1 Robert Engle UCSD and NYU July 2000. 2 WHAT IS LIQUIDITY? n A market with low “transaction costs” including execution price, uncertainty and speed n.

Slides:



Advertisements
Similar presentations
Some recent evolutions in order book modelling Frédéric Abergel Chair of Quantitative Finance École Centrale Paris
Advertisements

Aswath Damodaran1 Smoke and Mirrors: Price patterns, charts and technical analysis Aswath Damodaran.
Chapter 16 Value Traders. Value traders supply liquidity Uninformed traders cause prices to deviate from fundamental values Dealers mistakenly respond.
Modeling the Asymmetry of Stock Movements Using Price Ranges Ray Y. Chou Academia Sinica “ The 2002 NTU International Finance Conference” Taipei. May 24-25,
Futures trading and market microstructure of the underlying security: A high frequency experiment at the single stock futures level Kate Phylaktis and.
Arturo Bris “Short Selling Activity in Financial Stocks and the SEC July 15 th Emergency Order” Discussion by Ian Marsh, Cass Business School.
Transactions Costs.
X-CAPM: An extrapolative capital asset pricing model Barberis et al
CHAPTER FOUR EFFICIENT MARKETS, INVESTMENT VALUE AND MARKET PRICE.
By Paul Cottrell, BSc, MBA, ABD. Author Complexity Science, Behavioral Finance, Dynamic Hedging, Financial Statistics, Chaos Theory Proprietary Trader.
Information-based Trading, Price Impact of Trades, and Trade Autocorrelation Kee H. Chung Mingsheng Li Thomas H. McInish.
HIA and Multi-Agent Models ●New paradigm of heterogeneous interacting agent models (Reviewed in Markose, Arifovic and Sunder (2007)) ●Zero intelligence.
Implied Volatility Correlations Robert Engle, Stephen Figlewski and Amrut Nashikkar Date: May 18, 2007 Derivatives Research Conference, NYU.
Market Efficiency Chapter 10.
CHAPTER 10 Overcoming VaR's Limitations. INTRODUCTION While VaR is the single best way to measure risk, it does have several limitations. The most pressing.
Simple Linear Regression
NEW MODELS FOR HIGH AND LOW FREQUENCY VOLATILITY Robert Engle NYU Salomon Center Derivatives Research Project Derivatives Research Project.
1 Caput Financial Markets Frank de Jong Universiteit van Amsterdam September 2001.
PREDICTABILITY OF NON- LINEAR TRADING RULES IN THE US STOCK MARKET CHONG & LAM 2010.
© 2008 Pearson Education Canada7.1 Chapter 7 The Stock Market, the Theory of Rational Expectations, and the Efficient Markets Hypothesis.
Empirical Financial Economics The Efficient Markets Hypothesis Review of Empirical Financial Economics Stephen Brown NYU Stern School of Business UNSW.
Spline Garch as a Measure of Unconditional Volatility and its Global Macroeconomic Causes Robert Engle and Jose Gonzalo Rangel NYU and UCSD.
Discussion of The Examination of R&D Impact on Firm Value By Chuan Yang Hwang Nanyang Technological University.
FINANCIAL ECONOMETRICS FALL 2000 Rob Engle. OUTLINE DATA MOMENTS FORECASTING RETURNS EFFICIENT MARKET HYPOTHESIS FOR THE ECONOMETRICIAN TRADING RULES.
The Lognormal Distribution
The Stock Market Forecasting and Risk Management System using Genetic Programming Li Wang Ross School of Business Ann Arbor, MI
Impact of the introduction of the risk management products Dr. San-Lin Chung Department of Finance National Taiwan University.
©R. Schwartz Equity Markets: Trading and Structure Slide 1 Topic 5.
Chapter 7 The Stock Market, The Theory of Rational Expectations, and the Efficient Market Hypothesis.
Instruments of Financial Markets at Studienzentrum Genrzensee Switzerland. August 30-September 17, 2004 Course attended by: Muhammad Arif Senior Joint.
Chapter 12: Market Microstructure and Strategies
Efficient Market Hypothesis by Indrani Pramanick (44)
Efficient Capital Markets Objectives: What is meant by the concept that capital markets are efficient? Why should capital markets be efficient? What are.
Price patterns, charts and technical analysis: The momentum studies Aswath Damodaran.
Is Information Risk Priced? Evidence from the Price Discovery of Large Trades Chuan Yang Hwang Nanyang Technological University and Xiaolin Qian Nanyang.
Presented by Ori Gil Supervisor : Gal Zahavi Control and Robotics Laboratory Winter 2011.
Barrow Boys (and Girls!) with Degrees The Theory and Practice of Equity Trading.
Presented by Bruce Vanstone for the Australian Shareholders Association.
This paper is about model of volume and volatility in FX market Our model consists of 4 parts; 1. M odel of Order Flow Generation 2. Model of Price Determination.
FINANCE AND THE FUTURE In this great future you can’t forget your past … by David Pollard 1.
©R. Schwartz Equity Markets: Trading and Structure Slide 1 Bob Schwartz Zicklin School of Business Baruch College, CUNY.
Federico M. Bandi and Jeffrey R. Russell University of Chicago, Graduate School of Business.
MARKET MICROSTRUCTURE. THE FUNDAMENTAL QUESTION OF MARKET MICROSTRUCTURE: zHOW DOES INFORMATION GET INCORPORATED INTO PRICES??
Effect of Learning and Market Structure on Price Level and Volatility in a Simple Market Walt Beyeler 1 Kimmo Soramäki 2 Robert J. Glass 1 1 Sandia National.
Downside Risk: Implications for Financial Management Robert Engle NYU Stern School of Business Tilburg April 22, 2004.
1 Robert Engle and Asger Lunde NYU and UCSD and University of Aarhus May 2001.
1 Model 3 (Strategic informed trader) Kyle (Econometrica 1985) The economy A group of three agents trades a risky asset for a risk-less asset. One insider.
Chapter 7 The Stock Market, the Theory of Rational Expectations, and the Efficient Market Hypothesis.
Discussion of Evans and Lyons, “A New Micro Model of Exchange Rate Dynamics” Nelson C. Mark University of Notre Dame.
1 13. Empirical market microstructure Empirical analysis of market microstructure focuses on order flows, bid/ask spread, and structure of the limit-order.
Incorporating News into Algorithmic Market Trading Presented by Philip Gagner, Vice President RavenPack International, S.L. With the kind assistance of.
1 Transparency, Information Content and Order Placement Strategy Tai Ma, Yaling Lin, Hsiu-Kuei Cheng Department of Finance, National Sun Yat-sen University.
Robert Engle UCSD and NYU and Robert F. Engle, Econometric Services DYNAMIC CONDITIONAL CORRELATIONS.
©R. Schwartz, B Steil, & B. Weber June 2008 Slide 1 Bob Schwartz Zicklin School of Business Baruch College, CUNY.
Investor Sentiment and Price Discovery: Evidence from the Pricing Dynamics between the Futures and Spot Markets SWJTU, Chengdu, 2015 Robin K. Chou National.
1. 2 EFFICIENT MARKET HYPOTHESIS n In its simplest form asserts that excess returns are unpredictable - possibly even by agents with special information.
Run length and the Predictability of Stock Price Reversals Juan Yao Graham Partington Max Stevenson Finance Discipline, University of Sydney.
ARCH AND GARCH V AIBHAV G UPTA MIB, D OC, DSE, DU.
1 Lecture 12 The Stock Market, the Theory of Rational Expectations, and the Efficient Market Hypothesis.
Copyright © 2002 Pearson Education, Inc. Slide 10-1.
Lecture 8 Stephen G. Hall ARCH and GARCH. REFS A thorough introduction ‘ARCH Models’ Bollerslev T, Engle R F and Nelson D B Handbook of Econometrics vol.
Introduction to Probability - III John Rundle Econophysics PHYS 250
Predicting Returns and Volatilities with Ultra-High Frequency Data -
Empirical Financial Economics
Author: Konstantinos Drakos Journal: Economica
Private Equity Indices Based on Secondary Market Transactions
Techniques for Data Analysis Event Study
High frequency market microstructure
Presentation transcript:

1 Robert Engle UCSD and NYU July 2000

2 WHAT IS LIQUIDITY? n A market with low “transaction costs” including execution price, uncertainty and speed n This may mean different things depending upon the volume to be traded and impatience of the trader.

3 THREE MEASURES: n Bid Ask Spread –measures costs for small trades n Depth –quoted depth for small trades –depth with some price deterioration n Price Impact of a Trade –how much prices move in response to a large trade

4

5 HOW DO THESE MEASURES OF LIQUIDITY VARY OVER TIME AND CAN THEY BE PREDICTED? n BRIEFLY -THE ANSWER FIRST!! n ACROSS ASSETS – MORE TRANSACTIONS AND MORE VOLUME MEANS MORE LIQUIDITY. n HOWEVER – OVER TIME, MARKETS BECOME LESS LIQUID WHEN THEY ARE MORE ACTIVE!!!

6 WHY SHOULD EXECUTION BE WORSE WHEN THE MARKET IS ACTIVE? n Because the market is more active when there is information flowing. n When there is information, traders watch trades (and each other) to learn the information as quickly as possible n Often called “Price Discovery”

7 MICROSTRUCTURE THEORY n Inventory models –More trades make inventories easier to manage –lower transaction costs and more liquidity n Asymmetric Information models –More informed traders increase adverse selection costs - greater spreads and price impacts

8 ASYMMETRIC INFORMATION MODELS n Glosten and Milgrom(1985) following Bagehot(1971) and Copeland and Galai(1983) n A fraction of the traders have superior information about the value of the asset but they are otherwise indistinguishable.

9 MARKET MAKER INFERENCE PROBLEM: n If the next trader is a buyer, this raises my probability that the news is good. Knowing all the probabilities I can calculate bid and ask prices: n Over time, the specialist and the market ultimately learn the information and prices reflect this.

10 Easley and O’Hara(1992) n Three possible events- Good news, Bad news and no news n Three possible actions by traders- Buy, Sell, No Trade n Same updating strategy is used

11

12 Easley Kiefer and O’Hara n Empirically estimated these probabilities n Econometrics involves simply matching the proportions of buys, sells and non- trades to those observed. n Does not use (or need) prices, quantities or sequencing of trades

13

14

15

16

17 LIQUIDITY IMPLICATIONS n When the proportion of informed traders is high, the market is less liquid in all dimensions n When information flows, there are more informed traders, as they rush to trade ahead of price movements n For specific public news events, this could approach 100%

18 INFORMED TRADERS n What is an informed trader? –Information about true value –Information about fundamentals –Information about quantities –Information about who is informed

19 PRICE IMPACTS OF TRADES n In real settings where traders have a choice about when to trade, how to trade and how much to trade –Their choices may indicate whether they have information –Large trades and rapid trades and trades by big players all have greater price impacts

20 Econometric Tools n Data are irregularly spaced in time n The timing of trades is informative n Need to model jointly the time and characteristics of a trade n This is called a marked point process n Will use Engle and Russell(1998) Autoregressive Conditional Duration (ACD)

21 STATISTICAL MODELS n There are two kinds of random variables: –Arrival Times of events such as trades –Characteristics of events called Marks which further describe the events n Let x denote the time between trades called durations and y be a vector of marks n Data:

22 A MARKED POINT PROCESS n Joint density conditional on the past: n can always be written:

23 THE CONDITIONAL INTENSITY PROCESS n The conditional intensity is the probability of an event at time t+  t given past arrival times and the number of events.

24 THE ACD MODEL n The statistical specification is: n where  is the conditional duration and  is an i.i.d. random variable with non- negative support

25 TYPES OF ACD MODELS n Specifications of the conditional duration: n Specifications of the disturbances –Exponential –Weibul –Generalized Gamma –Non-parametric

26 MAXIMUM LIKELIHOOD ESTIMATION n For the exponential disturbance n which is so closely related to GARCH that often theorems and software designed for GARCH can be used for ACD. It is a QML estimator.

27 EMPIRICAL EVIDENCE n Dufour and Engle(2000), “Time and the Price Impact of a Trade”, Journal of Finance, forthcoming n Engle, Robert and Jeff Russell,(1998) “Autoregressive Conditional Duration: A New Model for Irregularly Spaced Data, Econometrica n Engle, Robert,(2000), “The Econometrics of Ultra-High Frequency Data”, Econometrica n Engle and Lunde, “Trades and Quotes - A Bivariate Point Process” n Russell and Engle, “Econometric analysis of discrete-valued, irregularly-spaced, financial transactions data ”

28

29 APPROACH n Extend Hasbrouck’s Vector Autoregressive measurement of price impact of trades n Measure effect of time between trades on price impact n Use ACD to model stochastic process of trade arrivals

30 DATA: n TORQ dataset -transactions on 18 stocks for 3 months from Nov Jan These are the actively traded stocks.

31 DEFINITIONS PRICE is midquote when a trade arrives (actually use 5 seconds before a trade). R is the log change in PRICE T is the time between transactions X = 1 if transaction price> midquote, i.e. BUY X= -1 if transaction price < midquote, SELL X= 0 if transaction price = midquote V is the number of shares in a transaction

32 CORRELATIONS

33 HASBROUCK MODEL (1991) GENERALIZED FOR TIME EFFECT n Vector Autoregression of trade directions and returns n Use to calculate the long run effect of trades on prices as a function of time between trades

34 RESULTS FOR RETURN EQUATION: n  o > 0 for all 18, all very significant –Buys raise prices n  o < 0 for 17, 13 significant –Buys raise prices more when durations are short n H: all  = 0; rejected for 13 –Time Matters n H: ; rejected for 13, negative for 16

35 n for 18, all very significant, –serial correlation in trade direction n for 15, significantly negative for 10, –short durations increase autocorrelation n rejected for 11 n rejected for 12, 11 negative –time matters for trade dynamics RESULTS FOR TRADE EQN.

36 INTRODUCING OTHER INTERACTIONS n n H: all  =0;rejected for 8 of 18 stocks. n Volume and Spread are very significant

37 WACD estimation for FNM and IBM

38 CALCULATE IMPULSE RESPONSES OF A TRADE. n WITH DURATIONS FIXED AT A PARTICULAR VALUE n WITH DURATIONS EVOLVING JOINTLY n MEASURED IN CALENDAR TIME RATHER THAN TRANSACTION TIME n Latter two require stochastic simulation of the ACD

39

40

41 SUMMARY n The price impacts, the spreads, the speed of quote revisions, and the volatility all respond to information n Econometric measures of information –high shares per trade –short duration between trades –wide spreads

42 CONCLUSIONS n MARKETS ARE LESS LIQUID WHEN THEY ARE MORE ACTIVE n TRANSITION TO FULL INFORMATION OR EFFICIENT PRICES IS FASTER WHEN THERE IS INFORMATION ARRIVING