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Estimating and Forecasting Conditional Risk Measures with Extreme Value Theory: A Review

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  • Marco Bee

    (Department of Economics and Management, University of Trento, via Inama 5, 38122 Trento, Italy)

  • Luca Trapin

    (Department of Mathematical Sciences, Mathematical Finance and Econometrics, Università Cattolica del Sacro Cuore, Largo Gemelli 1, 20123 Milano, Italy)

Abstract

One of the key components of financial risk management is risk measurement. This typically requires modeling, estimating and forecasting tail-related quantities of the asset returns’ conditional distribution. Recent advances in the financial econometrics literature have developed several models based on Extreme Value Theory (EVT) to carry out these tasks. The purpose of this paper is to review these methods.

Suggested Citation

  • Marco Bee & Luca Trapin, 2018. "Estimating and Forecasting Conditional Risk Measures with Extreme Value Theory: A Review," Risks, MDPI, vol. 6(2), pages 1-16, April.
    • Handle: RePEc:gam:jrisks:v:6:y:2018:i:2:p:45-:d:142858
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    References listed on IDEAS

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    2. Hamed Tabasi & Vahidreza Yousefi & Jolanta Tamošaitienė & Foroogh Ghasemi, 2019. "Estimating Conditional Value at Risk in the Tehran Stock Exchange Based on the Extreme Value Theory Using GARCH Models," Administrative Sciences, MDPI, vol. 9(2), pages 1-17, May.
    3. Osman Doğan & Süleyman Taşpınar & Anil K. Bera, 2021. "Bayesian estimation of stochastic tail index from high-frequency financial data," Empirical Economics, Springer, vol. 61(5), pages 2685-2711, November.

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