Bayesian semiparametric multivariate GARCH modeling

Arbeitspapier

This paper proposes a Bayesian nonparametric modeling approach for the return distribution in multivariate GARCH models. In contrast to the parametric literature, the return distribution can display general forms of asymmetry and thick tails. An infinite mixture of multivariate normals is given a flexible Dirichlet process prior. The GARCH functional form enters into each of the components of this mixture. We discuss conjugate methods that allow for scale mixtures and nonconjugate methods, which provide mixing over both the location and scale of the normal components. MCMC methods are introduced for posterior simulation and computation of the predictive density. Bayes factors and density forecasts with comparisons to GARCH models with Student-t innovations demonstrate the gains from our flexible modeling approach.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Working Paper ; No. 2012-9

Classification and Topics

• Classification: Wirtschaft ; Bayesian Analysis: General ; Semiparametric and Nonparametric Methods: General ; Forecasting Models; Simulation Methods ; Financial Econometrics
• Subject: Bayesian nonparametrics ; cumulative Bayes factor ; Dirichlet process mixture ; forecasting ; infinite mixture model ; MCMC ; slice sampler

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Jensen, Mark J. ; Maheu, John M.
• Event: Veröffentlichung
• (who): Federal Reserve Bank of Atlanta
• (where): Atlanta, GA
• (when): 2012

Further information

• Handle: http://hdl.handle.net/10419/70636
• Last update: 10.03.2025, 11:44 AM CET