Maximum likelihood estimation of stochastic volatility models

G. Sandmann and Siem Jan Koopman

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

Abstract: This paper discusses the Monte Carlo maximum likelihood method of estimating stochastic volatility (SV) models. The basic SV model can be expressed as a linear state space model with log chi-square disturbances. The likelihood function can be approximated arbitrarily accurately by decomposing it into a Gaussian part, constructed by the Kalman filter, and a remainder function, whose expectation is evaluated by simulation. No modifications of this estimation procedure are required when the basic SV model is extended in a number of directions likely to arise in applied empirical research. This compares favorably with alternative approaches. The finite sample performance of the new estimator is shown to be comparable to the Monte Carlo Markov chain (MCMC) method.

Keywords: GARCH model; importance sampling; Kalman filter smoother; Monte Carlo simulation; quasi-maximum; stochastic volatility; unobserved components (search for similar items in EconPapers)
JEL-codes: C22 G12 (search for similar items in EconPapers)
Pages: 31 pages
Date: 1996-06-01
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http://eprints.lse.ac.uk/119161/ Open access version. (application/pdf)

Related works:
Working Paper: Maximum Likelihood Estimation of Stochastic Volatility Models (1996)
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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:119161

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