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Financial Risk Management with Bayesian Estimation of GARCH Models by David Ardia
Financial Risk Management with Bayesian Estimation of GARCH Models by David Ardia
The methods for the Bayesian estimation of single-regime and switching-regime GARCH models are presented in depth in this book. These models, which are common and crucial tools in nancial econometrics, were mostly estimated using the traditional Maximum Likelihood method until recently. The Bayesian technique offers a compelling alternative that allows for small sample findings, robust estimates, model discrimination, and probabilistic claims on nonlinear functions of the model parameters, as this work tries to show. The production of this work required assistance from many people, which the author is grateful for. I owe a lot to Prof. Dr. Philippe J. Deschamps, who first and foremost motivated me to study Bayesian econometrics, recommended the topic, supervised my work, and supported my research. For their insightful remarks and conversations, I also like to thank Prof. Dr. Martin Wallmeier and my colleagues from the Department of Quantitative Economics, especially Michael Beer, Roberto Cerratti, and Gilles Kaltenrieder. For their assistance in the fields of economics, mathematics, and statistics, my friends Carlos Orda as Criado, Julien A. Straubhaar, Jerôme Ph. A. Taillard, and Mathieu Vuilleumier deserve the utmost gratitude. My buddy Kevin Barnes, who assisted me with my English for this piece, is also owed gratitude. Last but not least, I want to express my gratitude to my parents and grandparents for their help and support while I struggled to write my thesis.
Review
The book applies Bayesian principles to financial risk management, according to the reviewers. The book is nicely written, has a thorough list of references, and has an index that makes it quite simple to navigate between its many themes. Graduate students and researchers that study financial econometrics and/or quantitative risk management may find this book to be extremely helpful. In conclusion, the book is nicely structured and gives a detailed analysis of the Bayesian estimation of GARCH-like models and its application to risk management. Mathematical Reviews, Issue 2010b, Yannick Malevergne
Printed on the back cover
David Ardia received the 2008 Chorafas medal from the University of Fribourg in Switzerland for his outstanding monograph.
The methods for Bayesian estimation of GARCH models and their application to financial risk management are presented in this book. Due to the benefits of the Bayesian method, particularly the ability to acquire small-sample results and integrate these results in a formal decision model, the study of these models from a Bayesian standpoint is very recent and can be regarded extremely promising. The work is introduced in the first two chapters, which also provide an introduction of the Bayesian paradigm for inference. The estimate of the GARCH model with normal innovations and the linear regression models with conditionally normal and Student-t-GJR errors are covered in the next three chapters. The sixth chapter demonstrates how agents can choose their best Value at Risk Bayesian point estimate when faced with various risk perspectives and provides evidence that individual variances in regulatory capital can be rather significant. The estimate of a Markov-switching GJR model is suggested in the last chapter.
Salepage : Financial Risk Management with Bayesian Estimation of GARCH Models by David Ardia
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