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Multivariate Stochastic Volatility, Manabu Asai, Michael Mcaleer, Jun Yu
Multivariate Stochastic Volatility, Manabu Asai, Michael Mcaleer, Jun Yu
Research Collection School Of Economics
The literature on multivariate stochastic volatility (MSV) models has developed significantly over the last few years. This paper reviews the substantial literature on specification, estimation and evaluation of MSV models. A wide range of MSV models is presented according to various categories, namely (i) asymmetric models; (ii) factor models; (iii) time-varying correlation models; and (iv) alternative MSV specifications, including models based on the matrix exponential transformation, Cholesky decomposition, Wishart autoregressive process, and the empirical range. Alternative methods of estimation, including quasi-maximum likelihood, simulated maximum likelihood, Monte Carlo likelihood, and Markov chain Monte Carlo methods, are discussed and compared. Various methods …
Multivariate Stochastic Volatility Models: Bayesian Estimation And Model Comparison, Jun Yu, Renate Meyer
Multivariate Stochastic Volatility Models: Bayesian Estimation And Model Comparison, Jun Yu, Renate Meyer
Research Collection School Of Economics
In this paper we show that fully likelihood-based estimation and comparison of multivariate stochastic volatility (SV) models can be easily performed via a freely available Bayesian software called WinBUGS. Moreover, we introduce to the literature several new specifications that are natural extensions to certain existing models, one of which allows for time-varying correlation coefficients. Ideas are illustrated by fitting, to a bivariate time series data of weekly exchange rates, nine multivariate SV models, including the specifications with Granger causality in volatility, time-varying correlations, heavy-tailed error distributions, additive factor structure, and multiplicative factor structure. Empirical results suggest that the best specifications …
Multivariate Stochastic Volatility: A Review, Manabu Asai, Michael Mcaleer, Jun Yu
Multivariate Stochastic Volatility: A Review, Manabu Asai, Michael Mcaleer, Jun Yu
Research Collection School Of Economics
The literature on multivariate stochastic volatility (MSV) models has developed significantly over the last four years. This paper reviews the substantial literature on specification, estimation, and evaluation of MSV models. A wide range of MSV models is presented according to various categories, namely, (i) asymmetric models, (ii) factor models, (iii) time-varying correlation models, and (iv) alternative MSV specifications, including models based on the matrix exponential transformation, the Cholesky decomposition, and the Wishart autoregressive process. Alternative methods of estimation, including quasi-maximum likelihood, simulated maximum likelihood, and Markov chain Monte Carlo methods, are discussed and compared. Various methods of diagnostic checking and …