David Veredas (Eds.)
High Frequency
Financial
Econometrics
Recent Developments
With 57 Figures and 64 Tables
Physica-Verlag
A Springer Company
High Frequency Financial Econometrics
Recent Developments
Prof. Winfried Pohlmeier
Department of Economics
University of Konstanz
78457 Konstanz
Germany
winfried.pohlmeier@uni-konstanz.de
Prof. Luc Bauwens
CORE
Voie du Roman Pays
1348 Louvain-la-Neuve
Belgium
bauwens@ucl.ac.be
Prof. David Veredas
ECARES
´
Universite Libre des Bruxelles
30, Avenue Roosevelt
1050 Brussels
Belgium
dveredas@ulb.ac.be
Parts of the papers have been first published in
“
“Empirical Economics, Vol. 30, No. 4, 2006
Library of Congress Control Number: 2007933836
ISBN 978-3-7908-1991-5 Physica-Verlag Heidelberg New York
This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilm or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Physica-Verlag. Violations are liable for prosecution under the GermanCopyright Law.
PhysicaVerlag is a part of SpringerScience+Business Media springer.com © Physica-Verlag Heidelberg 2008
The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use.
A
Typesetting by the author and SPi using a Springer L TEX macro package
Cover-design: WMX design GmbH, Heidelberg
Printed on acid-free paper SPIN : 12107759
88/SPi
543210
References: Amilon H (2003) GARCH estimation and discrete stock prices: an application to low-priced Australian stocks Andersen TG, Bollerslev T, Diebold FX, Labys P (1999) (Understanding, optimizing, using and forecasting) realized volatility and correlation, New York University, Leonard N School Finance Department Working Paper, No. 99–061 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 Brock WA, Dechert WD, Scheinkman JA, LeBaron B (1996) A test for independence based on the correlation dimension Cameron C, Li T, Trivedi P, Zimmer D (2004) Modelling the differences in counted outcomes using bivariate copula models with application to mismesured counts Crack TF, Ledoit O (1996) Robust structure without predictability: the “compass rose” pattern of the stock market Denuit M, Lambert P (2005) Constraints on concordance measures in bivariate discrete data. J Multivariate Anal 93:40–57 Diebold FX, Gunther TA, Tay AS (1998) Evaluating density forecasts, with applications to financial risk management Fang Y (2002) The compass rose and random walk tests. Comput Stat Data Anal 39:299–310 Gleason KC, Lee CI, Mathur I (2000) An explanation for the compass rose pattern 68(2):127–133 Hansen PR, Lunde A (2006) Realized variance and market microstructure noise 24:127–218 Heinen A, Rengifo E (2003) Multivariate autoregressive modelling of time series count data Huang RD, Stoll HR (1994) Market microstructure and stock return predictions. Rev Financ Stud 7(1):179–213 Johnson N, Kotz S, Balakrishnan N (1997) Discrete multivariate distributions. Wiley, New York Kocherlakota S, Kocherlakota K (1992) Bivariate discrete distributions Krämer W, Runde R (1997) Chaos and the compass rose. Econ Lett 54(2):113–118 Lee CI, Gleason KC, Mathur I (1999) A comprehensive examination of the compass rose pattern in futures markets. J Futures Mark 19(5):541–564 Liesenfeld R, Nolte I, Pohlmeier W (2006) Modelling financial transaction price movements: a dynamic integer count data model. Empir Econ 30:795–825 Meester S, MacKay J (1994) A parametric model for cluster correlated categorical data. Biometrics 50:954–963 Oomen RCA (2005) Properties of bias-corrected realized variance under alternative sampling schemes. J Financ Econ 3:555–577 Patton A (2001) Modelling time-varying exchange rate dependence using the conditional copula. Discussion Paper, UCSD Department of Economics Russell JR, Engle RF (2002) Econometric analysis of discrete-valued irregularly-spaced financial Shephard N (1995) Generalized linear autoregressions. Working Paper, Nuffield College, Oxford Sklar A (1959) Fonctions de répartition à n dimensions et leurs marges Statistics at the University of Paris 8:229–231 Stevens W (1950) Fiducial limits of the parameter of a discontinuous distribution 37:117–129 Szpiro GG (1998) Tick size, the compass rose and market nanostructure 22(12):1559–1569 Vorlow CE (2004) Stock price clustering and discreteness: the “compass rose” and predictability. model extends the vector autoregressive (VAR) model introduced by Hasbrouck (Hasbrouck J (1991) Measuring the information content of stock trades Departament of Economics, Universidad Carlos III de Madrid, C/Madrid 126, Getafe, 28903 Madrid, Spain