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Fourier inference for stochastic volatility models with heavy-tailed innovations

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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 \(L_2\)-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.

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The research of Simos Meintanis was partially supported by grant Nr. 11699 of the Special Account for Research Grants (E\(\Lambda \)KE) of the National and Kapodistrian University of Athens.

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Correspondence to Bruno Ebner.

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On sabbatical leave from the University of Athens.

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Ebner, B., Klar, B. & Meintanis, S.G. Fourier inference for stochastic volatility models with heavy-tailed innovations. Stat Papers 59, 1043–1060 (2018).

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  • Stochastic volatility model
  • Minimum distance estimation
  • Heavy-tailed distribution
  • Characteristic function