Simulated maximum likelihood for general stochastic volatility models: a change of variable approach
AbstractMaximum 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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Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 12022.
Date of creation: 10 Jul 2008
Date of revision:
Change of Variable; Heston Model; Laplace Importance Sampler; Simulated Maximum Likelihood; Stochastic Volatility;
Find related papers by JEL classification:
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
This paper has been announced in the following NEP Reports:
- NEP-ALL-2008-12-14 (All new papers)
- NEP-ECM-2008-12-14 (Econometrics)
- NEP-ETS-2008-12-14 (Econometric Time Series)
- NEP-ORE-2008-12-14 (Operations Research)
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