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Parametric estimation of hidden stochastic model by contrast minimization and deconvolution: application to the Stochastic Volatility Model

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  • Salima El Kolei
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    Abstract

    We study a new parametric approach for particular hidden stochastic models such as the Stochastic Volatility model. This method is based on contrast minimization and deconvolution. After proving consistency and asymptotic normality of the estimation leading to asymptotic confidence intervals, we provide a thorough numerical study, which compares most of the classical methods that are used in practice (Quasi Maximum Likelihood estimator, Simulated Expectation Maximization Likelihood estimator and Bayesian estimators). We prove that our estimator clearly outperforms the Maximum Likelihood Estimator in term of computing time, but also most of the other methods. We also show that this contrast method is the most robust with respect to non Gaussianity of the error and also does not need any tuning parameter.

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    File URL: http://arxiv.org/pdf/1202.2559
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    Bibliographic Info

    Paper provided by arXiv.org in its series Papers with number 1202.2559.

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    Date of creation: Feb 2012
    Date of revision: Mar 2013
    Publication status: Published in Metrika, journal 184 article 430, 2013
    Handle: RePEc:arx:papers:1202.2559

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    Web page: http://arxiv.org/

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    1. Ronald J. Mahieu & Peter C. Schotman, 1998. "An empirical application of stochastic volatility models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 13(4), pages 333-360.
    2. Melino, Angelo & Turnbull, Stuart M., 1990. "Pricing foreign currency options with stochastic volatility," Journal of Econometrics, Elsevier, vol. 45(1-2), pages 239-265.
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