A Maximum Likelihood Approach for Non-Gaussian Stochastic Volatility Models
- 1 July 1998
- journal article
- research article
- Published by Taylor & Francis in Journal of Business & Economic Statistics
- Vol. 16 (3) , 284-291
- https://doi.org/10.1080/07350015.1998.10524767
Abstract
A maximum likelihood approach for the analysis of stochastic volatility models is developed. The method uses a recursive numerical integration procedure that directly calculates the marginal likelihood. Only conventional integration techniques are used, making this approach both flexible and simple. Experimentation shows that the method matches the performance of the best estimation tools currently in use. New stochastic volatility models are introduced and estimated. The model that best fits recent stock-index data is characterized by a highly non-Gaussian stochastic volatility innovation distribution. This model dominates a model that includes an autoregressive conditional heteroscedastic effect in the stochastic volatility process and a model that includes a stochastic volatility effect in the conditional mean.Keywords
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