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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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.
|Item Type:||MPRA Paper|
|Original Title:||Simulated maximum likelihood for general stochastic volatility models: a change of variable approach|
|Keywords:||Change of Variable; Heston Model; Laplace Importance Sampler; Simulated Maximum Likelihood; Stochastic Volatility|
|Subjects:||C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C13 - Estimation: General
C - Mathematical and Quantitative Methods > C2 - Single Equation Models; Single Variables > C22 - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
|Depositing User:||Tore Selland Kleppe|
|Date Deposited:||09. Dec 2008 00:35|
|Last Modified:||16. Feb 2013 05:05|
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