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An unscented Kalman smoother for volatility extraction: Evidence from stock prices and options

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  • Li, Junye
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    Abstract

    A smoothing algorithm based on the unscented transformation is proposed for the nonlinear Gaussian system. The algorithm first implements a forward unscented Kalman filter and then evokes a separate backward smoothing pass by only making Gaussian approximations in the state but not in the observation space. The method is applied to volatility extraction in a diffusion option pricing model. Both simulation study and empirical applications with the Heston stochastic volatility model indicate that in order to accurately capture the volatility dynamics, both stock prices and options are necessary.

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    File URL: http://www.sciencedirect.com/science/article/pii/S0167947311002015
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    Bibliographic Info

    Article provided by Elsevier in its journal Computational Statistics & Data Analysis.

    Volume (Year): 58 (2013)
    Issue (Month): C ()
    Pages: 15-26

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    Handle: RePEc:eee:csdana:v:58:y:2013:i:c:p:15-26

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    Web page: http://www.elsevier.com/locate/csda

    Related research

    Keywords: Nonlinear Gaussian state-space models; Nonlinear Kalman filters; Unscented Kalman smoother; Heston stochastic volatility model; Option pricing;

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    Cited by:
    1. Eddelbuettel, Dirk & Sanderson, Conrad, 2014. "RcppArmadillo: Accelerating R with high-performance C++ linear algebra," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 1054-1063.
    2. 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.
    3. Peter Christoffersen & Christian Dorion & Kris Jacobs & Lotfi Karoui, 2012. "Nonlinear Kalman Filtering in Affine Term Structure Models," CREATES Research Papers 2012-49, School of Economics and Management, University of Aarhus.

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