Fourier inference for stochastic volatility models with heavy-tailed innovations

Bruno Ebner (), Bernhard Klar () and Simos G. Meintanis ()
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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

Statistical Papers, 2018, vol. 59, issue 3, No 10, 1043-1060

Abstract: 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.

Keywords: Stochastic volatility model; Minimum distance estimation; Heavy-tailed distribution; Characteristic function (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s00362-016-0803-6

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