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Tail Risk Signal Detection through a Novel EGB2 Option Pricing Model

Author

Listed:
  • Hang Lin

    (Department of Agricultural and Consumer Economics, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA)

  • Lixin Liu

    (School of Statistics, University of International Business and Economics, Beijing 100029, China)

  • Zhengjun Zhang

    (School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, China
    Department of Statistics, University of Wisconsin-Madison, Madison, WI 53706, USA)

Abstract

Connecting derivative pricing with tail risk management has become urgent for financial practice and academia. This paper proposes a novel option pricing model based on the exponential generalized beta of the second kind (EGB2) distribution. The newly proposed model is of generality, simplicity, robustness, and financial interpretability. Most importantly, one can detect tail risk signals by calibrating the proposed model. The model includes the seminal Black–Scholes (B−S) formula as a limit case and can perfectly “replicate” the option prices from Merton’s jump-diffusion model. Based on the proposed pricing model, three tail risk warning measures are introduced for tail risk signals detection: the EGB2 implied tail index, the EGB2 implied Value-at-Risk (EGB2-VaR), and the EGB2 implied risk-neutral density (EGB2 R.N.D.). Empirical results show that the new pricing model can yield higher pricing accuracy than existing models in normal and crisis periods, and three model-based tail risk measures can perfectly detect tail risk signals before financial crises.

Suggested Citation

  • Hang Lin & Lixin Liu & Zhengjun Zhang, 2023. "Tail Risk Signal Detection through a Novel EGB2 Option Pricing Model," Mathematics, MDPI, vol. 11(14), pages 1-32, July.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:14:p:3194-:d:1198862
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    References listed on IDEAS

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