Bayesian Inference for Generalised Markov Switching Stochastic Volatility Models

CEREMADE Journal Working Paper No.0414

47 Pages Posted: 13 Mar 2006

See all articles by Roberto Casarin

Roberto Casarin

University Ca' Foscari of Venice - Department of Economics

Date Written: 2004

Abstract

We study a Markov switching stochastic volatility model with heavy tail innovations in the observable process. Due to the economic interpretation of the hidden volatility regimes, these models have many financial applications like asset allocation, option pricing and risk management. The Markov switching process is able to capture clustering effects and jumps in volatility. Heavy tail innovations account for extreme variations in the observed process. Accurate modelling of the tails is important when estimating quantiles is the major interest like in risk management applications. Moreover we follow a Bayesian approach to filtering and estimation, focusing on recently developed simulation based filtering techniques, called Particle Filters. Simulation based filters are recursive techniques, which are useful when assuming non-linear and non-Gaussian latent variable models and when processing data sequentially. They allow to update parameter estimates and state filtering as new observations become available.

Keywords: Particle Filter, Markov Switching, Stochastic Volatility, Heavy Tails

JEL Classification: C11, C15, C50, C63

Suggested Citation

Casarin, Roberto, Bayesian Inference for Generalised Markov Switching Stochastic Volatility Models (2004). CEREMADE Journal Working Paper No.0414, Available at SSRN: https://ssrn.com/abstract=739790 or http://dx.doi.org/10.2139/ssrn.739790

Roberto Casarin (Contact Author)

University Ca' Foscari of Venice - Department of Economics ( email )

San Giobbe 873/b
Venice, 30121
Italy
+39 030.298.91.49 (Phone)
+39 030.298.88.37 (Fax)

HOME PAGE: http://sites.google.com/view/robertocasarin

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