Robert Engle and Robert Ferstenberg Microstructure in ...

Robert Engle and Robert Ferstenberg Microstructure in Paris

December 8, 2014

Is varying over time and over assets

Is a powerful input to many financial decisions such as portfolio construction and trading algorithms

Is never directly observed but is believed to be correlated with volatility and volume and bid ask spread

Can be interpreted as the ease with which an investor can purchase or sell large quantities.

Forecasting Transaction Costs

Create several measures of transaction costs based on market data with daily market volume orders (DMVO)

Produce forecasts of these measures

Compare these forecasts with transaction records from Abel Noser Solutions (ANS)

Do this daily for many US equity names over a 15 year period.

IS is the difference between the market price at the time the order is entered and the price at which a trade is executed, measured as a return.

IS =log(execution price/arrival price)*side ◦ Where side is 1 for a buy order and -1 for a sell

order

◦ IS should be positive on average for all trades.

◦ IS has low ratio of signal to noise.

Perold, Kyle, Grinold and Kahn, Engle Ferstenberg Russell, Kyle and Obisheva, Pastore and Stambaugh, Hasbrouck, Roll, Amihud, Easley Lopez de Prado and O’Hara, Russell, Jones and Lipson, Engle and Lange, Keim and Madhavan….

Many industry studies, ITG, ANS, AQR…

Apologies for the authors omitted

Forecast Transaction Costs

Use transaction data from ANS. These are “parent” orders which were typically filled with sequences of small “child” orders.

We have execution price and fill quantity. We also have a buy sell indicator.

We approximate the arrival price by the opening price.

We do not have the order size for orders that are not completed. This is probably an important bias.

Thus we can compute IS for more than 300 million executed orders since 1998.

We consider the entire daily market volume on a name as an order.

The arrival price is then the open and the execution price is VWAP. We approximate with the close.

The direction of the trade must be inferred from the price movement. If the price goes up, the trade is classified as a buy and conversely for a sell.

An order which is a small fraction of daily volume will incur only a fraction of the daily impact.

Trade direction has always been inferred from prices with market data. After all, every execution has a willing buyer and a seller.

The Lee and Ready algorithm offers two solutions to signing a single trade – either the price change is used or the price relative to the recent mid-quote.

Lee and Ready was motivated by the idea that a market order was active and executed by a specialist. Today there are no specialists and informed traders do not generally use market orders. Limit orders can have price impact so the direction of trade can best be inferred by the price movement.

Not all trades have equal price impact. More informative trades have more impact. Hence, it makes sense to measure impact on average. Information on the informativeness of trades can be used if available.

Easley, Lopez de Prado, O’Hara in a series of papers have introduced Bulk Volume Classification or BVC which uses inter daily data to classify trades by the price change.

We take this to the daily limit. DMVO

Volatility

Volume

Average Daily Volume

Bid Ask Spread

The correlation of volume and volatility is central to price impact.

Predictions of transaction cost are based on order size relative to predicted volume.

Amihud, Yakov(2002), Illiquidity and stock returns: Cross-section and time series effects, Journal of Financial Markets 5, 31–56.

Grinold,Richard and Ronald Kahn(1999) Active Portfolio Management: A Quantitative Approach for Producing Superior Returns and Controlling Risk, McGraw Hill

Engle, Robert, Robert Ferstenberg and Jeffrey Russell (2012), ―Measuring and Modeling Execution Cost and Risk,‖ The Journal of Portfolio Management.

Historical forecasts ◦ Lagged 21 day average of absolute return divided

by daily dollar volume

◦ There are no unknown parameters in this system

◦ Forecasting can also be done econometrically by using MEM models as in VLAB

Transaction costs are approximated by multiplying ILLIQ by the order in dollars

Where Volatility is 21 day lagged standard deviation of open to close returns, Volume is todays dollar volume, ADV is 21 day average dollar volume and Spread is the bid ask spread divided by open price.

Estimated with panel of CRSP names for a year and forecast with the same betas for the following year.

1 1 2 1

1

* tt t t

t

VolumeIS Spread Volatility

ADVb b- -

-

= +

This specification ensures that expected IS is non-negative. It is a generalization of GK.

Estimate with panel of one year data on all trades in the year.

Forecast for the next years trades.

0 31 2

1 1t t t tIS e Volatility Volume ADVb bb b

- -=

0,00E+00

5,00E-09

1,00E-08

1,50E-08

2,00E-08

2,50E-08

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

Mean by Year of Daily ADN Weighted Mean of Mean of 21 Day Lagged IILIQ

12/22/2014 NYU Stern Volatility Institute 16

0

0,00005

0,0001

0,00015

0,0002

0,00025

0,0003

0,00035

0,0004

0,00045

0,0005

Mean by Year of Daily ADN

Weighted Mean of Out of Sample

ILLIQ Forecast of 1% ADV

0,00E+00

5,00E-07

1,00E-06

1,50E-06

2,00E-06

2,50E-06

3,00E-06

3,50E-06

4,00E-06

4,50E-06

5,00E-06

1 3 5 7 9 11 13 15 17 19 21



ILLIQ Forecast of $1M


0

0,005

0,01

0,015

0,02

0,025

0,03

0,035

1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

forecast

realization

prediction


0

0,0005

0,001

0,0015

0,002

0,0025

0,003

0,0035

0,004

1994 1996 1998 2000 2002 2004 2006 2008 2010 2012



CRSP GK Forecast of 1% ADV

0

0,0005

0,001

0,0015

0,002

0,0025

1994 1996 1998 2000 2002 2004 2006 2008 2010 2012



CRSP GK Forecast of $1M


0

0,005

0,01

0,015

0,02

0,025

0,03

0,035

1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

forecast

realization

prediction


0

0,001

0,002

0,003

0,004

0,005

0,006

0,007

0,008

1994 1996 1998 2000 2002 2004 2006 2008 2010 2012



CRSP EFR Forecast of 1% ADV

0

0,0002

0,0004

0,0006

0,0008

0,001

0,0012

0,0014

1994 1996 1998 2000 2002 2004 2006 2008 2010 2012



CRSP EFR Forecast of $1M

ABEL NOSER SOLUTIONS

ANCERNO TRANSACTION DATA SET

More than 300 million orders executed by ANS clients from 1998-2013.

Arrival price is the open

Execution price is the average price of the executed shares.

Fill is the number of shares executed.

Data are cleaned, extremes are removed and bucketed in many ways.

Data are matched with CRSP by date/cusip/symbol


-0,001

-0,0005

0

0,0005

0,001

0,0015

0,002

0,0025

0,003

0,0035

0,004

0,0045

Mean IS

EW-IS

VW-IS

AW-IS

NW-IS

-0,0005

0

0,0005

0,001

0,0015

0,002

0,0025

0,003

Mean of IS 50-51%tile

EW-IS

VW-IS

AW-IS

NW-IS

ANS Summary Statistics By Year


• Comparison between: • VWAP: Un-weighted

average execution cost if ANS fills are priced at VWAP approximated as average of open and close price

• EW-IS: Un-weighted average reported by ANS

• Assumption is that impact of ANS executions are priced in the market data 0

0,0005

0,001

0,0015

0,002

0,0025

0,003

0,0035

19

98

19

99

20

00

20

01

20

02

20

03

20

04

20

05

20

06

20

07

20

08

20

09

20

10

20

11

20

12

20

13

Mean IS Fills Priced at VWAP

EW-IS

VWAP

The ANS estimate of Implementation Shortfall shows some surprising features.

The peak IS for medium to large trades occurs in 2009 and then it falls slowly.

For smaller trades it is in 2011 and seems surprisingly large.

2001 and 2002 are very low cost years and this seems surprising too.

For each trade in ANS data set, predict IS from model parameters in preceding year using ANS fill and market variables.

Predict ANS IS as an intercept plus slope coefficient on forecast for all trades in a year. This is sometimes called Mincer-Zarnowitz regression.

Consider the predictions of this model as the transaction cost model.


0

0,0005

0,001

0,0015

0,002

0,0025

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

forecast

realization

prediction


0

0,0005

0,001

0,0015

0,002

0,0025

0,003

0,0035

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

forecast

realization

prediction

Better spread data

Explore impact of estimation universe ◦ CRSP is ~5000 names

◦ ANS is ~2000 names

Explore models of excess returns

Seek alternative samples of executions.

A PROMISING APPROACH TO LIQUIDITY FORECASTING!

Robert Engle and Robert Ferstenberg Microstructure in ...

Documents

Transcript of Robert Engle and Robert Ferstenberg Microstructure in ...