STOCHASTIC VOLATILITY: LIKELIHOOD INFERENCE AND COMPARISON WITH ARCH MODELS

Sangjoon Kim, Neil Shephard () and Siddhartha Chib
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Sangjoon Kim: Salomon Brothers Asia Limited, Tokyo, Japan
Siddhartha Chib: Washington University, St. Louis

Econometrics from University Library of Munich, Germany

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.

Keywords: Bayes estimation; Bayes factors; GARCH; Gibbs sampler; Heteroscedasticity; Maximum~likelihood; Likelihood ratio; Markov chain Monte Carlo; Quasi-maximum likelihood; Simulation; Stochastic EM algorithm; Stochastic volatility; Stock returns. (search for similar items in EconPapers)
JEL-codes: C1 C2 C3 C4 C5 C8 (search for similar items in EconPapers)
Date: 1996-10-07
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Citations: View citations in EconPapers (14)

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Related works:
Journal Article: Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models (1998)
Working Paper: Stochastic volatility: likelihood inference and comparison with ARCH models (1994)
Working Paper: Stochastic volatility: likelihood inference and comparison with ARCH models
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Persistent link: https://EconPapers.repec.org/RePEc:wpa:wuwpem:9610002

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