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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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    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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    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. 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.
    2. 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.
    3. Norman R. Swanson & Lili Cai, 2011. "In- and Out-of-Sample Specification Analysis of Spot Rate Models: Further Evidence for the Period 1982-2008," Departmental Working Papers 201102, Rutgers University, Department of Economics.
    4. Tore Selland Kleppe & Jun Yu & Hans J. Skaug, 2011. "Simulated Maximum Likelihood Estimation for Latent Diffusion Models," Working Papers CoFie-04-2011, Sim Kee Boon Institute for Financial Economics.
    5. Almut E. D. Veraart, 2010. "How precise is the finite sample approximation of the asymptotic distribution of realised variation measures in the presence of jumps?," CREATES Research Papers 2010-65, School of Economics and Management, University of Aarhus.


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