GARCH volatility models with fixed parameters are too restrictive for long time series due to breaks in the volatility process. Flexible alternatives are Markov-switching GARCH and change-point GARCH models. They require estimation by MCMC methods due to the path dependence problem. An unsolved issue is the computation of their marginal likelihood, which is essential for determining the number of regimes or change-points. We solve the problem by using particle MCMC, a technique proposed by Andrieu, Doucet, and Holenstein (2010). We examine the performance of this new method on simulated data, and we illustrate its use on several return series
Efficient simulation techniques for Bayesian inference on Markov-switching (MS) GARCH models are dev...
Efficient simulation techniques for Bayesian inference on Markov-switching (MS) GARCH models are dev...
The instability of volatility parameters in GARCH models is an important issue for analyzing financi...
GARCH volatility models with fixed parameters are too restrictive for long time series due to breaks...
GARCH volatility models with fixed parameters are too restrictive for long time series due to breaks...
GARCH volatility models with fixed parameters are too restrictive for long time series due to breaks...
Generalized Auto-regressive Conditional Heteroskedastic (GARCH) models with fixed parameters are typ...
We develop a Markov-switching GARCH model (MS-GARCH) wherein the conditional mean and variance switc...
We develop a Markov-switching GARCH model (MS-GARCH) wherein the conditional mean and variance switc...
Regime Switching models, especially Markov switching models, are regarded as a promising way to capt...
Regime switching models, especially Markov switching models, are regarded as a promising way to capt...
This paper is devoted to show duality in the estimation of Markov Switching (MS) GARCH processes. It...
Dynamic volatility and correlation models with fixed parameters are restrictive for time series subj...
Change-point models are useful for modeling times series subject to structural breaks. For interpret...
We present an estimation and forecasting method, based on a differential evolution MCMC method, for ...
Efficient simulation techniques for Bayesian inference on Markov-switching (MS) GARCH models are dev...
Efficient simulation techniques for Bayesian inference on Markov-switching (MS) GARCH models are dev...
The instability of volatility parameters in GARCH models is an important issue for analyzing financi...
GARCH volatility models with fixed parameters are too restrictive for long time series due to breaks...
GARCH volatility models with fixed parameters are too restrictive for long time series due to breaks...
GARCH volatility models with fixed parameters are too restrictive for long time series due to breaks...
Generalized Auto-regressive Conditional Heteroskedastic (GARCH) models with fixed parameters are typ...
We develop a Markov-switching GARCH model (MS-GARCH) wherein the conditional mean and variance switc...
We develop a Markov-switching GARCH model (MS-GARCH) wherein the conditional mean and variance switc...
Regime Switching models, especially Markov switching models, are regarded as a promising way to capt...
Regime switching models, especially Markov switching models, are regarded as a promising way to capt...
This paper is devoted to show duality in the estimation of Markov Switching (MS) GARCH processes. It...
Dynamic volatility and correlation models with fixed parameters are restrictive for time series subj...
Change-point models are useful for modeling times series subject to structural breaks. For interpret...
We present an estimation and forecasting method, based on a differential evolution MCMC method, for ...
Efficient simulation techniques for Bayesian inference on Markov-switching (MS) GARCH models are dev...
Efficient simulation techniques for Bayesian inference on Markov-switching (MS) GARCH models are dev...
The instability of volatility parameters in GARCH models is an important issue for analyzing financi...
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