Particle Filters for Markov-Switching Stochastic Volatility Models
Keyword(s):
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.
Proceedings of the ISCIE International Symposium on Stochastic Systems Theory and its Applications
◽
2008 ◽
Vol 2008
(0)
◽
pp. 8-13
◽
2014 ◽
Vol 21
(8)
◽
pp. 923-927
◽
2007 ◽
Vol 34
(5-6)
◽
pp. 1002-1024
◽
2017 ◽
Vol 74
◽
pp. 46-62
◽
Keyword(s):
2020 ◽
Vol 13
◽
pp. 84-105
◽
Keyword(s):
2002 ◽
Vol 108
(2)
◽
pp. 281-316
◽