Simulation-based sequential analysis of Markov switching stochastic volatility models

Authors:
Carlos M. Carvalho;Hedibert F. Lopes
Affiliations:
ISDS-Duke University, NC, USA and GSB-University of Chicago, IL, USA;ISDS-Duke University, NC, USA and GSB-University of Chicago, IL, USA
Venue:
Computational Statistics & Data Analysis
Year:
2007

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Abstract

We propose a simulation-based algorithm for inference in stochastic volatility models with possible regime switching in which the regime state is governed by a first-order Markov process. Using auxiliary particle filters we developed a strategy to sequentially learn about states and parameters of the model. The methodology is tested against a synthetic time series and validated with a real financial time series: the IBOVESPA stock index (Sao Paulo Stock Exchange).