Fitting general stochastic volatility models using Laplace accelerated sequential importance sampling

Tore Kleppe () and Hans Julius Skaug

Computational Statistics & Data Analysis, 2012, vol. 56, issue 11, 3105-3119

Abstract: A methodology for fitting general stochastic volatility (SV) models that are naturally cast in terms of a positive volatility process is developed. Two well known methods for evaluating the likelihood function, sequential importance sampling and Laplace importance sampling, are combined. The statistical properties of the resulting estimator are investigated by simulation for an ensemble of SV models. It is found that the performance is good compared to the efficient importance sampling (EIS) algorithm. Finally, the computational framework, building on automatic differentiation (AD), is outlined. The use of AD makes it easy to implement other SV models with non-Gaussian latent volatility processes.

Keywords: Accelerated sequential importance sampling; Heston model; Laplace importance sampler; Simulated maximum likelihood; Stochastic volatility (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:56:y:2012:i:11:p:3105-3119

DOI: 10.1016/j.csda.2011.05.007

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