scholarly journals Analyzing Commodity Futures Using Factor State-Space Models with Wishart Stochastic Volatility

2019 ◽  
Author(s):  
Tore Selland Kleppe ◽  
Roman Liesenfeld ◽  
Guilherme Valle Moura ◽  
Atle Oglend
Keyword(s):  
State Space ◽  
Stochastic Volatility ◽  
State Space Models ◽  
Commodity Futures ◽  
Factor State

An effcient exact Bayesian method For state space models with stochastic volatility

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Yu-Fan Huang
Keyword(s):  
State Space ◽  
Stochastic Volatility ◽  
Linear Models ◽  
State Space Models ◽  
Acceptance Probability ◽  
Indicator Variable ◽  
Vector Autoregressive ◽  
Unobserved Component ◽  
Key Innovation ◽  
Unobserved Component Model

AbstractThis paper introduces a Bayesian MCMC method, referred to as a marginalized mixture sampler, for state space models whose disturbances follow stochastic volatility processes. The marginalized mixture sampler is based on a mixture-normal approximation of the log-χ2 distribution, but it is implemented without the need to simulate the mixture indicator variable. The key innovation is to use the filter ing scheme developed by Kim (Kim C.-J. 1994. “Dynamic Linear Models with Markov-Switching.” Journal of Econometrics 60: 1–22.) and the forward-filtering backward-sampling algorithm to generate a proposal series of the latent stochastic volatility process. The proposal series is then accepted according to the Metropolis-Hastings acceptance probability. The new sampler is examined within an unobserved component model and a time-varying parameter vector autoregressive model, and it reduces substantially the correlations between MCMC draws.

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