Modeling Tail Risk with Tempered Stable Distributions: An Overview

This paper studies the option valuation problem of non-Gaussian and asymmetric GARCH models from a state-space structure perspective. Assuming innovations following an infinitely divisible distribution, we apply different estimation methods including filtering and learning approaches. We then investigate the performance in pricing S&P 500 index short-term options after obtaining a proper change of measure. We find that the sequential Bayesian learning approach (SBLA) significantly and robustly decreases the option pricing errors. Our theoretical and empirical findings also suggest that, when stock returns are non-Gaussian distributed, their innovations under the risk-neutral measure may present more non-normality, exhibit higher volatility, and have a stronger leverage effect than under the physical measure.

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Learning for infinitely divisible GARCH models in option pricing

Zhu

Bianchi

Kim

et al. 2020

Studies in Nonlinear Dynamics &Amp; Econometrics

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Modeling Tail Risk with Tempered Stable Distributions: An Overview

Cited by 1 publication

References 52 publications

Learning for infinitely divisible GARCH models in option pricing

Learning for infinitely divisible GARCH models in option pricing

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