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Estimating Systematic Risk under Extremely Adverse Market Conditions

Author

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  • Maarten R C van Oordt
  • Chen Zhou

Abstract

This paper considers the problem of estimating a linear model between two heavy-tailed variables if the explanatory variable has an extremely low (or high) value. We propose an estimator for the model coefficient by exploiting the tail dependence between the two variables and prove its asymptotic properties. Simulations show that our estimation method yields a lower mean-squared error than regressions conditional on tail observations. In an empirical application, we illustrate the better performance of our approach relative to the conditional regression approach in projecting the losses of industry-specific stock portfolios in the event of a market crash.

Suggested Citation

  • Maarten R C van Oordt & Chen Zhou, 2019. "Estimating Systematic Risk under Extremely Adverse Market Conditions," Journal of Financial Econometrics, Oxford University Press, vol. 17(3), pages 432-461.
    • Handle: RePEc:oup:jfinec:v:17:y:2019:i:3:p:432-461.
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    File URL: http://hdl.handle.net/10.1093/jjfinec/nbx033
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    Cited by:

    1. Maroun, George & Fromentin, Vincent, 2024. "Financial instability in Lebanon: Do the liquidity creation and performance of banks matter?," The Quarterly Review of Economics and Finance, Elsevier, vol. 96(C).
    2. Radoslav Raykov & Consuelo Silva-Buston, 2022. "Asymmetric Systemic Risk," Staff Working Papers 22-19, Bank of Canada.
    3. Davydov, Denis & Vähämaa, Sami & Yasar, Sara, 2021. "Bank liquidity creation and systemic risk," Journal of Banking & Finance, Elsevier, vol. 123(C).
    4. Shuying Tan & Tingting Liu & Chan Wang, 2024. "The double-edged effect of bank liquidity creation efficiency on systemic risk: Evidence from China," PLOS ONE, Public Library of Science, vol. 19(11), pages 1-26, November.
    5. Junshu Jiang & Jordan Richards & Raphael Huser & David Bolin, 2024. "The Efficient Tail Hypothesis: An Extreme Value Perspective on Market Efficiency," Papers 2408.06661, arXiv.org, revised Jul 2025.
    6. Maarten R.C. Van Oordt, 2023. "Calibrating the Magnitude of the Countercyclical Capital Buffer Using Market‐Based Stress Tests," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 55(2-3), pages 465-501, March.
    7. Fang, Yi & Wang, Yanru & Wang, Qi & Zhao, Yang, 2023. "Policy uncertainty and bank systemic risk: A perspective of risk decomposition," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 88(C).
    8. Jeongseop Song & Kim Hiang Liow, 2023. "Industrial tail exposure risk and asset price: Evidence from US REITs," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 51(5), pages 1209-1245, September.
    9. Wu, Baohui & Zhu, Pingheng & Yin, Hua & Wen, Fenghua, 2023. "The risk spillover of high carbon enterprises in China: Evidence from the stock market," Energy Economics, Elsevier, vol. 126(C).

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    Keywords

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    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • G01 - Financial Economics - - General - - - Financial Crises

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