Multivariate Stochastic Volatility Models: Bayesian Estimation and Model Comparison
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 extensio...
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| Main Authors: | , |
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| Format: | text |
| Language: | English |
| Published: |
Institutional Knowledge at Singapore Management University
2006
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| Subjects: | |
| Online Access: | https://ink.library.smu.edu.sg/soe_research/360 https://ink.library.smu.edu.sg/context/soe_research/article/1359/viewcontent/Multivariate_Stochastic_Volatility_Models_Bayesian_Estimation.pdf |
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| Institution: | Singapore Management University |
| Language: | English |
| Summary: | 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 are those that allow for time-varying correlation coefficients. |
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