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Fitting general stochastic volatility models using Laplace accelerated sequential importance sampling

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  • Kleppe, Tore Selland
  • Skaug, Hans Julius

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.

Suggested Citation

  • Kleppe, Tore Selland & Skaug, Hans Julius, 2012. "Fitting general stochastic volatility models using Laplace accelerated sequential importance sampling," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3105-3119.
  • Handle: RePEc:eee:csdana:v:56:y:2012:i:11:p:3105-3119
    DOI: 10.1016/j.csda.2011.05.007
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    References listed on IDEAS

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    Cited by:

    1. Skaug, Hans J. & Yu, Jun, 2014. "A flexible and automated likelihood based framework for inference in stochastic volatility models," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 642-654.
    2. Jean-François Richard, 2015. "Likelihood Evaluation of High-Dimensional Spatial Latent Gaussian Models with Non-Gaussian Response Variables," Working Paper 5778, Department of Economics, University of Pittsburgh.
    3. Tore Selland KLEPPE & Jun YU & Hans J. SKAUG, 2009. "Stimulated Maximum Likelihood Estimation of Continuous Time Stochastic Volatility Models," Working Papers 20-2009, Singapore Management University, School of Economics.
    4. Kleppe, Tore Selland & Liesenfeld, Roman, 2014. "Efficient importance sampling in mixture frameworks," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 449-463.
    5. Stojanović, Vladica S. & Popović, Biljana Č. & Milovanović, Gradimir V., 2016. "The Split-SV model," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 560-581.

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