Stochastic Volatility: Likelihood Inference and Comparison with Arch Models

Posted: 15 Oct 1996

See all articles by Sangjoon Kim

Sangjoon Kim

affiliation not provided to SSRN

Neil Shephard

Harvard University

Siddhartha Chib

Washington University in St. Louis - John M. Olin Business School

Date Written: Undated

Abstract

In this paper, Markov chain Monte Carlo sampling methods are exploited to provide a unified, practical likelihood-based framework for the analysis of stochastic volatility models. A highly effective method is developed that samples all the unobserved volatilities at once using an approximating offset mixture model, followed by an importance reweighting procedure. This approach is compared with several alternative methods using real data. The paper also develops simulation-based methods for filtering, likelihood evaluation and model failure diagnostics. The issue of model choice using non-nested likelihood ratios and Bayes factors is also investigated. These methods are used to compare the fit of stochastic volatility and GARCH models. All the procedures are illustrated in detail.

JEL Classification: C15, C51

Suggested Citation

Kim, Sangjoon and Shephard, Neil and Chib, Siddhartha, Stochastic Volatility: Likelihood Inference and Comparison with Arch Models (Undated). Available at SSRN: https://ssrn.com/abstract=4320

Sangjoon Kim

affiliation not provided to SSRN

Neil Shephard

Harvard University ( email )

1875 Cambridge Street
Cambridge, MA 02138
United States

Siddhartha Chib (Contact Author)

Washington University in St. Louis - John M. Olin Business School ( email )

One Brookings Drive
Campus Box 1133
St. Louis, MO 63130-4899
United States
314-935-4657 (Phone)
314-935-6359 (Fax)

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