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Abelian theorems for stochastic volatility models with application to the estimation of jump activity

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  • Belomestny, Denis
  • Panov, Vladimir

Abstract

In this paper, we prove a kind of Abelian theorem for a class of stochastic volatility models (X,V) where both the state process X and the volatility process V may have jumps. Our results relate the asymptotic behavior of the characteristic function of XΔ for some Δ>0 in a stationary regime to the Blumenthal–Getoor indexes of the Lévy processes driving the jumps in X and V. The results obtained are used to construct consistent estimators for the above Blumenthal–Getoor indexes based on low-frequency observations of the state process X. We derive convergence rates for the corresponding estimator and show that these rates cannot be improved in general.

Suggested Citation

  • Belomestny, Denis & Panov, Vladimir, 2013. "Abelian theorems for stochastic volatility models with application to the estimation of jump activity," Stochastic Processes and their Applications, Elsevier, vol. 123(1), pages 15-44.
    • Handle: RePEc:eee:spapps:v:123:y:2013:i:1:p:15-44
    DOI: 10.1016/j.spa.2012.08.015
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    References listed on IDEAS

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    1. Bates, David S, 1996. "Jumps and Stochastic Volatility: Exchange Rate Processes Implicit in Deutsche Mark Options," The Review of Financial Studies, Society for Financial Studies, vol. 9(1), pages 69-107.
    2. Heston, Steven L, 1993. "A Closed-Form Solution for Options with Stochastic Volatility with Applications to Bond and Currency Options," The Review of Financial Studies, Society for Financial Studies, vol. 6(2), pages 327-343.
    3. Belomestny, Denis, 2009. "Spectral estimation of the fractional order of a Lévy process," SFB 649 Discussion Papers 2009-021, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    4. Ole E. Barndorff‐Nielsen & Neil Shephard, 2001. "Non‐Gaussian Ornstein–Uhlenbeck‐based models and some of their uses in financial economics," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(2), pages 167-241.
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