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- W2389772972 abstract "To capture changes in volatility which is caused by economic forces,market events and so on we studies the Markov switching stochastic volatility model with heavy-tails and its applications in this paper.However,because of the two hidden levels of the latent variable and the nonlinear sate space,it's hard to get the analytical evolution of the model.In the Bayesian perspective we applied the auxiliary particle filtering algorithm to filtering the hidden states and estimated the parameters.We focused on the effect of tails' heaviness and got the conclusion that the APF algorithm performed more efficient when the MSSV model with heavier tails.The model was also applied to a real financial time series: the Shanghai Composite Index and showed the ability to capture the different volatility regimes and to improve the estimation precise." @default.
- W2389772972 created "2016-06-24" @default.
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- W2389772972 date "2009-01-01" @default.
- W2389772972 modified "2023-09-23" @default.
- W2389772972 title "Bayesian Auxiliary Particle Filtering for Markov Switching Stochastic Volatility Models with Heavy-tails" @default.
- W2389772972 hasPublicationYear "2009" @default.
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