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Tempered stable processes with time-varying exponential tails

Author

Listed:
  • Raphaël Douady

    (CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique)

  • Young Shin Kim
  • Kum-Hwan Roh
Abstract
In this paper, we introduce a new time series model having a stochastic exponential tail. This model is constructed based on the Normal Tempered Stable distribution with a time-varying parameter. The model captures the stochastic exponential tail, which generates the volatility smile effect and volatility term structure in option pricing. Moreover, the model describes the time-varying volatility of volatility. We empirically show the stochastic skewness and stochastic kurtosis by applying the model to analyze S\&P 500 index return data. We present the Monte-Carlo simulation technique for the parameter calibration of the model for the S\&P 500 option prices. We can see that the stochastic exponential tail makes the model better to analyze the market option prices by the calibration.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Raphaël Douady & Young Shin Kim & Kum-Hwan Roh, 2021. "Tempered stable processes with time-varying exponential tails," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-03512709, HAL.
    • Handle: RePEc:hal:cesptp:hal-03512709
    DOI: 10.1080/14697688.2021.1962958
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    Cited by:

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