Maximum likelihood estimation of stochastic volatility models

Sandmann, G. and Koopman, Siem (1996) Maximum likelihood estimation of stochastic volatility models. Financial Markets Group Discussion Papers (248). Financial Markets Group, The London School of Economics and Political Science, London, UK.

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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.

Item Type:	Monograph (Discussion Paper)
Official URL:	https://www.fmg.ac.uk/
Additional Information:	© 1996 The Authors
Divisions:	Financial Markets Group
Subjects:	H Social Sciences > HC Economic History and Conditions H Social Sciences > HG Finance
JEL classification:	G - Financial Economics > G1 - General Financial Markets > G12 - Asset Pricing; Trading volume; Bond Interest Rates C - Mathematical and Quantitative Methods > C2 - Econometric Methods: Single Equation Models; Single Variables > C22 - Time-Series Models
Date Deposited:	05 Jun 2023 12:45
Last Modified:	14 Sep 2024 04:34
URI:	http://eprints.lse.ac.uk/id/eprint/119161

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