in Genomics, J Royal Stat Soc, Series B 73 (Part 1), 33-57.
Holmes, C., Hjort, N., Muller, P. and Walker, S. 2010. Bayesian Nonparametrics. Cambridge University Press.
tim jenkinson
Jenkinson, T.J., Abrahamson, M., and Jones, H. 2011. Why don’t U.S. Issuers Demand European Fees for IPOs?, Journal of Finance, to appear.
Jenkinson, T.J. and Sousa, M. 2011. Why SPAC Investors should listen to the Market?, Journal of Applied Finance, to appear.
hanqing jin
Jin, H., Dai, M. and Liu, H. 2011. Illiquidity, Position Limits, and Optimal Investment for Mutual Funds, Journal of Economic Theory, to appear.
Jin, H. and Zhou, X. 2011. Greed, Leverage, and Potential Losses: A Prospect Theory Perspective, Mathematical Finance, to appear.
Jin, H., Zhang, S., Hanqing, J., Zhang, S. and Yu Zou, X. 2011. Behavioural Portfolio Selection with Bounded Loss, Acta Mathematica Sinica.
Jin, H., Dai, M., Zhong, Y. and Yu Zhou, X. 2010. Buy Low and Sell High. Contemporary Quantitative Finance, 317-334.
ada lau
Lau, A. and McSharry, P. 2010. Approaches for Multi-Step Density Forecasts with Application to Aggregated Wind Power, Annals of Applied Statistics, 4, (3), 1311–1341.
Lau, A., Baaquie, B. E., Cao, Y. and Tang, P. 2011. Path Integral for Equities: Dynamic Correlation and Empirical Analysis, Physica A, to appear.
anthony ledford
Ledford, A. and Ramos, A. 2011. An Alternative Point Process Framework for Modelling Multivariate Extreme Values, Communications in Statistics - Theory and Methods, 40, (12), 2205 – 2224.
anthony lee
Lee, A. 2010. Comment on Particle Markov Chain Monte Carlo Methods. J. Royal Statistical Soc. B.
Lee, A. 2010. On the Utility of Graphics Cards to Perform Massively Parallel Simulation with Advanced Monte Carlo Methods. JCGS.
gechuan liang
Liang, G., Lyons, T. and Qian, Z. Backward Stochastic Dynamics on a Filtered Probability Space, Annals of Probability, to appear.
Liang, G. and Jiang, L. A Modified Structural Model for Credit Risk, IMA Journal of Management Mathematics, to appear.
jeremy large
Large, J. 2011. Estimating Quadratic Variation when Quoted Prices Change by a Constant Increment, Journal of Econometrics, 160, 2-11.
terry lyons
Lyons, T., Cass, T. and Litterer, C. 2011 Integrability Estimates for Gaussian Rough Differential Equations, 1-23, arXiv: 1104.1813
Lyons, T., Cass, T. and Litterer, C. 2011. Rough Paths on Manifolds, New Trends in Stochastic Analysis and Related Topics, A volume in Honour of Prof K.D. Elworthy, arXiv: 1102.0998v1
Lyons, T. and Hao, N. 2011. Expected signature of two dimensional Brownian Motion up to the first exit time of the domain. Pgs. 1-21 arXiv: 1101.5902
Lyons, T., Liang, G. and Qian, Z. 2010. A Functional Approach to FBSDEs and Its Application in Optimal Portfolios, arXiv: 1011.4499
Lyons, T. and Litterer, C. 2010. High order recombination and an application to cubature on Wiener space, arXiv: 1008.4942
michael monoyios
Monoyios, M. and Ng, A. 2011. Optimal Exercise of an Executive Stock Option by an Insider, International Journal of Theoretical and Applied Finance, 1483-106.
Monoyios, M., Ng, A. and Danilova, A. 2010. Optimal Investment with Inside Information and Parameter Uncertainty, Mathematics and Financial Economics, 3, 13-38.
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RESEARCH
Monoyios, M. 2010. Utility-Based Valuation and Hedging of Basis Risk with Partial Information, Applied Mathematical Finance, 17, 519-551.
josé martinez
Martinez, J. and Sandleris, G. 2011. Is it Punishment? Sovereign Defaults and the Declines in Trade, Accepted, Journal of International Money and Finance, to appear.
Martinez, J. 2010. Information Misweighting and the Cross Section of Stock Recommendations, Journal of Financial Markets, to appear.
per mykland
Mykland, P. A., Lin, M. and Chen, R. 2010. On Generating Monte Carlo Samples of Continuous Diffusion Bridges, Journal of the American Statistical Association, 105, 820-838.
Mykland, P.A., Ait-Sahalia, Y., and Zhang, L. 2011. Ultra High Frequency Volatility Estimation with Dependent Microstructure Noise, Journal of Econometrics, 160, 160-165.
Mykland, P.A., Zhang, L., and Aït-Sahalia, Y. 2011. Edgeworth Expansions for Realized Volatility and Related Estimators, Journal of Econometrics, 160, 190-203.
Mykland, P.A., and Zhang, L. 2011. The Double Gaussian Approximation for High Frequency Data, Scandinavian Journal of Statistics, to appear.
thomas noe
Noe, T. 2010. Where Did all the Dollars Go? The Effect of Cash Flows on Capital and Asset Structure, Forthcoming in the Journal of Financial and Quantitative Analysis, to appear.
Noe, T. 2009. Tunnel-Proofing the Executive Suite: Transparency, Temptation, and the Design of Executive Compensation, Review of Financial Studies, 22, 4849-4880 (lead article).
Noe, T. 2009. Stock Market Liquidity and Firm Performance: Wall Street Rule or Wall Street Rules? (with Fang, V. and Tice, S.), Journal of Financial Economics, 94, 150-169.
diaa noureldin
Noureldin, D., Shephard, N. and Sheppard, K. 2011. Multivariate High-Frequency-Based Volatility (HEAVY) Models. Journal of Applied Econometrics, to appear.
han ozsoylev
Ozsoylev, H. and Werner, J., 2011. Liquidity and Asset Prices in Rational Expectations Equilibrium with Ambiguous Information, Economic Theory, to appear.
cavit pakel
Pakel, C., Shephard N. and Sheppard K., 2011. Nuisance Parameters, Composite Likelihoods and a Panel of GARCH Models, Statistica Sinica, 21, 307-329.
andrew patton
Patton, A. and Timmermann, A. Predictability of Output Growth and Inzation: A Multi-Horizon Survey Approach, Journal of Business and Economic Statistics, to appear.
Patton, A. 2011. Data-Based Ranking of Realised Volatility Estimators, Journal of Econometrics, 161(2), 284-303.
Patton, A. 2011. Volatility Forecast Comparison using Imperfect Volatility Proxies, Journal of Econometrics, 160(1), 246-256.
Patton, A. and Timmerman, A. 2010. Why do Forecasters Disagree? Lessons from the Term Structure of Cross-Sectional Dispersion, Journal of Monetary Economics, 57(7), 803-820.
Patton, A. and Timmerman, A. 2010. Monotonicity in Asset Returns: New Tests with Applications to the Term Structure, the CAPM and Portfolios Sorts, Journal of Financial Economics, 98(3), 605-625.
stephen roberts
Roberts, S., Yoon, J.W., Dyson, M. and Gan, J. 2011. Bayesian Inference for an Adaptive Ordered Probit Model: an Application to Brain Computer Interfacing, Neural Network, to appear.
Roberts, S., Karastergiou, A., Johnston, S., Lee, H., Weltevrede, P. and Kramer, M. 2011. A Transient Component in the Pulse Profile of PSR J0738-4042, Monthly Notices of the Royal Astronomical Society, to appear.
Roberts, S. and Ebden, M. 2011. Graph Marginalization for Rapid Assignment in Widearea Surveillance, Adhoc Networks Journal 9(2), 180-8, to appear.
Roberts, S., Psorakis, I. and Ebden, M. 2011. Overlapping Community Detection using Bayesian Non-Negative Matrix Factorization, Physical Review E, in press.
Roberts, S., Fox, C. 2011. A Tutorial on Variational Bayesian Inference. Artificial Intelligence Review, Spinger, in press.
Roberts, S. Yoon, J.W. Dyson, M. and Gan, J. 2011. Bayesian Inference for an Adaptive Ordered Probit model: an Application to Brain Computer Interfacing, Neural Networks, in press.
Roberts, S., Karastergiou, A., Johnston, S., Lee, H., Weltevrede, P. and Kramer, M. 2011. A Transient Component in the Pulse Profile of PSR J0738-4042, Monthly Notices of the Royal Astronomical Society, B0736-40.
Roberts, S. and Ebden, M. 2011. Graph Marginalization for Rapid Assignment in Wide-Area Surveillance, Adhoc Networks Journal 9(2), 180-8.
Roberts, S., Reece, S., Nicholson D. and Lloyd, C. 2011. Determining Intent using Hard/Soft Data and Gaussian Process Classifiers, Proceedings of Fusion.
Pickup, L., Capel, D., Roberts, S. and Zisserman, A. 2010. Multiframe Super-Resolution from a Bayesian Perspective, In Super-Resolution Imaging, Chapter 9, CRC Press, 247-284.
Roberts, S. and Reece, S. 2010. The Near Constant Acceleration Gaussian Process Kernel for Tracking, IEEE Signal Processing Letters, 17(8), 707-710.
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Roberts, S., Ebden, M. and Stranjak, A. 2010. Visualizing Uncertainty in Reliability Functions with Application to Aero Engine Overhaul, Journal of the Royal Statistical Society C. Volume 59, part 1, 163-173.
Roberts, S. Garnett, R. Osborne, M. A. Reece, S. and Rogers, A. 2010. Sequential Bayesian Prediction in the Presence of Changepoints and Faults, The Computer Journal, 53(9), 1430-1446.
Roberts, S. and Yoon, J. W. 2010. Robust Measurement Validation in Target Tracking using Geometric Structure, IEEE Signal Processing Letters, 17(5), 493-496.
Roberts, S. and Lee, S. M. 2010. Sequential Dynamic Classification using Latent Variable Models, The Computer Journal, 53, 1415-1429.
Roberts, S., Lowne, D. and Garnett, R. 2010. Sequential Non-Stationary Dynamic Classification with Sparse Feedback, Pattern Recognition, 43, (3)0, March 2010, 897-905.
Psorakis, I., Roberts, S. and Sheldon, B. 2010. Soft Partitioning in Networks via Bayesian Non-Negative Matrix Factorization, Proceedings of NIPS 2010 workshop on community detection.
Reece,S., Mann,R., Rezek. I. and Roberts, S. 2010. Gaussian Process Segmentation of Co-Moving Animals, 30th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering , Chamonix, France.
Kaufman, M. and Roberts, S. 2010. Coordination vs. Information in Multi-Agent Decision Processes, Proceedings of AAMAS 2010.
McInerney, R., Roberts, S. and Rezek, I. 2010. Sequential Bayesian Decision Making for Multi-Armed Bandit, Proceedings of AAMAS 2010.
Roberts, S. and Reece, S. 2010. An Introduction to Gaussian Processes for the Kalman Filter Expert, Proceedings of Fusion 2010.
Roberts, S., Garnett, R. and Osborne, M. A. 2010. Bayesian Optimization for Sensor Set Selection, Proceedings of IPSN 2010, Stockholm.
Roberts, S. and Ebden, M. 2010. Graph Marginalization for Rapid Assignment in Wide-Area Surveillance. International Conference on Ad Hoc Networks, Niagara Falls, Canada, LNICST (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), 28, 691-703.
christoph reisinger
Reisinger, C., Bush, N. Hambly, B.M. Haworth, H. and Jin, L. 2011. Stochastic Evolution Equations in Portfolio Credit Modelling, SIAM Journal on Financial Mathematics, to appear.
Reisinger, C. and Witte, J.H. 2011. A Penalty Method for the Numerical Solution of Hamilton-Jacobi-Bellman (HJB) Equations in Finance, SIAM Journal of Numerical Analysis, 49(1), 213-231.
tarun ramadorai
Ramadorai, T. 2011. Capacity Constraints, Investor Information, and Hedge Fund Returns, Journal of Financial Economics, to appear. Previously entitled “Investor Interest and Hedge Fund Returns.”
Ramadorai, T. 2010. The Secondary Market for Hedge Funds and the Closed Hedge Fund Premium, Journal of Finance, to appear. Internet Appendix.
neil shephard
Shephard, N., Noureldin, D. and Sheppard, K. 2011. Multivariate High-Frequency-Based Volatility (HEAVY) Models, Journal of Applied Econometrics, to appear.
Shephard, N. and Flury, T. 2011.Bayesian Inference Based only on a Simulated Likelihood, Econometric Theory, to appear.
Shephard, N., Barndorff-Nielsen, O. E., Lunde, A. and Hansen, P.R. 2011. Subsampling Realised Kernels, Journal of Econometrics, 160, 204-219.
Shephard, N. Multivariate Realised Kernels: Consistent Positive Semi-Definite Estimators of the Covariation of Equity Prices with Noise and Non-Synchronous Trading, Journal of Econometrics, to appear.
Shephard, N. Nuisance Parameters, Composite Likelihoods and a Panel of GARCH Models, forthcoming Statistica Sinica.
Shephard, N. Bayesian Inference Based only on Simulated Likelihood: Particle Filter.
Shephard, N. 2010. Deferred Fees for Universities, Economic Affairs, 30, (2), 40-44.
Shephard, N. and Barndorff-Nielsen, O.E. 2010. Volatility, in Encyclopedia of Quantitative Finance, edited by Rama Cont, John Wiley and Sons Ltd, Chichester, UK, 1898-1901.
Shephard, N., Barndorff-Nielsen, O. E. and Kinnebrouck, S. 2010. Measuring Downside Risk: Realised Semivariance, in Volatility and Time Series Econometrics: Essays in Honor of Robert F. Engle, edited by T. Bollerslev, J. Russell and M. Watson (eds), Oxford University Press, 117-136.
Shephard, N. 2010. Realising the Future: Forecasting with High Frequency Based Volatility (HEAVY) Models, Journal of Applied Econometrics, 25, 197-231.
torsten schöneborn
Schöneborn, T. and Martin, R. 2011. Mean Reversion Pays, but Costs. RISK, 96-101.
suresh sundaresan
Sundaresan, S., Tonetti, C., Bartolini, L. and Hilton, S. 2011. Collateral Values by Asset Class: Evidence from Primary Securities Dealers, Financ. Stud, (2011) 24(1), 248-278.
Sundaresan, S., Asvanunt, A. and Broadie, M. 2011 Managing Corporate Liquidity: Strategies and Pricing Implications, International Journal of Theoretical and Applied Finance, 14, (3), 369-406.
nithum thain
Thain, N., Mirrokni, V., and Vetta A., 2011. On the Implications of Lookahead Search in Game Playing.
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zhongmin qian
Qian, Z. and Tudor, J. 2011. Differential Structure and Flow Equations on Rough Path Space, arXiv:1102.561, to appear.
Qian, Z. Tudor, J. and Cass, T. 2011. Non-Linear Evolution Equations Driven by Rough Paths, arXiv:0911.0281, to appear.
Qian, Z. and Ying, J. 2011. Martingale Representations for Diffusion Processes and Backward Stochastic Differential Equations, arXiv:0910.4911, to appear in Sem de Probab.
Qian, Z., Zheng, W. and Duan, X.L. 2011. On Local Linear Approximations to Diffusion Processes, to appear in International Journal of Mathematics and Mathematical Sciences.
thaleia zariphopoulou
Zariphopoulou, T. and Sircar, R. 2010.Utility Valuation of Credit Derivatives and Applications to CDOs, Quantitative Finance, 10 195-208.
Zariphopoulou, T., Sokolova, K. and Musiela, M. 2010. Indifference Valuation in Incomplete Binomial Models, Mathematics in Action, 3(2), 1-36.
Zariphopoulou, T. and Musiela, M. 2010. Portfolio Choice Under Space-Time Monotone Performance Criteria, SIAM Journal on Financial Mathematics,1, 326-365.
Zariphopoulou, Z. and Zitkovic, G. 2010. Maturity-Independent Risk Measures, SIAM Journal on Financial Mathematics,1, 266-288.
Zariphopoulou, T. and Musiela, M. 2010. Stochastic Partial Differential Equations and Portfolio Choice, Contemporary Quantitative Finance, Springer-Verlag, 195-215.
Zariphopoulou, T. and Musiela, M. 2010. Initial Investment Choice and Optimal Future Allocations Under Time-Monotone Performance Criteria, International Journal of Theoretical and Applied Finance, 14(1), 61-81.
lan zhang
Zhang, L. 2011. Estimating Covariation: Epps Effect, Microstructure Noise, Journal of Econometrics, 160, 33-47.
Zhang, L., Mykland, P.A., and Ait-Sahalia, Y. 2011. Edgeworth Expansions for Realized Volatility and Related Estimators, Journal of Econometrics, 160, 190-203.
Zhang, L. Mykland, P.A. and Ait-Sahalia, Y. 2011. Ultra High Frequency Volatility Estimation with Dependent Microstructure Noise, Journal of Econometrics, 160, 160-175.
Zhang, L., Kang, Z.X. and Chen, R. 2010. Forecasting Return Volatility in the Presence of Microstructure Noise, Statistics and its Interface, 3 (2), 145-158.
Zhang, L. Implied and Realized Volatility: Empirical Model Selection. 2010. Annals of Finance, to appear.
Zhang, L., and Chen, R. Kang, Z.X. 2010. Forecasting Return Volatility in the Presence of Microstructure Noise, Statistics and Its Interface. 3 (2), 145-158.
Zhang, L. and Mykland, P.A. 2010. The Econometrics of High Frequency Data. Statistical Methods for Stochastic Differential Equations, to appear.
yifei zhong
Dai, M. and Zhong, Y. 2010. Penalty Methods for Continuous-Time Portfolio Selection with Proportional Transaction Costs, Journal of Computational Finance, 13(3), 1-31.
Dai, M. and Zhong, Y. 2010. Optimal Stock Selling/Buying Strategy with Reference to the Ultimate Average, Mathematical Finance, to appear.
Dai, M, Jin, H., Zhong, Y. and Zhou, X. 2010. Buy Low and Sell High, Contemporary Quantitative Finance: Essays in Honour of Eckhard Platen, Springer, 317-334.
Dai, M, Zhong, Y. and Kwok, Y.K. 2011. Optimal Arbitrage Strategies on Stock Index Futures under Position Limits. Journal of Futures Markets, 31, 394-406.
Bian, B., Dai, M., Jiang, L., Zhang, J. and Zhong, Y. 2011. Optimal Decision for Selling an Illiquid Stock, Journal of Optimization Theory and Application, to appear.
xunyu zhou
Zhou, X. and Jin, H. 2011. Greed, Leverage, and Potential Losses: A Prospect Theory Perspective, to appear in Mathematical Finance.
Zhou, X., Meyer-Brandis, T. and Øksendal, B. 2011. A Mean-Field Stochastic Maximum Principle via Malliavin Calculus, Stochastics, (A Special Issue for Mark Davis’ Festschrift), to appear.
Zhou, X. and He, X. 2011. Portfolio Choice Under Cumulative Prospect Theory: An Analytical Treatment, Management Science, 57, 315-331.
Zhou, X. and He, X. 2011. Portfolio Choice via Quantiles, Mathematical Finance, 21 (2011), 203-231.
Zhou, X. Jin, H. and Zhang, S. 2011. Behavioral Portfolio Selection with Loss Control, Acta Mathematica Sinica, 27, 255-274. (A Special Issue Dedicated to Loo-Keng Hua on his 100th Birthday).
Zhou, X. and Chiu, C. 2011. The Premium of Dynamic Trading, Quantitative Finance, 11, 115-123.
Zhou, X. 2010. Mathematicalising Behavioural Finance, Proceedings of the International Congress of Mathematicians, Hyderabad, India.
Zhou, X., Dai, M. , Jin, H. and Zhong, Y. 2010. Buy Low and Sell High, Contemporary Quantitative Finance, Edited by Carl Chiarella and Alexander Novikov, Springer, 317-334. (Essays in Honour of Eckhard Platen).
Zhou, X. and Ji, S. 2010. A Generalized Neyman-Pearson Lemma for G-Probabilities. Probability Theory and Related Fields, 148, 645-669.
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In October 2010, the company acquired GLG Partners to create
a broader mix of fund strategies and products for investors. The
combined firm now has expertise in a wide range of investment
styles including managed futures, equity, credit, emerging
markets, global macro and multi-manager – all of which share a
relentless focus on investment performance.
Since the acquisition, new offerings combining the strengths of
the two businesses have been developed. In February 2011, the
company announced the launch of Man IP 220 GLG, a structured
product which offers investors access to a combination of Man’s
flagship managed futures manager, AHL, and a broad range of
GLG’s discretionary strategies for the first time.
Then in June, Man launched the Man GLG Multi-Strategy fund
– a unique combined fund which offers investors access to a
range of Man and GLG funds which comply with European
UCITS regulations. This latest fund launch was a particular
success attracting 100 million in commitments.
Japan fund launch
Perhaps the most significant fund launch in the last year,
however, was the successful launch of an AHL open-ended fund
in Japan called Nomura Global Trend, which raised an initial
US$1.5 billion. The fund began trading at the end of April 2011.
Alongside this growth in new funds and products, Man has
also been investing in its people, technology and operations
underpinning trading. In May, Man’s trend following CTA
business, AHL, became the first CTA to create a standalone
trading team in Hong Kong. AHL staff in Oxford can see this
team on large LCD screens via a video link, and the Hong Kong
trading team have a similar view of the Oxford team, binding
together research and trading teams across the globe.
New hq opens
In July, Man also unveiled new company headquarters in Swan
Lane in the City of London. The new £250m development on
the banks of the Thames houses 957 employees and includes the
latest trading and communications technology designed to enable
excellence in research, communication and trade execution.
Through this pursuit of excellence and its continued focus on
performance, Man is strongly positioned for the future, and
the commitment to the Oxford-Man Institute of Quantitative
Finance remains key.
UNIQUE
The last year has been one of transformation for man Group, a world-leading alternative investment management business, and collaborative partner for omi.