91_1 engle forecasting illiquidity-1

8/16/2019 91_1 Engle Forecasting Illiquidity-1

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Robert Engle and Robert FerstenbergMicrostructure in Paris

December 8, 2014


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Is varying over time and over assets Is a powerful input to many financial

decisions such as portfolio construction andtrading algorithms

Is never directly observed but is believed tobe correlated with volatility and volume andbid ask spread

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

Forecasting Transaction Costs


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Create several measures of transaction costsbased on market data with daily marketvolume orders (DMVO)

Produce forecasts of these measures

Compare these forecasts with transactionrecords from Abel Noser Solutions (ANS)

Do this daily for many US equity names over a

15 year period.


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IS is the difference between the market priceat the time the order is entered and the priceat which a trade is executed, measured as areturn.

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.


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Perold, Kyle, Grinold and Kahn, EngleFerstenberg 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


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Use transaction data from ANS. These are“parent” orders which were typically filled withsequences 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 arenot completed. This is probably an important

bias. Thus we can compute IS for more than 300

million executed orders since 1998.


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

The arrival price is then the open and theexecution 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, thetrade 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.


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Trade direction has always been inferred fromprices with market data. After all, everyexecution has a willing buyer and a seller.

The Lee and Ready algorithm offers two solutionsto 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 aspecialist. Today there are no specialists and

informed traders do not generally use marketorders. Limit orders can have price impact so thedirection of trade can best be inferred by theprice movement.


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Not all trades have equal price impact. Moreinformative trades have more impact. Hence,it makes sense to measure impact onaverage. Information on the informativeness

of trades can be used if available.

Easley, Lopez de Prado, O’Hara in a series ofpapers have introduced Bulk VolumeClassification or BVC which uses inter dailydata to classify trades by the price change.

We take this to the daily limit. DMVO


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Volatility Volume

Average Daily Volume

Bid Ask Spread

The correlation of volume and volatility iscentral to price impact.

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


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Amihud, Yakov(2002), Illiquidity and stockreturns: Cross-section and time series effects,

Journal of Financial Markets 5, 31– 56.

Grinold,Richard and Ronald Kahn(1999) Active

Portfolio Management: A Quantitative Approachfor Producing Superior Returns and ControllingRisk, McGraw Hill

Engle, Robert, Robert Ferstenberg and Jeffrey

Russell (2012), ―Measuring and ModelingExecution Cost and Risk,‖ The Journal of PortfolioManagement.


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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 byusing MEM models as in VLAB

Transaction costs are approximated by

multiplying ILLIQ by the order in dollars


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

Estimate with panel of one year data on alltrades in the year.

Forecast for the next years trades.

0 31 2

1 1t t t t IS e Volatility Volume ADV b bb b

- -=


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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


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12/22/2014NYU 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


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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


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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


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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


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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


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ABEL NOSER SOLUTIONSANCERNO TRANSACTIONDATA SET


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

Arrival price is the open

Execution price is the average price of theexecuted 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


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-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


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ANS Summary Statistics By Year

12/22/2014 NYU Stern Volatility Institute 29

• Comparison between:• VWAP: Un-weighted

average execution cost ifANS fills are priced at

VWAP approximated asaverage of open and closeprice

• EW-IS: Un-weightedaverage reported by ANS

•

Assumption is thatimpact of ANS executionsare priced in the marketdata 0

0,0005

0,001

0,0015

0,002

0,0025

0,003

0,0035

1 9 9 8

1 9 9 9

2 0 0 0

2 0 0 1

2 0 0 2

2 0 0 3

2 0 0 4

2 0 0 5

2 0 0 6

2 0 0 7

2 0 0 8

2 0 0 9

2 0 1 0

2 0 1 1

2 0 1 2

2 0 1 3

Mean IS Fills Priced at VWAP

EW-IS

VWAP


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The ANS estimate of Implementation Shortfallshows some surprising features.

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

For smaller trades it is in 2011 and seemssurprisingly large.

2001 and 2002 are very low cost years and

this seems surprising too.


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For each trade in ANS data set, predict ISfrom model parameters in preceding yearusing 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-Zarnowitzregression.

Consider the predictions of this model as thetransaction cost model.


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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


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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


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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.


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A PROMISING APPROACHTO LIQUIDITY FORECASTING!

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