Frank Musella Senior Thesis: The Impact of High-Frequency Trading Regulatory Regimes on European...
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THE IMPACT OF HIGH-FREQUENCY
TRADING REGULATORY REGIMES
ON EUROPEAN MARKET QUALITY
BY
Francis Joseph Musella
Submitted to Princeton University
Department of Economics
In Partial Fulfillment of the Requirements for the A.B. Degree
April 15, 2014
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Abstract
I examine the impact of three different national regimes for regulating high-frequency
trading: a licensure regime in Germany, establishment of an HFT order cancellation tax
in Italy, and a combined order cancellation tax and general financial transactions tax in
France. Using GARCH and EGARCH models, I find that the German regime
significantly reduces the persistence of volatility shocks. The French regime significantly
reduces long-run volatility, reduces the size of bid-ask spreads, and increases intraday
volatility. It also weakly reduces volatility persistence and the sensitivity of bid-ask
spreads to volatility. The Italian regime significantly reduces long-run volatility,
increases the persistence of volatility shocks, increases intraday volatility, and reduces
the sensitivity of bid-ask spreads to volatility. It weakly increases the size of bid-ask
spreads. The French and German regimes were associated with a significant reduction in
trade volume, which was not the case with the Italian regime. Overall, I find that the three
regimes improve market quality more often than they detract from it.
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Acknowledgements
I want to thank my advisor, Professor Stephen Redding, for helping shape my
ideas into a concrete thesis. My JP advisor, Professor Valentin Haddad, taught me to take
a rigorous approach to econometrics, and I owe much of my knowledge of ARCH models
and demonstrating exogeneity to his teachings. Professor Harrison Hong gave me my
first exposure to theoretical finance, which proved intensely useful when combing the
existing literature. I also want to thank Professor Hank Farber, who inspired my passion
for econometrics while giving me the tools to pursue significant original research.
Additionally, Bobray Bordelon and Todd Hines proved invaluable in helping me gather
data for this thesis, which would have been an impossible undertaking otherwise.
My experience as a Princeton senior would not have been the same without the
support of my friends. I owe thanks to a wide variety of people. To Tierney Kuhn, my
intellectual partner in crime, who inspired me to push through every obstacle life could
throw my way. To Bryton Shang, whose experience in High-Frequency Trading at
Eladian Partners helped inspire this thesis. To Anthony Paranzino, whose endless games
of billiards kept me sane in the face of the abyss. To James di Palma-Grisi, who reviewed
a draft of my thesis over WaWa hoagies and coffee. To Christiana Lloyd-Kirk, whose
keen eye for the English language helped me extract meaning from the complex. And to
all the members of Colonial Club, who made the experience of writing my thesis far less
painful than imagined.
Finally, I owe the ultimate debt of gratitude to my parents, Marianne Musella and
Joseph Whittick, for giving me their love, support, and gametes. They have given me a
joyous life for the past twenty-two years, and I wouldnt be here without them.
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Contents
Abstract i
Acknowledgements ii
1. Introduction 1
2. Literature review: Benefits of HFT 4
2.1 Volatility reduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.2 Improved price discovery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.3 Liquidity improvements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
3. Literature review: Drawbacks of HFT 8
3.1 Volatility increases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
3.2 Quote stuffing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
3.3 Risk events . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
4. Existing and proposed regulatory mechanisms 13
4.1 Transactions tax . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
4.1.1 2012 French implementation . . . . . . . . . . . . . . . . . . . . . . . . . . 14
4.1.2 2013 Italian implementation . . . . . . . . . . . . . . . . . . . . . . . . . . 15
4.1.3 Planned 2015 Eurozone FTT . . . . . . . . . . . . . . . . . . . . . . . . . . 16
4.2 Licensure regime . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
4.2.1 German HFT Act of 2013 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
4.3 Price limits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
4.4 Trading halts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
4.5 Maximum order-to-trade ratios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
4.6 Proposed novel ideas yet to be implemented . . . . . . . . . . . . . . . . . . . . . 20
4.6.1 Minimum order durations/holding periods . . . . . . . . . . . . . . . 20
4.6.2 Quoting obligations for market makers . . . . . . . . . . . . . . . . . . 20
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5. Methodology 21
5.1 Impact on daily trade volume . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
5.2 Impact on long-run volatility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
5.3 Models of intraday volatility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
5.4 Models of liquidity provision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
5.5 Potential threats to validity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
6. Data 31
7. Hypothesis 33
8. Results and analysis 34
8.1 Change in trade volume . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
8.2 Change in long-run volatility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
8.3 Change in intraday volatility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
8.4 Change in liquidity provision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
9. Conclusion 42
References 46
Appendix 1: Results of EGARCH model regressions 51
Appendix 2: Results of GARCH model regressions 53
Appendix 3: Results of AR(1) model regressions 55
Appendix 4: A day in the life of duopolistic market-making HFTs 56
Pledge 59
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1. Introduction
High-Frequency Trading (HFT) is a form of algorithmic trading in which orders are
rapidly placed, modified, or cancelled in accordance with market changes occurring at the
millisecond or microsecond level. HFT has seen widespread adoption in the early twenty-
first century. While HFT trade volumes were minimal at the start of the century, by 2008,
HFT accounted for a majority of all daily trading volume in the United States. Though
HFT volumes peaked in 2009, HFT still comprises roughly half of daily trading volume
in the US, and 30-40% of trading volume in Europe and Canada.1
Multiple different trading strategies can be classified as HFT. The first strategy,
market making, is as old as financial markets themselves. A trader simultaneously places
offers at the best bid and ask price, updating the offers several times per second. The
trader effectively sells liquidity: he takes on inventory risk and is compensated by
capturing the bid-ask spread. Depending on exchange policies, the trader may also
receive a small rebate from the exchange for providing liquidity via limit orders.
Another major HFT strategy is arbitrage between exchanges. Arbitrage consists of
strategies designed to profit from different asset prices across different exchanges. This
might consist of buying assets on exchanges where the price is low and reselling those
assets on exchanges where the price is high. Arbitrage can also take advantage of the
expected relationship between two related assets: buying an Exchange Traded Fund while
shorting its components would produce risk-free profits if the ETF traded at a discount.
These price disparities tend to be short-lived, meaning that HFTs must have access to the
1 Kumar et al, 2011, p. 2
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fastest available data feeds to profit from them. Arbitrage strategies help ensure the
convergence of prices across different exchanges.
More sophisticated HFTs may try to predict price movement, instead of making
money on spreads or arbitrage. The simplest price prediction strategy is event-based
trading. HFTs will attempt to be the first to trade after important economic events for
example, the release of the monthly unemployment report. Since economic events drive
prices, there are tremendous profits to be made by being the first to trade. Event-driven
strategies gained attention in September 2013, when an unknown trader placed more than
a billion dollars of buy orders on a major gold ETF, coinciding with the release of the
Federal Reserves minutes. The order was placed at exactly 2 PM, suggesting that the
report was leaked early: it would take 7 milliseconds for information to travel at the
speed of light from Washington, D.C. to Chicago.2 As long as event-driven strategies are
profitable, there will be incentives to leak information and commit insider trading.
Another strategy to predict price movements is analyzing order book depth. A
relative abundance of sell orders may imply that prices will move down, and a flood of
buy offers suggests that prices will move up. This is a form of momentum trading, since
it relies solely on past price movements to predict future price movements. However,
strategies rely on order book depth are vulnerable to exploitation by other HFTs. The
practice of spoofing involves placing phantom orders away from the best bid and offer
to create artificial depth in the order book. The phantom orders encourage other traders to
place real orders on the other side of the book, which the abusive HFT can trade against
at a profit. The abuses are considered serious enough that the Securities and Exchange
2 Foxman, 2013
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Commission has begun acting to shut down trading shops that engage in spoofing, most
recently Visionary Trading in 2014.3
By far the most controversial form of HFT consists of ultra-low latency trading.
Ultra-low latency strategies depend on accessing data faster than other market
participants. This might involve colocation, where traders place their computers in the
same data centers as the exchanges computers to reduce communication time to
practically zero. Low latency strategies also involve the use of proprietary
communication technology, such as private fiber optic cables or microwave
communication networks. These traders generally spend significant amounts of money on
direct data feeds, instead of relying on the slower Securities Information Processor (SIP)
feed, which is publicly available. The difference in speed between the two data feeds
about 25 milliseconds creates frequent price dislocations that HFTs can trade on and
consistently profit from.
The two-tiered structure of information is troubling from an efficiency standpoint.
In order to be competitive, firms must spend millions of dollars on data and
infrastructure, raising significant barriers to entry for smaller firms. In Flash Boys,
Michael Lewis relates the story of Spread Networks, a company that charged firms
upwards of $10 million for access to a single fiber optic cable connecting New York and
Chicago. Due to the winner-take-all nature of HFT, firms were forced to buy access to
the line, which was the fastest in existence at the time. By reducing the number of firms
that can afford to be competitive, proprietary data feeds and communication networks
may harm market efficiency. After all, why would Low-Frequency Trading firms with
imperfect information attempt to compete against HFT firms with perfect information?
3 Lynch, 2014
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In addition to efficiency concerns, it is possible that ultra-low latency strategies
violate existing US securities law. At issue is whether colocation and high-speed data
feeds give HFT firms selective access to material nonpublic information. If so, these
practices would be considered insider trading. In 2013, the FBI, SEC, and New York
Attorney Generals office launched a joint investigation into the practice of selling direct
feeds to firms engaging in HFT.4 This investigation has the potential to dramatically alter
the trading landscape in the United States.
Designing an optimal HFT regulatory policy is challenging. An ideal policy must
mitigate the harms of HFT while preserving any social benefits HFT provides. This thesis
will examine the effectiveness of HFT regulatory regimes recently imposed in three
countries: Germany, Italy, and France. Policy efficacy will be measured by the
preservation or improvement of market quality, specifically volatility and bid-ask
spreads. I will attempt to answer two research questions. First, what effect did the
regulations have on market quality? Second, what is the optimal HFT regulatory policy?
2. Literature review: Benefits of HFT
2.1 Volatility reduction
Several studies have found that HFT activity reduces intraday volatility. Hasbrouck and
Saar examine quotes at the millisecond level, using strategic runs, or sequences of
consecutive order modifications, as a proxy for HFT activity. They find that increased
HFT activity is associated with a decrease in short-term volatility. Of course, the
4 Geiger & Mamudi, 2014
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direction of causality is uncertain: it might be the case that volatility chases HFTs away
instead of HFTs reducing volatility with their presence. Hasbrouck and Saar acknowledge
this problem and attempt to address it by using trading activity on other exchanges as
instrumental variables. Their results are robust to their IV specification, though they
hinge on the validity of their instruments.
Brogaard employs a different methodology, which more closely matches the
methodology of my own paper. He conducts an event study, examining the impact of the
SECs temporary ban on short sales imposed immediately after the collapse of Lehman
Brothers. Using a policy change helps eliminate the problem of endogeneity, because the
change in HFT activity during the sample period can be attributed to the short sale ban
and not to a rise in volatility. Brogaard finds that decreases in HFT activity caused
increases in short-run volatility, though the effect was only significant at the 10% level
due to a small sample size.
Brogaard also tests the volatility hypothesis by examining a counterfactual: how
would market quality change if there were no HFTs? He constructs a hypothetical price
path by removing all trades in which HFTs participated, then compares the volatility of
prices on the real price path to the volatility on the hypothetical one using one-minute
intervals. He finds that the HFT price path has significantly less volatility than the non-
HFT price path. However, the size of the effect was less than 1% of total volatility.
Furthermore, when examining the volatility of individual stocks instead of the aggregate
market, only one of the 120 analyzed stocks saw a significant decrease in volatility
attributable to the presence of HFTs.5
5 Brogaard, 2010, p. 75
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2.2 Improved price discovery
Hendershott and Riordan study the impact of Algorithmic Trading (AT) on price
discovery6, using data from the 30 DAX stocks trading on the Deutsche Borse during
January 2008. They find that AT initiated trades have a more than 20% larger permanent
price impact than human trades. In other words, AT is more likely to lead to successful
price discovery than human-generated trades. However, their dataset does not distinguish
between high-frequency and low-frequency AT strategies. It may be the case that low-
frequency strategies, such as an institutional investor placing a large order, contribute
more to price discovery than high-frequency or market-making strategies.
Brogaard replicates the results of Hendershott and Riordan with high-frequency
data taken from the NASDAQ during 2008-2010. Using a Vector Autoregression (VAR)
model, Brogaard finds that an innovation in HFT tends to lead to a 34% greater
permanent price change than does a trade by a non-HFT.7 This effect is present in both
short-run prices and long-run prices. Unlike Hendershott and Riordan, Brogaards dataset
includes information about whether orders were placed by HFTs, which increases the
validity of his results.
Aitken, Cumming, and Zhan examine the impact of HFT on end-of-day price
dislocations. There exist strong financial incentives to manipulate the closing price on
certain days because closing prices are used at the option expiry dates to determine the
value of options, and can strongly influence portfolio allocations at the end of each fiscal
quarter. The question is whether HFTs reduce or increase these dislocations. The authors
find that the presence of HFTs reduces the probability of an EOD price dislocation by
6 Roughly speaking, price discovery is the ability of traders to incorporate new information in asset pricing.
7 Ibid, p. 33
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about 21%. To address possible concerns about endogeneity, the authors use press
releases announcing new colocation agreements as a proxy for HFT activity.
2.3 Liquidity improvements
Hasbrouck and Saar examine the impact of HFT on two measures of liquidity: bid-ask
spreads and order book depth. They find that an increase in HFT activity significantly
reduces bid-ask spreads while increasing order book depth, both of which can be
considered improvements in liquidity and market quality. As in their analysis of
volatility, the effect is only significant over the aggregate market, and not for any
individual stocks.
Hendershott, Jones, and Menkveld analyze the impact of Algorithmic Trading on
bid-ask spreads on the New York Stock Exchange. They use the 2003 introduction of
automated quote dissemination (autoquote) as an exogenous instrument that should
increase AT. They find a significant negative relationship between AT volumes and bid-
ask spreads, suggesting that ATs increase the provision of liquidity.
Hendershott and Riordan examine the quoting behavior of ATs versus human
traders on the Deutsche Borse. Although algorithmic and human traders each supply 50%
of liquidity in realized trades, they find that ATs are significantly more likely than
humans to quote at the best bid and ask prices. The effect size is roughly one additional
hour per day with the best offer. However, the authors do not attribute this difference to
improved liquidity provision. Instead, they cite two factors relating to the nature of ATs.
First, ATs tend to place much smaller offers than human traders, due to inventory
aversion. Second, human quotes are more likely to be stale and adversely selected
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against. The quoting disparity is particularly strong when spreads are high, suggesting
that ATs only supply liquidity when it is expensive.
At-Sahalia and Saglam develop a theoretical model of HFTs. In the model, an
HFT has a modified mean-variance utility function, with his profit increasing in the
expected bid-ask spread and decreasing in his inventory and inventory aversion. Latency
is modeled with a Poisson process: the trader has a constant probability of receiving a
signal about the future direction of prices, and quotes based on his signal. As latency
decreases, the profitability of quoting increases, as does the fill rate of the LFTs market
orders. Speed thus increases the provision of liquidity by reducing risk aversion in market
makers.
3. Literature review: Drawbacks of HFT
Although many authors have written about the successes of HFT, their findings are not
unanimous. Several studies find that HFT actually increases market volatility, and other
authors allege that HFTs withdraw liquidity in times of high volatility, thereby increasing
bid-ask spreads and exacerbating market crashes. The latter is a particularly strong
concern, since sound markets require robustness to negative shocks.
3.1 Volatility increases
Zhang studies the impact of HFTs on long-run volatility. Using the same methodology as
Hendershott, Jones, and Menkveld, Zhang uses the 2003 introduction of autoquote on the
NYSE as an exogenous instrument. He studies all equities covered by the Thomsen
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Reuters Institutional Holdings database over the period from 1985 to 2009. Zhang finds
that volatility significantly increases as the market share of HFTs increases. He also
analyzes the price discovery process by using analyst forecasts and earnings surprises,
finding that prices are significantly more likely to overreact to news when HFT is
widespread. He attributes this result to the interplay between two different types of HFTs:
event-driven and momentum-driven investors. Event-driven HFTs respond first to new
information. Momentum traders, who disregard fundamentals, then respond to the price
change generated by the first HFT group, magnifying the original price movement. This
implies that new information is effectively double-counted, as both types of traders
respond to it for different reasons.
Jarrow and Protter develop a theoretical model of latency arbitraging HFTs. The
model is highly stylized and uses several strong assumptions such as continuous time,
zero bid-ask spreads, and zero-latency trading. Nevertheless, they find that the
introduction of high-frequency trades both increases market volatility and generates
abnormal profit opportunities for the high-frequency traders at the expense of the
ordinary traders.8 These abnormal profits may represent a pure arbitrage, which would
contradict the efficient markets hypothesis. The authors describe latency arbitrage as a
transfer of wealth from mutual funds, pension funds, and other financial institutions, to
the firm doing high-frequency trading.9 It is unclear whether their results would hold if
the model were generalized to include bid-ask spreads or imperfect liquidity, or if a
similar general equilibrium model were used.
8 Jarrow & Protter, 2012, p. 14
9 Ibid, p. 5
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3.2 Quote stuffing
Egginton, Van Ness, and Van Ness examine the practice of quote stuffing, which
occurs when HFTs submit excessive orders with the goal of causing latency to confound
competitors algorithms. Using data from the NYSEs Trade and Quote database, they
define an episode of quote stuffing as a one-minute period that includes quoting activity
exceeding the mean level of activity over the trailing 20 days by at least 20 standard
deviations. After accounting for major events like corporate earnings releases, they find
that 24,733 instances of quote stuffing occurred during 2010.10
They find that these
episodes of quote stuffing caused decreased liquidity, increased volatility, and increased
trading costs. Once again, however, there is the problem of identifying causality: HFTs
might initiate quote stuffing as a response to changes in market volatility and liquidity,
instead of quote stuffing causing those changes. The results would also be less robust if
the authors failed to remove every instance of news-driven quote stuffing.
3.3 R isk events
Another common criticism is that HFT is a strategy that works until it doesnt. In other
words, while HFT may improve market quality under normal conditions, it will
occasionally create or exacerbate major risk events. The classic example of such an event
is the May 6, 2010 Flash Crash. Over the course of 15 minutes, the Dow Jones and
S&P 500 both lost and recovered over 5% of their value, with several stocks briefly
trading for one cent per share.11
There was no apparent fundamental cause for the sudden
10
Egginton, Van Ness, and Van Ness, 2012, p. 12 11
Trades against these one-cent stub quotes were generally broken after the fact.
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drop. A joint report by the SEC and Commodities Futures Trading Commission (CFTC)
attempted to uncover the trigger that started the decline. They find that at 2:32 PM, the
beginning of the crash, the hedge fund Waddell & Reed placed a $4.1 billion sell order on
the S&P 500 mini contract. What followed was a complex interplay between HFTs
employing different strategies, each of which contributed to the precipitous price
declines.
The sudden influx of supply in the S&P mini contract overwhelmed market
making HFTs, who sought to quickly unload the positions they had acquired. While
HFTs traded over 1 million contracts on the day of the Flash Crash, they never held net
positions of greater than 3000 contracts long or short, due to inventory aversion.12
When
market makers hit their inventory constraint, they began selling off their contracts to
other HFTs. At this point, momentum-driven HFTs sensed a preponderance of selling
activity, and began to short the contract, driving the price down even further. Of course,
for every seller there must be a buyer. On May 6, the buyers were arbitrage-driven HFTs,
who bought the S&P mini contract while shorting the underlying stocks. While
arbitrageurs generally improve market efficiency, this time they had the effect of
spreading the original mispricing like wildfire, creating a perfect storm.
Compounding the issue was the general withdrawal of liquidity from the market.
The SEC interviewed employees at over 30 HFT firms in the wake of the crash. Many of
them noted that their algorithms had built-in data-integrity pauses, which means the
HFT algorithms were designed to stop trading when large price movements occurred, due
to concerns over potentially erroneous price data.13
These trading halts resulted in the
12
Kirilenko et al, 2011, p. 3 13
SEC, 2011, p. 35
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withdrawal of more than 80% of liquidity in stocks, and more than 90% of liquidity in
ETFs.14
Since HFTs tend to trade in the most liquid assets possible, this drop in liquidity
significantly affected the 100 largest stocks by market capitalization.
The withdrawal of liquidity caused trades to occur far outside of the range of
sanity. Exchanges often require registered market makers to continuously quote on both
sides of the market. Instead of completely discontinuing their quoting practices, these
HFTs converted their quotes to stub quotes quotes so far from the market price that
they are never expected to be executed. These quotes were generally one cent for bid
offers, and $100,000 for ask offers.15
Due to the extreme withdrawal of liquidity, in a few
cases, these stub quotes were the only active quotes for a stock. When incoming market
orders hit the stub quotes, trades were executed. As a result of the Flash Crash, the SEC
broke more than 20,000 trades, and later formally banned the practice of stub quoting.
Although not rising to the level of the Flash Crash, the August 1, 2012 meltdown
of the HFT firm Knight Capital had the potential to create systemic contagion.
Immediately after the start of trading, a faulty algorithm caused Knight to purchase
billions of dollars worth of unwanted positions. The error caused dramatic mispricings in
over 140 equities; one company, Wizzard Software Corp, saw prices shoot from $3.50 to
over $14.70.16
While some of the trades were broken, Knight ultimately lost roughly
$440 million after unwinding its positions. The incident led Knight to be acquired by
Getco to remain in business. To add insult to injury, the SEC fined Knight $12 million for
violating its standards for firms with direct market access standards that were
14
Ibid, p. 40 15
Ibid, p. 63 16
Valetkevitch & Mikolajczak, 2012
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implemented as a result of the 2010 Flash Crash.17
Clearly, these examples demonstrate
that HFTs can create or exacerbate extreme market conditions, and should be regulated in
a responsible manner.
4. Existing and proposed regulatory mechanisms
4.1 Transactions tax
The simplest possible way to regulate trades by HFTs would be to tax them. This tax
could take the form of a general Financial Transactions Tax (FTT), sometimes referred to
as a Tobin tax after the economist James Tobin. The tax would consist of a small levy,
generally in the range of 0.1%-1%, on the purchase of stocks or other securities. A
general FTT would impact all securities traders, not just HFTs. FTTs are highly
controversial, and a comprehensive literature review would span hundreds of pages.
One of the most famous advocates of an FTT is Lawrence Summers, former
Secretary of the Treasury. He and his wife authored a paper advocating for a limited FTT.
They found that an increase in stock speculation was driving a corresponding increase in
market volatility, and that an FTT would throw sand into the gears of the market
structure that facilitated that speculation.18
They cite the examples of Britain and Japan,
two nations that successfully implemented their own FTTs. Though the paper does not
specifically address HFT, which did not exist at the time, it can be seen as a response to
program trading the early form of algorithmic trading which was heavily blamed for
the market crash in 1987.
17
Stevenson, 2013 18
Summers & Summers, 1989, p. 1
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At-Sahalia and Saglam model the Tobin tax as an exogenous shock to bid-ask
spreads. They find that a FTT would make HFTs less likely to supply liquidity,
particularly in times of low volatility. However, their analysis primarily applies to low-
latency arbitrageurs, and would likely not apply to market makers, who are often
exempted from the tax in practice.
A more novel and more targeted concept is a tax specifically applying to order
cancellations or modifications. Such a tax would dramatically reduce the incidence of
quote stuffing and latency arbitraging, while leaving market makers relatively unscathed.
At-Sahalia and Saglam examine the theoretical impact of an order cancellation tax, and
find it much less disruptive than a general FTT. In fact, they find that the HFTs optimal
response to an order cancellation tax is to increase his inventory limits, leaving him more
likely to quote and leaving market orders more likely to be filled.
4.1.1 2012 French implementation
In August 2012, France became the first country in the world to specifically tax high-
frequency trading. The regulations called for a 0.01% tax on the value of orders cancelled
or modified within 0.5 seconds of being placed. It also stipulates a maximum order
cancellation/modification rate of 80%; trades modified or cancelled below this threshold
are not subject to the tax. The regulations only apply to principal traders, and not to
brokers acting on behalf of their clients.19
The law also creates an exemption for market-
making activities.
19
France, FTT and HFT, 2013
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In addition to the HFT tax, France implemented a general tax on financial
transactions. The tax was set at the rate of 0.2% of the value of the transaction. The tax
only applies to securities of companies with market capitalizations over 1 billion.
Interestingly, the FTT only applies to traditional traders, not high-frequency traders.
Euroclear, the company responsible for settling transactions and assessing the tax, only
settles transactions at the end of the day. If a trader maintains zero net inventory at
market open and close, as the majority of HFTs do, then no general FTT can be assessed.
In 2012, the French government raised 199 million from the FTT, far less than the 530
million they had anticipated.20
4.1.2 2013 Italian implementation
In 2013, Italy followed Frances lead by implementing its own FTT and HFT tax. The
provisions of the Italian version are somewhat stricter than those for the French version.
The Italian regime calls for a 0.02% tax on order modifications or cancellations taking
place within 0.5 seconds of the original order. The general FTT was assessed at the rate
of 0.22% until January 1, 2014, when it was lowered to 0.2%. Exemptions were made for
designated market makers and securities of companies with a market capitalization under
500 million.21
Unlike the French implementation, the Italian regime was introduced in two steps.
Italy first implemented the FTT on March 1, 2013, and then introduced the HFT tax on
September 2, 2013. My analysis focuses on the second phase, since this more directly
impacts HFT.
20
Bisserbe, 2013 21
Salvadori di Wiesenhoff & Egori, 2013
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16
4.1.3 Planned 2015 Eurozone FTT
A group of 11 EU nations, led by France and Germany, are attempting to implement a
general financial transactions tax across Europe. The original proposal to cover the entire
European Union was defeated, but the nations have agreed to implement the policy
multilaterally through the process of enhanced cooperation. Similar to the notion of an
interstate compact in the United States, enhanced cooperation states that the FTT will
only become law upon the unanimous agreement of all 11 interested countries.
The most recent draft of the proposal calls for a 0.1% tax on equity and bond
transactions and a 0.01% tax on derivatives. There is no specific tax implemented on
high-frequency transactions or cancellations. Several points of contention, including the
lack of exemption for pension investors and the questionable legality of collecting the tax
from non-EU citizens, have prevented the speedy implementation of the proposal. While
the European FTT was scheduled to take effect on January 1, 2014, it now appears that it
will be implemented in 2015 at the earliest.22
4.2 Licensure regime
Another proposed means of regulating HFTs is through a licensure regime. Instead of
leaving market entry relatively unregulated, this regime would require firms to seek
government approval before engaging in HFT. A licensure regime would have the
advantage of being able to distinguish between beneficial and harmful HFT algorithms
as long as the regulators in charge of licensure are competent enough to understand the
difference. The main drawback of a licensure regime is the difficulty of implementing it,
22
Fairless, 2013
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17
including defining terms, hiring experts, and analyzing firm strategies. By raising barriers
to market entry, licensure may also increase the risk of market oligopolies.
4.2.1 German HFT Act of 2013
On May 15, 2013, Germany implemented a series of reforms designed to regulate high-
frequency trading. The core reform was a requirement for firms engaging in HFT to
obtain a license. The act defines HFTs as proprietary traders who access markets by a
high frequency algorithmic trading technique characterized by the use of infrastructure
intended to minimize latencies, by automated order initiation, generating, routing or
execution without human intervention for individual trades or orders and by a high
volume of intraday messages which constitute orders, quotes or cancellations.23
Licensure in Germany requires a capital base of at least 730,000. The German
government can compel firms to disclose their algorithms, and suspend trading in the
event of market abuse. Market abuse is defined as the placing of orders without a
trading intention, but (a) to disrupt or delay the functioning of the trading system, (b) to
make it more difficult for a third party to identify genuine purchase or sale orders in the
trading system, or (c) to create a false or misleading signal about the supply of or demand
for a financial instrument.24 This provision is intended to prevent phantom quoting from
occurring. The exchange may also fine people for excessive use, or for exceeding a fixed
order-to-trade ratio. Unlike the other regimes, the German regime allows this ratio to
differ between different equities.
23
Schuster & Dreibus, 2013, p. 2 24
Ibid, p. 4
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18
4.3 Price limits
One potential remedy for systemic risk events like the 2010 Flash Crash is the notion of
price limits. Price limits would prevent market participants from quoting more than a
given percentage away from the previous days closing price. Kim, Liu, and Yang
analyze the effectiveness of the price limit regime imposed by the Shenzhen Stock
Exchange in December 1996. The exchange imposed a 10% price limit for normal
stocks, and a 5% price limit for Special Treatment (i.e. underperforming) stocks.
Using a single-difference methodology, the authors find a significant reduction in
transitory volatility under the price limit regime. Price limits also helped volatility return
to normal levels more quickly after volatility shocks.
The United States implements a de facto price limit regime. In the wake of the
2010 Flash Crash, the SEC imposed a blanket ban on stub quotes. Market makers must
place limit orders within 8% of the National Best Bid and Offer (NBBO). The exchanges
and FINRA also make it a policy to break clearly erroneous trade executions.
Depending on several factors including stock price and the presence of circuit breakers,
trades may be broken if they occur anywhere from 3% to 30% away from a stocks
reference price.25
4.4 Trading halts
Many exchanges, including all of the major exchanges in the United States, impose
mandatory trading halts (Circuit Breakers) on stocks after large price movements. Circuit
breakers were instituted in the wake of the October 1987 stock market crash. The original
25
For a more comprehensive summary, see SEC, 2010, p. 7
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19
regime called for all trading to be halted for 15 minutes after a 10% or 20% decline in the
DJIA, and for trading to end for the day after a 30% decline in the index. In 2013, the
system received a dramatic overhaul. Under the current system, known as Limit
Up/Limit Down (LULD), trading may be halted for five minutes if the price of a stock
moves up or down by more than 5% in five minutes. This system would have stopped the
vast majority of erroneous trades that took place on May 6, 2010. Unfortunately, single-
stock circuit breakers were not yet in effect, and exchanges only broke trades occurring
more than 60% away from the reference price.
Santoni and Liu analyze the effectiveness of the original circuit breakers
implemented in the wake of the 1987 crash. Similar to my own methodology, they
construct a GARCH model of volatility in the S&P 500 from 1962 to 1991. They find no
evidence that the circuit breakers moderated long-run volatility, both on normal days and
on days with 50+ point moves in the index.
4.5 M aximum order-to-trade ratios
Yet another idea to regulate HFTs is to impose a cap on a firms order-to-trade ratio. In
2012, the Borsa Italiana implemented such a restriction. Firms placing more than 100
orders for every trade were subjected to a tax of up to 1000 per day. Friedrich and Payne
analyze the effects of the order-to-trade ratio (OTR), using a difference-in-difference
methodology matching my own. They find that bid-ask spreads in Italy significantly
increased after the implementation of the OTR. They also find a negative impact on order
book depths. They conclude that an OTR fails to distinguish between efficiency-
generating HFTs and rent-seeking HFTs, harming liquidity in the process. However, their
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20
results are somewhat weakened by the fact that spreads rose on competing Italian
exchanges that did not implement an OTR at the same time.
4.6 Proposed novel ideas yet to be implemented
4.6.1 M inimum order durations/holding periods
One potential method of curbing abusive HFT practices would be to require traders to
leave their orders unmodified for a minimum length of time usually on the order of one
second or less. These minimum order durations would effectively ban the practices of
quote stuffing and phantom quoting. At-Sahalia and Saglam address the idea of
minimum order durations in their theoretical paper on HFT. They find that imposing a
minimum time limit before allowing order cancellation would have two key benefits.
First, it would improve liquidity by increasing the probability of the HFT quoting at any
given time. Second, it would eliminate the HFTs sensitivity to volatility. Instead of
withdrawing liquidity when the market needs it most, the HFT would continue quoting at
normal levels. A speed bump of as little as 20 milliseconds could achieve this desirable
impact.26
4.6.2 Quoting obligations for market makers
Another proposal to mitigate risk events is to impose quoting obligations on HFT market
makers. In a speech before the Economic Club of New York, SEC Chairwoman Mary
Schapiro questioned whether the firms that effectively act as market makers during
normal times should have any obligation to support the market in reasonable ways in
26
At-Sahalia and Saglam, 2013, p. 44
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21
tough times.27 Forcing market makers to quote during the Flash Crash might have
alleviated the problems caused by the withdrawal of liquidity. Schapiro also implied that
HFTs should be banned from short-selling during periods of crisis, analogous to the
uptick rule which prevented firms from placing short positions on equities with falling
prices until 2007.
However, Rijper, Sprenkeler, and Kip argue that quoting obligations would be
unreasonably strict. Noting the extreme financial stress these obligations would cause
market makers during times of crisis, they claim that No company can simply be asked
to commit suicide voluntarily. They point out that existing quoting obligations failed to
stop market crashes in 1987, 1998, and 2007, and that no existing obligations require
designated market makers to quote 100% of the time.
5. M ethodology
My analysis will attempt to determine the impact of recent HFT regulations on several
different measures of market quality. A significant portion of this analysis focuses on
volatility. I measure the impact of regulations on both intraday and long-run volatility, as
well as the persistence of volatility shocks. I also measure how regulation affected
liquidity by examining the change in bid-ask spreads. Finally, I examine the relationship
between volatility and bid-ask spreads.
27
Schapiro, 2010
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22
5.1 Impact on daily trade volume
Before beginning my analysis of market quality, I must determine whether the European
HFT regulations had any impact on the market at all. Presumably, any law designed to
make HFT more difficult will monotonically decrease trading volume. I will thus
determine whether the expected decrease in overall volume took place. I model trading
volume with the following specification:
(1) ln(Vt) = + * D + * Mt +
Alpha is a baseline trading volume. Beta is the percentage change in volume
created by D, a dummy variable to indicate observations after regulations were
introduced. I also include 12 different dummy variables (Mt) for months to account for
seasonality. I will then perform a difference-in-difference test on the model on the date of
the implementation of the regulatory regime. Finding a significant difference in betas
between the experimental sample and the control sample implies that the regulatory
regime changed the level of trading volume. The specification is as follows:
(2) Et = -
(3) SEt =
Et denotes the effect size of treatment t, where and are regression
coefficients derived from equation 1. SEt, the standard error of the effect size, is a pooled
variance estimator constructed from the sample standard deviations and sample sizes of
the treatment and control groups. My methodology follows Zhang, who constructs
difference estimates in the same manner. I use the same methodology to construct
difference-in-difference estimators for the remainder of my analysis.
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23
5.2 Impact on long-run volatility
Volatility in financial markets is a property undesirable to all market participants, save
for a few options traders and short sellers. One of the oft-claimed benefits of HFT is a
reduction in overall volatility. By this logic, any HFT regulations should cause an
increase in volatility. Lanne and Vesala examined the theoretical effects of a Tobin tax,
using transaction cost data as a proxy, and found that transaction costs have a significant
positive effect on volatility. Their reasoning is as follows: transaction taxes push
informed participants (i.e. HFT) out of the market, leaving uninformed participants
in charge of price discovery. This adverse selection of parties impedes the price discovery
process, creating more price volatility.
However, HFT detractors are quick to point out that HFT exacerbates volatility
clustering. At-Sahalia and Saglam model HFT as a Poisson process. They find that in
periods of high volatility, HFT are less likely to quote, effectively withdrawing liquidity
from the market when it needs it the most. This amplifies volatility by making further
price movements more likely.28
When HFT do choose to quote in periods of high
volatility, they decrease the size of their orders below normal levels. The authors also
examined the theoretical effects of a Tobin tax, the form of regulation at the heart of the
French and Italian regimes. They found that The HFTs quoting has the same sensitivity
to volatility when compared to the scenario in the absence of a Tobin tax.29 In other
words, regulation should not impact the autoregressive component of volatility, though it
may change the baseline level.
28
At-Sahalia and Saglam, p. 31 29
Ibid, p. 40
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24
There is much discussion in the financial literature about which types of models
should be used to fit volatility. Poon and Granger analyzed no fewer than 93 different
papers on volatility forecasting in their 2003 literature review. They concluded that the
best models incorporated implied volatility extracted by using the Black-Scholes model
on option prices. However, since the HFT regulations under consideration specifically
target equity markets, interpolating equity volatility from option prices seems
inappropriate.
The simplest way to model volatility is with an autoregressive process. An AR(p)
model regresses volatility on p lags and a constant. The specification is as follows:
(4) ti2 = i +
+
AR processes have a few convenient properties. They are generally stationary,
with the beta coefficients having values between zero and one. In the long run, AR
processes mean revert to some natural level. They model the phenomenon of volatility
clustering fairly well: a shock to volatility at time t will impact volatility for a long time.
The AR regression specification also allows for the use of entity-fixed effects, which can
account for different levels of unconditional volatility in different stocks.
However, simple AR processes have a few drawbacks. The first is the assumption
of homoscedasticity: by definition, errors are i.i.d. and drawn from a Gaussian
distribution. This assumption is generally unrealistic, since errors are neither Gaussian
nor independent in most financial data. AR processes also depend heavily on the
selection of the correct number of lags. I use an AR(1) process to describe the long-run
volatility change, since
converges to the true value of long-run volatility regardless
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25
of the number of lags selected, but omit the AR process from my discussion on volatility
persistence, which relies heavily on lag selection.
Autoregressive Conditional Heteroscedasticity (ARCH) models are conceptually
related to AR(p) models. Instead of regressing volatility on a constant term and lagged
values of volatility, ARCH models regress on a constant term and lagged values of the
squared error term. The ARCH(q) specification is:
(5) t2 = +
Generalized ARCH, or GARCH, models improve on ARCH by including lagged
values of volatility. A GARCH(p, q) model is simply an ARCH(q) model with a set of
AR(p) autoregressive terms. The GARCH(p, q) specification is:
(6) t2 = +
+
The GARCH(1, 1) model is by far the most popular model in modeling financial
volatility.30
I will include a GARCH(1, 1) model in my analysis of long-run volatility and
the persistence of volatility shocks.
Exponential GARCH provides several more improvements to volatility modeling.
First, it removes the constraint that coefficients must be positive. Second, it more closely
resembles a normal distribution by accounting for outliers. Third, it reflects the fact that
the autocorrelation of volatility is asymmetric: negative shocks create more volatility than
positive shocks.31
Poon and Granger find that In general, models that incorporate
volatility asymmetry such as EGARCH and GJR-GARCH perform better than
GARCH.32
30
Tersvirta, p. 4 31
Poon and Granger, p. 484 32
Ibid, p. 507
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26
However, there are drawbacks to using complex models like EGARCH. The
largest difficulty is in ensuring convergence of the parameters. When estimating GARCH
family models, STATA uses two different algorithms: the Berndt-Hall-Hall-Hausman
(BHHH) algorithm, and the Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm. Both
algorithms are quasi-Newtonian iterative methods designed to maximize the log
likelihood function. Unfortunately, there is no guarantee of convergence: the algorithms
may encounter a flat log likelihood function and fail to converge. Failure to converge
would result in estimated parameters that were not global maxima.
The second major difficulty comes in applying univariate econometric techniques
to data with multiple panels. In order to fit multiple equities into a single model, I must
constrain the coefficients to be the same for every stock within a country. This may be a
suboptimal constraint, particularly if different industries are governed by different
volatility processes. However, by analyzing only the stocks on each nations benchmark
index, I hope to correct for industry effects by ensuring a diversity of industries are well-
represented in my sample. Multivariate GARCH techniques do exist, but are
inappropriate for my purposes; since the number of coefficients increases in proportion to
N2, each model would require estimating at least 900 parameters, which is effectively
impossible with iterative maximum likelihood estimation.
I include an EGARCH(1, 1) process as one of my volatility estimating techniques.
The EGARCH(1, 1) specification is as follows:
(7) Ln(RVit2) = + * Ln(RVi,t-1
2) + * |
| + *
The debate over the best method of modeling volatility will, to borrow a
colloquialism if you please, continue to rage until the end of time. I include multiple
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27
models of volatility as a means of determining whether model specification significantly
impacts the results. Note that I do not include models of Stochastic Volatility, which lack
a closed form expression and are thus even less likely to achieve convergence than
EGARCH.
I will fit EGARCH models for each of the different countries, with separate
fittings for the period before and after regulations were implemented. There are 16
EGARCH models in total after accounting for control models. For Italy, the hypothesized
break date is September 2, 2013. For Germany, there are two hypothesized break dates:
the passage of the law, on May 15, 2013, and the deadline for obtaining a license to
perform HFT, on November 14, 2013. In France, the hypothesized break date is August
1, 2012.
After fitting the models, I use pooled variance to construct difference estimators,
allowing for a difference-in-difference between sample and control to be derived. The
difference-in-difference methodology should ensure exogeneity of the results, since no
major changes in regulatory policy occurred on the London Stock Exchange in 2012 or
2013.
A significant difference-in-difference would imply that the HFT regulations had a
significant impact on volatility. There are two possible impacts I will consider: a change
in
, the baseline level of volatility, and a change in beta, the volatility persistence
coefficient. To simplify my analysis, I will consider a reduction in either value to be an
improvement in market quality.
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28
5.3 M odels of intraday volatility
The characteristics of intraday data make the use of GARCH family models intractable in
modelling volatility. Ghalanos describes the main problem with the data: volatility is not
mean-reverting. Instead, it exhibits strong time-of-day effects, peaking at the daily open
and close of the market. This trait means that convergence of the parameters in any
maximum likelihood estimation is nearly impossible. Therefore, I use a simple AR(1)
model of volatility. My specification is as follows:
(8) = + *
+ * D + * D *
My volatility measure is the standard deviation in midprice movements over the
course of one minute, per Brogaard. Midprice is the average of the best bid and offer. is
a baseline volatility coefficient, and is the change in that coefficient after the
introduction of regulations. is an autoregressive coefficient, and is the change in
that coefficient caused by regulations. D is a dummy variable for observations after the
imposition of regulations. Average volatility can be recovered as
in the period
before regulations, and
in the period after. To account for excess volatility at the
open and close of trading, I omit observations from the first and last five minutes of
trading each day.
5.4 M odels of liquidity provision
There are two main ways to measure the provision of liquidity: bid-ask spreads and order
book depth. Ironically, data on depth is generally proprietary and reserved for HFT
clients, so the former will be my metric of choice. Using high-frequency data, I will
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29
measure bid-ask spreads at one minute intervals. My objective is to determine to what
extent volatility impacts the size of spreads, and whether HFT regulations increased or
decreased the size of the effect. My specification is as follows:
(9) ln(St) = + * ln(St-1) + * + + * D * ln(St-1) + * D *
St is the quoted spread of the benchmark ETF at time t, measured as the natural
log of the percentage spread. Taking the log of spreads helps account for situations where
baseline spreads are dramatically different in different countries; in particular, the FTSE
spread typically hovered around 3 basis points, while the MIB spread was in the
neighborhood of 17 basis points. is the constant before regulations are imposed. is
an autoregressive coefficient, since spreads are highly autocorrelated. is the coefficient
of interest: the change in spread caused by a unit change in volatility. D is a dummy
variable equal to 1 for observations after the implementation of regulations. After fitting
the model, I will perform a Chow test to see if the relationship between bid-ask spreads
and volatility changed. An increase in gamma implies that the regulations make spreads
more sensitive to volatility, and thus hurt the price discovery process. A decrease in
gamma suggests that bid-ask spreads become less correlated with volatility, which would
vindicate the regulations.
I will also measure the long-run value of spreads before and after the change. This
is denoted by
+ *
in period 1 and
+ ( + ) *
in period 2, with
volatility defined using the long-run values from section 5.2. (The potential difference
between average and realized volatility is relatively unimportant in this application, since
volatility contributes less than 1% of the value of the spread.)
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30
5.5 Potential threats to validity
In spite of my confidence about ensuring exogeneity, my study has a few limitations. The
most obvious potential pitfall is the use of univariate volatility modelling techniques on
panel data. Constraining multiple different assets to have the same coefficients may
introduce substantial upward bias in estimates for volatility persistence. This could be
corrected by creating a separate model for each asset, or by simply testing the benchmark
ETF instead of the indexs individual components. However, the decision to use multiple
assets was an effort to address the small T, large N problem of having too few
observations.
Another study design decision that might have impacted my results is the decision
to use the London Stock Exchange as a control group. My rationale for doing so was
fairly straightforward: it is the largest stock exchange in Europe, yet did not implement
any regulations on high-frequency trading during the sample period. However, the LSE
also trades in Pounds Sterling instead of Euros, since Britain is not a Eurozone member.
This raises the complication of accounting for the Euro crisis. I excluded data points
before 2012 in an effort to keep the Euro crisis from biasing my long-run volatility
estimates. However, it is possible I failed to truncate enough data for example, Mario
Draghis whatever it takes speech, which definitively secured the future of the
Eurozone, did not occur until July 26, 2012. The model might interpret the economic
recovery in early 2012 as a period of high volatility, thereby exaggerating the extent to
which regulations decreased volatility.
Yet another potential issue is the relative lack of datapoints in some cases. The
most egregious case is the German HFT licensure deadline: there are only 30 trading days
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31
represented in the period after the deadline passed. This makes it difficult to draw strong
inferences about the effects of the policy, and explains the relative lack of significant
results. I would strongly encourage academics to continue to study the empirical impacts
of HFT regulations over the course of the next few years.
A further complication is the introduction of multiple regulatory changes
simultaneously. This is not a major issue in the case of Germany, which implemented
HFT-specific regulations, or Italy, which delayed the implementation of an HFT tax until
several months after the introduction of a financial transactions tax. However, France
implemented their HFT tax and FTT at the same time. It is thus difficult to determine
which effects were caused by the HFT tax, and which were caused by the FTT. It would
be convenient if a country adopted an HFT tax without having an FTT in place, but alas,
countries dont design their regulatory policy around the dreams of econometricians.
6. Data
Data on long-run equity volatility will be drawn from Thomson Reuters Datastream
International and the CompuStat Global database. The data will span from January 1,
2012 to December 31, 2013. Data from the French, Italian, and German stock exchanges
will form the experimental groups, and British equities will form the control group. Per
Poon and Granger, I will use squared returns to calculate volatility, instead of squared
deviation from the mean return.
My sampling frequency will be daily, due to the relatively short timeframe of my
analysis. I follow the methodology of Guo, Kassa, and Ferguson, who recommend using
a two year rolling window of daily returns to ensure robustness and make the model less
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32
sensitive to the initial values. There are 512 trading days in the sample period. Our
desired dependent variable, Realized Volatility, is defined as the following:
(10) RVit =
For each country, I will analyze the stocks included in the primary exchanges
benchmark index. The exchanges are summarized below.
Country Index # Stocks Trading days
Germany DAX 30 16,380
France CAC-40 40 21,840
Italy FTSE MIB 40 21,840
Britain FTSE 100 100 54,600
Total trading days = 114,660
Data on bid-ask spreads and intraday volatility will be drawn from TickWrite.
Experimental data are available for France and Italy, and Britain will again serve as the
control sample.33
Data will be drawn at the tick level and aggregated into one-minute
intervals. The sample period will consist of a 23-day window around the implementation
of new regulations in Italy, and a 25-day window in France. Due to the large volume of
data generated at the tick level, I will only analyze the ETF representation of each
benchmark index, in lieu of the actual components. These data are summarized below:
33
German data exists, but is proprietary. Unfortunately, Princeton didnt want to spend $120,000 to help me write my thesis. Cest la vie.
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33
Country Index (ETF) Minutes Tick Observations
France CAC-40 (FP) 12,750 1,379,735
Italy FTSE MIB (IMIB) 11,730 119,182
Britain FTSE 100 (ISF) 24,480 1,824,754
Total trading minutes = 48,960
# Observations at tick intervals = 3,323,671
7. Hypothesis
All things considered, any form of HFT regulation should reduce the daily trading
volume of assets. I hypothesize that all of the implemented regulations will cause a
significant decrease in the equilibrium trading volume.
In terms of volatility, different empirical papers imply different outcomes. Those
who believe HFT moderate volatility would predict an increase in omega following
regulation. Those who think HFT withdraw liquidity during times of high volatility
believe regulation would cause a decrease in beta. These two theoretical results are not
incompatible. To accommodate both, I make the following hypothesis: beta will decrease,
but overall volatility (in terms of
) will increase.
The results should be largely the same when looking at bid-ask spreads. I predict
a decrease in gamma, the parameter representing sensitivity to volatility. This reflects the
intuition that HFT are less likely to quote in periods of high volatility, a behavior which
increases the size of spreads. I also anticipate an increase in the bid-ask spread size.
In terms of magnitude, I predict the largest effects in Italy, which imposed the
strictest regulations on HFT. A slightly smaller effect should be seen in France, which
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34
implemented similar restrictions to Italy. In Germany, I predict a very small effect size,
perhaps not statistically significant. This is due to the relatively lax nature of a licensure
regime, combined with the fact that the Deutsche Borse already monitored algorithmic
trading as early as 2007. Since Britain forms the control group, and adopted no new
regulations on HFT or other trading since 2012, there should be no effect during the
introduction of regulations in other countries.
8. Results and analysis
8.1 Change in trade volume
I begin by analyzing the change in trade volume caused by the implementation of
regulations. Our coefficient of interest is from regression 1. My results are summarized
below.
Table 1: Change in trade volume after imposition of regulations
Country % Change in sample
volume
% Change in control
volume
Difference-in-
difference
Germany (5/13) -18.06%***
(2.29%)
-10.26%***
(1.33%)
-7.8%***
(2.48%)
Germany (11/13) -14.37%***
(5.12%)
-4.82%*
(2.91%)
-9.55%*
(5.67%)
France -19.6%***
(1.89%)
-15.67%***
(1.37%)
-3.93%*
(2.12%)
Italy -9.78%
(6.13%)
-13.15%***
(1.83%)
3.37%
(5.97%)
Standard errors in parentheses
* denotes 0.1>p>0.05, ** denotes 0.05>p>0.01, *** denotes 0.01>p
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35
In Germany, both the May 15 announcement and the November 14 licensure
deadline were associated with a significant decline in trading volume. The results
remained significant when compared to the change in British trading volume during the
same time period. The effect size was 7.8% for the announcement, and 9.55% for the
licensure deadline a modest decline, to be sure, but still significant.
In France, the implementation of a combined financial transactions tax and HFT
cancellation tax caused a 19.6% decline in trading volume. After accounting for the
British control group, the relative decline in trading volume was 3.93%. This effect was
significant at the 10% level, though not as large as the German effect.
In Italy, trading volume declined by 9.78% after the implementation of an FTT
and HFT tax. The decline in volume barely misses significance at the 10% confidence
level, with a P value of 0.1108. Surprisingly, the decline in trade volume in the British
control group during the same time frame was larger. The relative effect was a 3.37%
increase in trade volume, though this effect is not statistically distinguishable from zero.
The difference between France and Italy is easy to explain: while France implemented a
generic FTT and HFT tax simultaneously, Italy introduced its FTT six months before
implementing an HFT tax. The HFT tax may have encouraged LFTs to reenter the market
after being driven away by perceived unfairness.
8.2 Change in long-run volatility
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36
The change in regulatory policy generally resulted in a decline in long-run volatility.
Model selection had an impact on the effect size and direction, with two models
producing an increase in volatility. A summary of the different models is offered below34
:
Table 2: Summary of long-run volatility across different models
Country EGARCH GARCH AR(1)
Vol? Significant? Vol? Significant? Vol? Significant?
Germany
(5/13) Increase Yes Decrease Yes Decrease No
Germany
(11/13) Decrease No Increase Yes Decrease No
France Decrease No Decrease Yes Decrease Yes
Italy Decrease Yes Decrease Yes Decrease Yes
In France and Italy, regulation caused a decline in volatility relative to the
benchmark, regardless of the model used. In almost all cases, this decline was statistically
significant at the 1% confidence level. This finding strongly vindicates Larry Summers
and other advocates of transaction taxes, who claim that excess speculation increases
market volatility.
The case for the German licensure regime is much less clear. The announcement
of the regime in May 2013 caused a significant increase in long-run volatility in the
EGARCH model, a significant decrease in the GARCH model, and an insignificant
decrease in the AR(1) model. Of note is the nonstationarity of volatility in the GARCH
model in the period after the announcement. The alpha and beta coefficients sum to more
than 1, making volatility a random walk process instead of a mean reverting process. In
the long run, volatility is always mean reverting, but nonstationarity may persist for some
34
Coefficient values are reported in appendices 1 - 3
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37
time. I attribute this to the relatively short timeframe (7.5 months) of the regime. This
makes it somewhat difficult to draw strong inferences about regime effectiveness.
The German licensure deadline causes an insignificant decline in volatility in the
EGARCH and AR(1) specifications, and a significant increase in volatility in the
GARCH specification. Again, this difference may be due to the sample size, which is
even smaller (1.5 months) in the post-licensing regime. It may also be caused by the
different natures of GARCH and EGARCH with respect to shocks. EGARCH tends to
magnify large shocks, and magnifies negative shocks more than positive shocks.
Depending on the number, sign, and magnitude of shocks, different models may produce
significantly different results. Given the different sign changes produced by different
models, and the statistical insignificance of three of them, I conclude that the licensure
regime had no impact on long-run volatility.
Figure 1: Conditional volatility as a function of past shocks35
35
Chart from Bollerslev, 2011, p. 24
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38
Interestingly, the impact of HFT regulations on the persistence of volatility shocks
is generally the opposite of the impact on long-run volatility. The following table
summarizes changes in , or volatility persistence, relative to the British benchmark:
Table 3: Relative change in volatility persistence
Country EGARCH GARCH
Persistence? Significant? Persistence? Significant?
Germany (5/13) Decrease No Decrease No
Germany (11/13) Decrease Yes Decrease No
France Increase No Decrease Yes
Italy Increase Yes Increase No
Both the announcement of the German licensure regime and the licensure
deadline were associated with a decrease in volatility persistence, regardless of model
selection. Unfortunately, only the EGARCH model of the licensure deadline found a
result significant at the 1% confidence level.
In the case of France, the EGARCH model found a moderate but insignificant
increase in the persistence of volatility shocks, while the plain GARCH model produces a
significant decrease in persistence. For Italy, both models produce an increase in
volatility persistence, though this is only significant in the EGARCH model. In fact, the
EGARCH model gives a beta of greater than 1 for Italy, implying nonstationarity of
volatility. Also of note is that both French and Italian regulations produced an increase in
the absolute length of volatility persistence, but the French regulations produced a
decrease in the relative length, due to a substantial increase in persistence in the control
group.
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39
Table 4: Half-life of volatility shocks, measured in days, GARCH model
Country
Sample group British control group
in Pre Post Pre Post
Germany(1) 16.64 15.71 -0.93 18.15 18.29 0.14 -1.07
Germany(2) 20.36 7.78 -12.58 18.59 9.51 -9.08 -3.5
France 18.45 20.91 2.46 11.37 15.68 4.31 -1.85
Italy 19.24 19.45 0.21 18.96 17.56 -1.4 1.61
8.3 Change in intraday volatility
In both France and Italy, the implementation of regulations was associated with a
significant increase in intraday volatility. This is in line with the expectations of HFT
advocates, who argue that HFT acts as a stabilizing force in the markets. In France, the
persistence of volatility shocks significantly increased, while persistence declined in
Italy.
Table 5: Impact of regulations on intraday volatility, France
Constant Vol Chow(P) R2
France Baseline 4.19***
(0.255)
0.24***
(0.048)
5.51***
(0.063) 71.94
(0) 0.2009
Change -0.551
(0.546)
0.235***
(0.088)
1.41***
(0.094)
Britain Baseline 2.25***
(0.781)
0.451*
(0.194)
4.09***
(0.053) 7.01
(0.0009) 0.1635
Change 0.266
(0.838)
-0.109
(0.211)
-0.27***
(0.091)
Difference-in-
difference
-0.817
(0.683)
0.343**
(0.152)
1.68***
(0.093)
Standard errors in parentheses
* denotes 0.1>p>0.05, ** denotes 0.05>p>0.01, *** denotes 0.01>p
Constant and Vol multiplied by 100,000
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40
Table 6: Impact of regulations on intraday volatility, Italy
Constant Vol Chow(P) R2
Italy Baseline 9.23***
(2.31)
0.366**
(0.177)
14.55***
(0.921) 2.91
(0.0543) 0.0529
Change 5.35**
(2.53)
-0.291
(0.186)
1.21
(1.27)
Britain Baseline 3.62***
(0.31)
0.109
(0.075)
4.06***
(0.039) 68.70
(0) 0.0726
Change -1.47***
(0.34)
0.223**
(0.088)
-0.84***
(0.423)
Difference-in-
difference
6.82***
(1.78)
-0.513***
(0.144)
2.05**
(0.889)
Standard errors in parentheses
* denotes 0.1>p>0.05, ** denotes 0.05>p>0.01, *** denotes 0.01>p
Constant and Vol multiplied by 100,000
8.4 Change in liquidity provision
The impact of regulations on bid-ask spreads is not as clear cut as I had anticipated. In
Italy, as predicted, spreads increased relative to the control sample, though the effect
barely misses significance at the 10% confidence level. Surprisingly, spreads
significantly decreased in France, in both absolute and relative terms. The specific
implementation of the French law may hold the key. In France, HFTs are only taxed for
order cancellations, and not for completed transactions, as long as they clear their
inventory by the end of the day. It is possible that HFTs only altered their order
cancellation behavior in response to the regulations, and not their quoting behavior. This
would leave an HFT offer at best bid or ask more frequently, and, in theory, reduce
spreads. Indeed, At-Sahalia and Saglam acknowledge that the probability of an HFT
quoting at a given time is increasing in the level of a cancellation tax.
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41
Table 7: Impact of regulations on bid-ask spreads and their responsiveness to
volatility, France, August 1, 2012
Constant Spread36 Chow(P) R2
France Baseline -2.832***
(0.332)
0.617***
(0.045)
1715***
(304)
6.76***
(0.199) 51.19
(0) 0.6441
Change 0.514
(0.366)
0.076
(0.05)
-392
(392)
-15.8%***
(4.58%)
Britain Baseline -2.36***
(0.163)
0.705***
(0.021)
1268***
(111)
3.47***
(0.094) 34.28
(0) 0.5395
Change -0.541**
(0.216)
-0.075***
(0.028)
-162
(133)
14.1%***
(3.24%)
Difference-in-
difference
1.056**
(0.425)
0.151***
(0.057)
-230
(414)
-29.9%***
(5.61%)
Standard errors in parentheses
* denotes 0.1>p>0.05, ** denotes 0.05>p>0.01, *** denotes 0.01>p
Table 8: Impact of regulations on bid-ask spreads and their responsiveness to
volatility, Italy, September 2, 2013
Constant Spread36 Chow(P) R2
Italy Baseline -1.009***
(0.148)
0.842***
(0.023)
90**
(42.3)
17.32***
(0.537) 19.92
(0) 0.6549
Change -2.148***
(0.347)
-0.332***
(0.054)
78.5
(60.5)
-5.35%*
(3.15%)
Britain Baseline -3.027***
(0.154)
0.621***
(0.019)
110
(756)
3.42***
(0.168) 14.82
(0) 0.4313
Change -0.731***
(0.234)
-0.073***
(0.028)
3142***
(893)
-21%*
(11.3%)
Difference-in-
difference
-1.417***
(0.418)
-0.259***
(0.061)
-3064***
(895)
15.63%
(11.73%)
Standard errors in parentheses
* denotes 0.1>p>0.05, ** denotes 0.05>p>0.01, *** denotes 0.01>p
My empirical findings with respect to gamma, the volatility sensitivity parameter,
exactly match my hypothesis. In both Italy and France, sensitivity to volatility decreased
relative to the control sample. In France, sensitivity decreased in both the absolute and
relative senses, though neither change was significant at the 10% confidence level. In
36
Baseline spread is reported in basis points, while change is measured as a percentage of the baseline
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42
Italy, sensitivity only declined relative to the British control sample, though the relative
decline was significant at the 1% confidence level.
In every case, there is a positive correlation between volatility and bid-ask
spreads. The findings confirm the theoretical results of At-Sahalia and Saglam, who state
that HFTs are less likely to quote during periods of high volatility, widening spreads in
the process. Cancellation taxes directly incentivize HFTs not to withdraw liquidity during
periods of high volatility, when liquidity is most needed.
9. Conclusion
The problem of regulating high-frequency trading is especially difficult to solve. There
are many different stakeholders with dramatically different interests to satisfy. A central
regulator must balance the demands of traders, exchanges, and institutional investors, all
while ensuring the stability and robustness of markets.
Additionally, the rapidly-evolving nature of HFT means that regulators lack any
meaningful precedent to turn to for informing their policymaking. European governments
appear to be entering a test and learn phase, in which new policies are implemented and
tweaked until they appear to work.
My analysis underscores the difficulty of finding an optimal regulatory policy. In
several cases, the significance and even direction of the change in market quality
measures differed between different models. Even when the models do agree, the policies
themselves generally produce different results. And no form of regulation is perfect
each regulation improved some measures of market quality while worsening others.
Nevertheless, there are some useful pieces of information we can extract.
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43
The German licensure regime appears to have modestly improved market quality.
The impact on long-run volatility is ambiguous, though a majority of models found some
decrease in the unconditional variance of returns. However, all models tested found a
decrease in volatility persistence. Licensure may reduce conditional heteroscedasticity by
discouraging HFTs from withdrawing liquidity at key moments or exacerbating volatility
with phantom orders designed to move the market. Indeed, the true success of licensure is
its ability to distinguish between abusive practices and legitimate market-making
activities.
Both the French and Italian regulatory regimes appear to have substantially
decreased long-run volatility. Model selection had little to no impact on this result. Since
a reduction in volatility means an improvement in market quality, the policies were
dramatically successful on this front. However, there appears to be a tradeoff between
long-run volatility and persistence. The persistence of volatility shocks significantly
increased in Italy; in France, the models disagreed on the direction of the change. This
inverse relationship is surprising: if we held constant, then long-run volatility and
volatility persistence would move in tandem by definition.
There are more tradeoffs to consider when analyzing market microstructure. The
French and Italian regulations both substantially increased intraday volatility measured at
one-minute intervals. However, both regulations decreased the sensitivity of bid-ask
spreads to volatility. This tradeoff might strike different stakeholders very differently. An
institutional investor, for example, might not care about intraday volatility if his holding
period is measured in months or years, but might care a lot about preventing potential
flash crash situations. Meanwhile, a market maker who ends each day with zero net
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44
inventory would be tremendously harmed by an increase in intraday volatility, but
wouldnt particularly care that HFTs withdraw liquidity in times of high volatility
indeed, he would be one of the first traders to cancel his orders.
My final analysis considered how bid-ask spreads responded to regulatory
changes. I was surprised to find that spreads in France significantly contracted after
regulations were introduced. This might simply be a result of HFTs leaving limit orders
in place that they would have cancelled in the absence of a tax. However, I am faced with
the classic joint-causality problem, due to the FTT introduced at the same time. In Italy,
spreads expanded, but not significantly so. This hints that the FTT was responsible for the
contraction of spreads. It may be the case that the FTT, which only applies to stocks held
at the end of the day, increases inventory aversion, leading HFTs to quote more
frequently to clear their inventories and avoid the tax.
In light of all this information, what is the optimal HFT regulatory policy? At the
risk of sounding indecisive, I would recommend an all-of-the-above strategy. The
licensure regime, while failing to change long-run volatility, decreased volatility
persistence, ensuring that shocks to volatility dissipate more quickly. Moreover, licensure
would force HFTs to disclose their algorithms to the government, allowing efficiency-
generating algorithms like market-making to proliferate, while curtailing rent-seeking
algorithms like low-latency frontrunning.
Cancellation taxes appear to decrease unconditional volatility, decrease bid-ask
spreads, and decrease the sensitivity of spreads to volatility. The latter property is
particularly important: robustness in the face of high volatility helps prevent flash crash
style events from occurring. These benefits do come at the cost of increased intraday
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45
volatility and increased volatility persistence, but the benefits appear to outweigh the
harms. Cancellation taxes would primarily hamper algorithms designed to artificially