Bien, Katarzyna, Nolte, Ingmar and Pohlmeier, Winfried. (2009) An inflated multivariate integer count hurdle model : an application to bid and ask quote dynamics. Journal of Applied Econometrics, Volume 26 (Number 4). pp. 669-707. ISSN 0883-7252

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Abstract

In this paper we develop a model for the conditional inflated multivariate density of integer count variables with domain Z(n), n is an element of N. Our modelling framework is based on a copula approach and can be used for a broad set of applications where the primary characteristics of the data are: (i) discrete domain; (ii) the tendency to cluster at certain outcome values; and (iii) contemporaneous dependence. These kinds of properties can be found for high- or ultra-high-frequency data describing the trading process on financial markets. We present a straightforward sampling method for such an inflated multivariate density through the application of an independence Metropolis-Hastings sampling algorithm. We demonstrate the power of our approach by modelling the conditional bivariate density of bid and ask quote changes in a high-frequency setup. We show how to derive the implied conditional discrete density of the bid-ask spread, taking quote clusterings (at multiples of 5 ticks) into account. Copyright (C) 2009 John Wiley & Sons, Ltd.

Item Type:	Journal Article
Subjects:	H Social Sciences > HG Finance Q Science > QA Mathematics
Divisions:	Faculty of Social Sciences > Warwick Business School > Financial Econometrics Research Centre Faculty of Social Sciences > Warwick Business School > Finance Group Faculty of Social Sciences > Warwick Business School
Library of Congress Subject Headings (LCSH):	Multivariate analysis, Copulas (Mathematical statistics), Finance -- Mathematical models
Journal or Publication Title:	Journal of Applied Econometrics
Publisher:	Wiley-Blackwell Publishing, Inc
ISSN:	0883-7252
Date:	2009
Volume:	Volume 26
Number:	Number 4
Number of Pages:	39
Page Range:	pp. 669-707
Identification Number:	10.1002/jae.1122
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Restricted or Subscription Access
Funder:	European Commission (EC)
Grant number:	HPRN-CT-2002-00232 (EC)
References:	Amilon H. 2003. Garch estimation and discrete stock prices: an application to low-priced Australian stocks. Economics Letters 81: 215–222. Andersen TG, Bollerslev T, Diebold FX, Labys P. 2003. Modeling and forecasting realized volatility. Econometrica 71: 579–625. Antoniou A, Vorlow CE. 2005. Price clustering and discreteness: is there chaos behind the noise? Physica A 348: 389–403. Ball C. 1988. Estimation bias induced by discrete security prices. Journal of Finance 43: 841–865. Brock WA, Dechert WD, Scheinkman JA, LeBaron B. 1996. A test for independence based on the correlation dimension. Econometric Reviews 15: 197–235. Cameron C, Li T, Trivedi P, Zimmer D. 2004. Modelling the differences in counted outcomes using bivariate copula models with application to mismeasured counts. Econometrics Journal 7: 566–584. Cheung Y. 2002. Zero-inflated models for regression analysis of count data: a study of growth and development. Statistics in Medicine 21: 1461–1469. Chiao C, Wang ZM. 2009. Price clustering: evidence using comprehensive limit-order data. Financial Review 44: 1–29. Christie WG, Schultz P. 1994. Why do nasdaq market makers avoid odd-eighth quotes? Journal of Finance 49: 1813–1840. Christie WG, Harris J, Schultz P. 1994. Why did nasdaq market makers stop avoiding odd-eighth quotes? Journal of Finance 49: 1841–1860. Crack TF, Ledoit O. 1996. Robust structure without predictability: The ‘compass rose’ pattern of the stock market. Journal of Finance 51: 751–62. Diebold FX, Gunther TA, Tay AS. 1998. Evaluating density forecasts, with applications to financial risk management. International Economic Review 39: 863–883. Dixon MJ, Coles S. 1997. Modelling association football scores and inefficiencies in the football betting market. Applied Statistics 46: 265–280. Fang Y. 2002. The compass rose and random walk tests. Computational Statistics and Data Analysis 39: 299–310. Gleason KC, Lee CI, Mathur I. 2000. An explanation for the compass rose pattern. Economics Letters 68: 127–133. Grogger JT, Carson RT. 1991. Models for truncated counts. Journal of Applied Econometrics 6: 225–238. Grossman SJ, Miller MH, Cone KR, Fischel DR, Ross DJ. 1997. Clustering and competition in asset markets. Journal of Law and Economics 40: 23–60. Hansen PR, Lunde A. 2006. Realized variance and market microstructure noise. Journal of Business and Economic Statistics 24: 127–218. Harris LE. 1991. Stock price clustering and discreteness. Review of Financial Studies 4: 389–415. Hasbrouck J. 1999. Security bid/ask dynamics with discreteness and clustering: Simple strategies for modelling and estimation. Journal of Financial Markets 2: 1–28. Hastings WK. 1970. Monte Carlo sampling methods using Markov chains and their applications. Biometrika 57: 97–109. Huang RD, Stoll HR. 1994. Market microstructure and stock return predictions. Review of Financial Studies 7: 179–213. Jang TY. 2005. Count data models for trip generation. Journal of Transportation Engineering 131: 444–450. Johnson N, Kotz S, Balakrishnan N. 1997. Discrete Multivariate Distributions. Wiley: New York. Karlis D, Ntzoufras I. 2003. Analysis of sports data by using bivariate Poisson models. Journal of the Royal Statistical Society: Series D (The Statistician) 52: 381–393. Karlis D, Ntzoufras I. 2005. Bivariate Poisson and diagonal inflated bivariate Poisson regression models in R. Journal of Statistical Software 14: 1–36. Kocherlakota, Kocherlakota. 1992. Bivariate Discrete Distributions. Dekker: New York. Kr¨amer W, Runde R. 1997. Chaos and the compass rose. Economics Letters 54: 113–118. Lambert D. 1986. Zero-inflated Poisson regression with an application to defects in manufacturing. Technometrics 34: 1–14. Lee CI, Gleason KC, Mathur I. 1999. A comprehensive examination of the compass rose pattern in futures markets. Journal of Futures Markets 19: 541–564. Li C, Lu J, Park J, Kim K, Peterson J. 1999. Multivariate zero-inflated Poisson models and their applications. Technometrics 41: 29–38. Liesenfeld R, Nolte I, Pohlmeier W. 2006. Modelling financial transaction price movements: a dynamic integer count data model. Empirical Economics 30: 795–825. Meester S, MacKay J. 1994. A parametric model for cluster correlated categorical data. Biometrics 50: 954–963. Mullahy J. 1986. Specification and testing of some modified count data models. Journal of Econometrics 33: 341–365. Oomen RCA. 2005. Properties of bias-corrected realized variance under alternative sampling schemes. Journal of Financial Econometrics 3: 555–577. Patton AJ. 2006. Modeling asymmetric exchange rate dependence. International Economic Review 47: 527–556. Russell JR, Engle RF. 2002. Econometric analysis of discrete-valued irregularly-spaced financial transactions data. Revised version of Discussion Paper 98-10, University of California, San Diego. Rydberg T, Shephard N. 2003. Dynamics of trade-by-trade price movements: Decomposition and models. Journal of Financial Econometrics 1: 2–25. Shephard N. 1995. Generalized linear autoregressions. Working paper, Nuffield College, Oxford. Silvapulle MJ, Sen PK. 2005. Constrained Statistical Inference. Wiley: New York. Sklar A. 1959. Fonctions de r´epartition `a n dimensions et leurs marges. Public Institute of Statistics at the University of Paris 8: 229–231. Szpiro GG. 1998. Tick size, the compass rose and market nanostructure. Journal of Banking & Finance 22: 1559–1569. Vorlow CE. 2004. Stock price clustering and discreteness: the ‘compass rose’ and predictability. Working paper, University of Durham. Wahlin J. 2001. Bivariate ZIP models. Biometrical Journal 43: 147–160. Wang K, Lee A, Yau K, Carrivick P. 2003. A bivariate zero-inflated regression model to analyze occupational injuries. Accident Analysis and Prevention 35: 625–629.
URI:	http://wrap.warwick.ac.uk/id/eprint/41333

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