Ancillarity-Sufficiency Interweaving Strategy (ASIS) for Boosting MCMC Estimation of Stochastic Volatility Models

Kastner, Gregor and Frühwirth-Schnatter, Sylvia (2013) Ancillarity-Sufficiency Interweaving Strategy (ASIS) for Boosting MCMC Estimation of Stochastic Volatility Models. Research Report Series / Department of Statistics and Mathematics, 121. WU Vienna University of Economics and Business, Vienna.


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Bayesian inference for stochastic volatility models using MCMC methods highly depends on actual parameter values in terms of sampling efficiency. While draws from the posterior utilizing the standard centered parameterization break down when the volatility of volatility parameter in the latent state equation is small, non-centered versions of the model show deficiencies for highly persistent latent variable series. The novel approach of ancillarity-sufficiency interweaving has recently been shown to aid in overcoming these issues for a broad class of multilevel models. In this paper, we demonstrate how such an interweaving strategy can be applied to stochastic volatility models in order to greatly improve sampling efficiency for all parameters and throughout the entire parameter range. Moreover, this method of "combining best of different worlds" allows for inference for parameter constellations that have previously been infeasible to estimate without the need to select a particular parameterization beforehand.

Item Type: Paper
Additional Information: With minor editorial changes, this article is published as: KASTNER, G. & FRÜHWIRTH-SCHNATTER, S. (2014). Ancillarity-sufficiency interweaving strategy (ASIS) for boosting MCMC estimation of stochastic volatility models. Computational Statistics and Data Analysis 76, 408-423. Access to the published version requires a subscription.
Keywords: Markov Chain Monte Carlo / Non-Centering / Auxiliary Mixture Sampling / Massively Parallel Computing / State Space Model / Exchange Rate Data
Classification Codes: RVK SK 820, QH 400
Divisions: Departments > Finance, Accounting and Statistics > Statistics and Mathematics
Depositing User: ePub Administrator
Date Deposited: 31 Jan 2013 08:43
Last Modified: 22 Oct 2019 00:41


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