scholarly journals A t-distribution based particle filter for univariate and multivariate stochastic volatility models

2015 ◽  
Vol 34 (2) ◽  
pp. 227-242
Author(s):  
E.B. Nkemnole ◽  
O. Abass
Keyword(s):  
Particle Filter ◽  
Stochastic Volatility ◽  
Stochastic Volatility Models ◽  
Volatility Models ◽  
Multivariate Stochastic Volatility ◽  
T Distribution

scholarly journals Particle Filters for Markov-Switching Stochastic Volatility Models

2018 ◽  
Author(s):  
Yun Bao ◽  
Carl Chiarella ◽  
Boda Kang
Keyword(s):  
Markov Chain ◽  
Particle Filter ◽  
Stochastic Volatility ◽  
Regime Switching ◽  
Transition Probabilities ◽  
Dirichlet Distribution ◽  
Stochastic Volatility Models ◽  
Volatility Models ◽  
Auxiliary Particle ◽  
Auxiliary Particle Filter

This chapter 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. It 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 that demonstrates the ability 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 in order to analyze a real-time series, namely, the foreign exchange rate between the Australian dollar and the South Korean won.

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