Download This PaperParticle Filters for Markov Switching Stochastic Volatility Models
Research Paper Number: 299, Quantitative Finance Research Centre, University of Technology, Sydney
21 Pages Posted: 23 Oct 2012
Date Written: January 18, 2012
Abstract
This paper proposes an auxiliary particle filter algorithm for inference in regime switching stochastic volatility models in which the regime state is governed by a first-order Markov chain. We proposes an ongoing updated Dirichlet distribution to estimate the transition probabilities of the Markov chain in the auxiliary particle filter. A simulation-based algorithm is presented for the method which demonstrated that we are able to estimate a class of models in which the probability that the system state transits from one regime to a different regime is relatively high. The methodology is implemented to analyze a real time series: the foreign exchange rate of Australian dollars vs South Korean won.
Keywords: particle filters, Markov switching stochastic volatility models, sequential Monte Carlo simulation
JEL Classification: C61, D11
Suggested Citation: Suggested Citation
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