Financial Risk Management with Bayesian Estimation of GARCH Models T
For his excellent monograph, David Ardia won the Chorafas prize 2008 at the University of Fribourg Switzerland. This book presents methodologies for the Bayesian estimation of GARCH models and their application to financial risk management. The study of t
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David Ardia
Financial Risk Management with Bayesian Estimation of GARCH Models Theory and Applications
Dr. David Ardia Department of Quantitative Economics University of Fribourg Bd. de Pérolles 90 1700 Fribourg Switzerland [email protected]
ISBN 978-3-540-78656-6
e-ISBN 978-3-540-78657-3
DOI 10.1007/978-3-540-78657-3 Lecture Notes in Economics and Mathematical Systems ISSN 0075-8442 Library of Congress Control Number: 2008927201 © 2008 Springer-Verlag Berlin Heidelberg This book is the Ph.D. dissertation with the original title “Bayesian Estimation of Single-Regime and Regime-Switching GARCH Models. Applications to Financial Risk Management” presented to the Faculty of Economics and Social Sciences at the University of Fribourg Switzerland by the author. Accepted by the Faculty Council on 19 February 2008. The Faculty of Economics and Social Sciences at the University of Fribourg Switzerland neither approves nor disapproves the opinions expressed in a doctoral dissertation. They are to be considered those of the author. (Decision of the Faculty Council of 23 January 1990). A X. Copyright © 2008 David Ardia. All rights reserved. Typeset with LT E 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.
Production: le-tex Jelonek, Schmidt & Vöckler GbR, Leipzig Cover design: WMX Design GmbH, Heidelberg Printed on acid-free paper 987654321 springer.com
To my nonno, Riziero.
Preface
This book presents in detail methodologies for the Bayesian estimation of singleregime and regime-switching GARCH models. These models are widespread and essential tools in financial econometrics and have, until recently, mainly been estimated using the classical Maximum Likelihood technique. As this study aims to demonstrate, the Bayesian approach offers an attractive alternative which enables small sample results, robust estimation, model discrimination and probabilistic statements on nonlinear functions of the model parameters. The author is indebted to numerous individuals for help in the preparation of this study. Primarily, I owe a great debt to Prof. Dr. Philippe J. Deschamps who inspired me to study Bayesian econometrics, suggested the subject, guided me under his supervision and encouraged my research. I would also like to thank Prof. Dr. Martin Wallmeier and my colleagues of the Department of Quantitative Economics, in particular Michael Beer, Roberto Cerratti and Gilles Kaltenrieder, for their useful comments and discussions. I am very indebted to my friends Carlos Ord´as Criado, Julien A. Straubhaar, J´erˆ ome Ph. A. Taillard and Mathieu Vuilleumier, for their support in the fields of economics, mathematics and statistics. Thanks also to my friend Kevin Barnes who helped with my English in this work. Finally, I am greatly indebted to my parents and grandparents for their support and encouragement while I wa
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