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Full-Text Articles in Social and Behavioral Sciences
Multivariate Stochastic Volatility Models Based On Generalized Fisher Transformation, Han Chen, Yijie Fei, Jun Yu
Multivariate Stochastic Volatility Models Based On Generalized Fisher Transformation, Han Chen, Yijie Fei, Jun Yu
Research Collection School Of Economics
Modeling multivariate stochastic volatility (MSV) can be challenging, particularly when both variances and covariances are time-varying. In this paper, we address these challenges by introducing a new MSV model based on the generalized Fisher transformation of Archakov and Hansen (2021). Our model is highly exible and ensures that the variance-covariance matrix is always positive-definite. Moreover, our approach separates the driving factors of volatilities and correlations. To conduct Bayesian analysis of the model, we use a Particle Gibbs Ancestor Sampling (PGAS) method, which facilitates Bayesian model comparison. We also extend our MSV model to cover the leverage effect in volatilities and …
Improved Marginal Likelihood Estimation Via Power Posteriors And Importance Sampling, Yong Li, Nianling Wang, Jun Yu
Improved Marginal Likelihood Estimation Via Power Posteriors And Importance Sampling, Yong Li, Nianling Wang, Jun Yu
Research Collection School Of Economics
Power posteriors have become popular in estimating the marginal likelihood of a Bayesian model. A power posterior is referred to as the posterior distribution that is proportional to the likelihood raised to a power b∈[0,1]. Important power-posterior-based algorithms include thermodynamic integration (TI) of Friel and Pettitt (2008) and steppingstone sampling (SS) of Xie et al. (2011). In this paper, it is shown that the Bernstein–von Mises (BvM) theorem holds for power posteriors under regularity conditions. Due to the BvM theorem, power posteriors, when adjusted by the square root of the auxiliary constant, have the same limit distribution as the original …