IDEAS home Printed from
   My bibliography  Save this article

Hedging of Time Discrete Auto-Regressive Stochastic Volatility Options


  • Alexandru Badescu
  • Joan del Castillo
  • Juan-Pablo Ortega


Numerous empirical proofs indicate the adequacy of the time discrete auto-regressive stochastic volatility models introduced by Taylor (Taylor S. J. [1982]; Taylor S. J. [1986]; Taylor S. J. [2005]) in the dynamical description of the log-returns of financial assets. The pricing and hedging of contingent products that use these models for their underlying assets is a complicated task due to the incomplete nature of the corresponding market and the non-observability of the associated volatility process. In this paper we introduce new pricing kernels for this setup and apply two existing volatility filtering techniques available in the literature for these models, namely Kalman filtering and the hierarchical-likelihood approach, in order to implement various pricing and dynamical hedging strategies. An extensive empirical analysis using both historical returns and options data illustrates the advantages of this model when compared with more standard approaches, namely Black-Scholes and GARCH.

Suggested Citation

  • Alexandru Badescu & Joan del Castillo & Juan-Pablo Ortega, 2016. "Hedging of Time Discrete Auto-Regressive Stochastic Volatility Options," Annals of Economics and Statistics, GENES, issue 123-124, pages 271-306.
  • Handle: RePEc:adr:anecst:y:2016:i:123-124:p:271-306 DOI: 10.15609/annaeconstat2009.123-124.0271

    Download full text from publisher

    File URL:
    Download Restriction: no

    References listed on IDEAS

    1. Golosnoy, Vasyl & Gribisch, Bastian & Liesenfeld, Roman, 2012. "The conditional autoregressive Wishart model for multivariate stock market volatility," Journal of Econometrics, Elsevier, vol. 167(1), pages 211-223.
    2. Luc Bauwens & Christian M. Hafner & Diane Pierret, 2013. "Multivariate Volatility Modeling Of Electricity Futures," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(5), pages 743-761, August.
    3. Colacito, Riccardo & Engle, Robert F. & Ghysels, Eric, 2011. "A component model for dynamic correlations," Journal of Econometrics, Elsevier, vol. 164(1), pages 45-59, September.
    4. Laurent, Sébastien & Rombouts, Jeroen V.K. & Violante, Francesco, 2013. "On loss functions and ranking forecasting performances of multivariate volatility models," Journal of Econometrics, Elsevier, vol. 173(1), pages 1-10.
    5. BAUWENS, Luc & STORTI, Giuseppe & VIOLANTE, Francesco, 2012. "Dynamic conditional correlation models for realized covariance matrices," CORE Discussion Papers 2012060, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    6. Diaa Noureldin & Neil Shephard & Kevin Sheppard, 2012. "Multivariate high‐frequency‐based volatility (HEAVY) models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(6), pages 907-933, September.
    7. Patton, Andrew J., 2011. "Volatility forecast comparison using imperfect volatility proxies," Journal of Econometrics, Elsevier, vol. 160(1), pages 246-256, January.
    Full references (including those not matched with items on IDEAS)

    More about this item


    Stochastic Volatility Models; ARSV Models; Hedging Techniques; Incomplete Markets; Local Risk Minimization; Kalman Filter; Hierarchical-Likelihood;

    JEL classification:

    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing


    Access and download statistics


    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:adr:anecst:y:2016:i:123-124:p:271-306. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Laurent Linnemer). General contact details of provider: .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.