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Simulated maximum likelihood for general stochastic volatility models: a change of variable approach

Kleppe, Tore Selland and Skaug, Hans J. (2008): Simulated maximum likelihood for general stochastic volatility models: a change of variable approach.

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Abstract

Maximum likelihood has proved to be a valuable tool for fitting the log-normal stochastic volatility model to financial returns time series. Using a sequential change of variable framework, we are able to cast more general stochastic volatility models into a form appropriate for importance samplers based on the Laplace approximation. We apply the methodology to two example models, showing that efficient importance samplers can be constructed even for highly non-Gaussian latent processes such as square-root diffusions.

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