This book presents in detail methodologies for the Bayesian estimation
of sing- regime and regime-switching GARCH models. These models are
widespread and essential tools in n ancial econometrics and have, until
recently, mainly been estimated using the classical Maximum Likelihood
technique. As this study aims to demonstrate, the Bayesian approach o
ers 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 er ^ ome Ph. A. Taillard and Mathieu
Vuilleumier, for their support in the elds 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 was struggling
with the writing of this thesis.