Financial Risk Management with Bayesian Estimation of GARCH Models: Theory and Applications
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This book presents in detail methodologies for the Bayesian
estimation of single-regime 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 first two chapters introduce the work
and give a short overview of the Bayesian paradigm for inference.
The next three chapters describe the estimation of the GARCH model
with Normal innovations and the linear regression models with
conditionally Normal and Student-t-GJR errors. For these models, we
compare the Bayesian and Maximum Likelihood approaches based on real
financial data. In particular, we document that even for fairly
large data sets, the parameter estimates and confidence intervals
are different between the methods. Caution is therefore in order
when applying asymptotic justifications for this class of models.
The sixth chapter presents some financial applications of the
Bayesian estimation of GARCH models. We show how agents facing
different risk perspectives can select their optimal VaR point
estimate and document that the differences between individuals can
be substantial in terms of regulatory capital. Finally, the last
chapter proposes the estimation of the Markov-switching GJR model.
An empirical application documents the in- and out-of-sample
superiority of the regime-switching specification compared to
single-regime GJR models. We propose a methodology to depict the
density of the one-day ahead VaR and document how specific
forecasters’ risk perspectives can lead to different conclusions on
the forecasting performance of the MS-GJR model. |
| Mots-clés |
Bayesian, MCMC, GARCH, GJR, Markov-switching, Value at Risk, Expected Shortfall, Bayes factor, DIC |
| Citation | Ardia, D. (2008). Financial Risk Management with Bayesian Estimation of GARCH Models: Theory and Applications (Vol. 612). Heidelberg: Springer. |
| Type | Livre (Anglais) |
| Année | 2008 |
| Titre de la collection | Lecture Notes in Economics and Mathematical Systems |
| Editeur commercial | Springer (Heidelberg) |
| Volume | 612 |
| Nombre de pages | 206 |
| URL | http://www.springer.com/us/book/9783540786566 |
| Liée au projet | Bayesian estimation of regime-switching GARCH models |