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Numerical integration-based Gaussian mixture filters for maximum likelihood estimation of asymmetric stochastic volatility models

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  • Hiroyuki Kawakatsu
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    Abstract

    I consider Gaussian filters based on numerical integration for maximum likelihood estimation of stochastic volatility models with leverage. I show that for this class of models, the prediction step of the Gaussian filter can be evaluated analytically without linearizing the state--space model. Monte Carlo simulations show that the mixture Gaussian filter performs remarkably well in terms of both accuracy and computation time compared to the quasi-maximum likelihood and importance sampler filters. The result that the prediction step of the Gaussian filter can be evaluated analytically is shown to apply more generally to a number of commonly used specifications of the stochastic volatility model. Copyright Royal Economic Society 2007

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    File URL: http://www.blackwell-synergy.com/doi/abs/10.1111/j.1368-423X.2007.00211.x
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    Bibliographic Info

    Article provided by Royal Economic Society in its journal Econometrics Journal.

    Volume (Year): 10 (2007)
    Issue (Month): 2 (07)
    Pages: 342-358

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    Handle: RePEc:ect:emjrnl:v:10:y:2007:i:2:p:342-358

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    Cited by:
    1. Tore Selland Kleppe & Hans J. Skaug & Jun Yu, 2009. "Simulated Maximum Likelihood Estimation of Continuous Time Stochastic Volatility Models," Working Papers CoFie-09-2009, Sim Kee Boon Institute for Financial Economics.
    2. Cai, Lili & Swanson, Norman R., 2011. "In- and out-of-sample specification analysis of spot rate models: Further evidence for the period 1982-2008," Journal of Empirical Finance, Elsevier, vol. 18(4), pages 743-764, September.
    3. Almut Veraart, 2011. "How precise is the finite sample approximation of the asymptotic distribution of realised variation measures in the presence of jumps?," AStA Advances in Statistical Analysis, Springer, vol. 95(3), pages 253-291, September.
    4. Tore Selland Kleppe & Jun Yu & Hans J. Skaug, 2012. "Simulated Maximum Likelihood Estimation for Latent Diffusion Models," Working Papers 12-2012, Singapore Management University, School of Economics.
    5. Carles Bretó & Helena Veiga, 2011. "Forecasting volatility: does continuous time do better than discrete time?," Statistics and Econometrics Working Papers ws112518, Universidad Carlos III, Departamento de Estadística y Econometría.

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