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Fourier inference for stochastic volatility models with heavy-tailed innovations

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  • Bruno Ebner

    (Karlsruhe Institute of Technology)

  • Bernhard Klar

    (Karlsruhe Institute of Technology)

  • Simos G. Meintanis

    (National and Kapodistrian University of Athens
    North-West University)

Abstract

We consider estimation of stochastic volatility models which are driven by a heavy-tailed innovation distribution. Exploiting the simple structure of the characteristic function of suitably transformed observations we propose an estimator which minimizes a weighted $$L_2$$ L 2 -type distance between the theoretical characteristic function of these observations and an empirical counterpart. A related goodness-of-fit test is also proposed. Monte-Carlo results are presented. The procedures are also applied to real data from the financial markets.

Suggested Citation

  • Bruno Ebner & Bernhard Klar & Simos G. Meintanis, 2018. "Fourier inference for stochastic volatility models with heavy-tailed innovations," Statistical Papers, Springer, vol. 59(3), pages 1043-1060, September.
    • Handle: RePEc:spr:stpapr:v:59:y:2018:i:3:d:10.1007_s00362-016-0803-6
    DOI: 10.1007/s00362-016-0803-6
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    References listed on IDEAS

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    Cited by:

    1. Christophe Chesneau & Salima El Kolei & Fabien Navarro, 2022. "Parametric estimation of hidden Markov models by least squares type estimation and deconvolution," Statistical Papers, Springer, vol. 63(5), pages 1615-1648, October.
    2. Marie Hušková & Simos G. Meintanis & Charl Pretorius, 2022. "Tests for heteroskedasticity in transformation models," Statistical Papers, Springer, vol. 63(4), pages 1013-1049, August.

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