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Estimation of continuous-time stochastic volatility models with jumps using high-frequency data

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  • Todorov, Viktor

Abstract

This paper proposes a method of inference for general stochastic volatility models containing price jumps. The estimation is based on treating realized multipower variation statistics calculated from high-frequency data as their unobservable (fill-in) asymptotic limits. The paper provides easy-to-check conditions under which the error in estimation resulting from this approximation is op(1) and additional ones under which it is , where T is the number of days in the sample. Extensive Monte Carlo analysis shows that the proposed estimation method works well in finite samples, provided asymptotic approximations are used. The estimation technique is applied to the estimation of two semiparametric models.

Suggested Citation

  • Todorov, Viktor, 2009. "Estimation of continuous-time stochastic volatility models with jumps using high-frequency data," Journal of Econometrics, Elsevier, vol. 148(2), pages 131-148, February.
  • Handle: RePEc:eee:econom:v:148:y:2009:i:2:p:131-148
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    References listed on IDEAS

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    1. Ole E. Barndorff-Nielsen & Neil Shephard, 2006. "Econometrics of Testing for Jumps in Financial Economics Using Bipower Variation," Journal of Financial Econometrics, Oxford University Press, vol. 4(1), pages 1-30.
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    4. Todorov, Viktor & Tauchen, George, 2006. "Simulation Methods for Levy-Driven Continuous-Time Autoregressive Moving Average (CARMA) Stochastic Volatility Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 24, pages 455-469, October.
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