Financial risk management with Bayesian estimation of GARCH models. Theory and applica\-tions.
This book presents methodologies for the Bayesian estimation of GARCH models and their application to financial risk management. The author shows that the Bayesian approach offers an attractive alternative of the maximum likelihood technique which is traditionally used for estimating GARCH models. The first two chapters give an overview of the Bayesian paradigm for inference. The next three describe the estimation of GARCH models with normal innovations and linear regression models with conditionally normal and Student-t-GJR errors. The sixth chapter shows how agents facing different risk perspectives can select their optimal VaR Bayesian point estimate and documents that differences between individuals can be substantial in terms of the regulatory capital. The last chapter proposes the estimation of a Markov-switching GJR model.
scientific article; zbMATH DE number 5281734