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.1998 Elsevier Science S.A. All rights reserved.JEL classification: C15; C22

There has been considerable interest, both academic and regulatory, in the hypothesis that the higher is the volume in the futures market, the greater is the destabilizing effect on the stock market. We show that conventional approaches, such as adding exogenous variables to GARCH models, may lead to false inferences in tests of this question. Using a stochastic volatility model, we show that, contrary to regulatory concern and the results of other papers, contemporaneous informationless futures market trading has no significant effect on spot market volatility. Copyright Blackwell Publishers Ltd 2001.