Simulation-based sequential analysis of Markov switching stochastic volatility models |
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Authors: | Carlos M Carvalho Hedibert F Lopes |
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Affiliation: | a ISDS-Duke University, NC, USA b GSB-University of Chicago, IL, USA |
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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 (São Paulo Stock Exchange). |
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Keywords: | Bayesian time series Bayes factor Markov chain Monte Carlo Particle filters Sequential analysis Stochastic volatility models |
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