Tempered Stable Processes with Time Varying Exponential Tails

Young Shin Kim, Kum-Hwan Roh and Raphael Douady ()

Papers from arXiv.org

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.

Date: 2020-06, Revised 2020-08
New Economics Papers: this item is included in nep-ecm, nep-ore and nep-rmg
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http://arxiv.org/pdf/2006.07669 Latest version (application/pdf)

Related works:
Working Paper: Tempered stable processes with time-varying exponential tails (2021)
Working Paper: Tempered stable processes with time-varying exponential tails (2021)
Working Paper: Tempered Stable Processes with Time Varying Exponential Tails (2020)
Working Paper: Tempered Stable Processes with Time Varying Exponential Tails (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2006.07669

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