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Lambda Value at Risk and Regulatory Capital: A Dynamic Approach to Tail Risk

Author

Listed:
  • Asmerilda Hitaj

    (Department of Statistics and Quantitative Methods, University of Milan Bicocca, U7, Via Bicocca degli Arcimboldi 8, Milan 20126, Italy)

  • Cesario Mateus

    (Department of Accounting and Finance, University of Greenwich, Old Royal Naval College, Park Row, London SE10 9LS, UK)

  • Ilaria Peri

    (Department of Economics, Mathematics and Statistics, Birkbeck University of London, Malet St, Bloomsbury, London WC1E 7HX, UK)

Abstract

This paper presents the first methodological proposal of estimation of the Λ V a R . Our approach is dynamic and calibrated to market extreme scenarios, incorporating the need of regulators and financial institutions in more sensitive risk measures. We also propose a simple backtesting methodology by extending the V a R hypothesis-testing framework. Hence, we test our Λ V a R proposals under extreme downward scenarios of the financial crisis and different assumptions on the profit and loss distribution. The findings show that our Λ V a R estimations are able to capture the tail risk and react to market fluctuations significantly faster than the V a R and expected shortfall. The backtesting exercise displays a higher level of accuracy for our Λ V a R estimations.

Suggested Citation

  • Asmerilda Hitaj & Cesario Mateus & Ilaria Peri, 2018. "Lambda Value at Risk and Regulatory Capital: A Dynamic Approach to Tail Risk," Risks, MDPI, vol. 6(1), pages 1-18, March.
    • Handle: RePEc:gam:jrisks:v:6:y:2018:i:1:p:17-:d:134856
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    References listed on IDEAS

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    7. Jacopo Corbetta & Ilaria Peri, 2016. "Backtesting Lambda Value at Risk," Papers 1602.07599, arXiv.org, revised Jun 2017.
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    Cited by:

    1. Javier Ojea-Ferreiro, 2021. "Deconstructing Systemic Risk: A Reverse Stress Testing Approach," Springer Books, in: Marco Corazza & Manfred Gilli & Cira Perna & Claudio Pizzi & Marilena Sibillo (ed.), Mathematical and Statistical Methods for Actuarial Sciences and Finance, pages 369-375, Springer.
    2. Matthias Fischer & Thorsten Moser & Marius Pfeuffer, 2018. "A Discussion on Recent Risk Measures with Application to Credit Risk: Calculating Risk Contributions and Identifying Risk Concentrations," Risks, MDPI, vol. 6(4), pages 1-28, December.
    3. Fabio Bellini & Ilaria Peri, 2021. "An axiomatization of $\Lambda$-quantiles," Papers 2109.02360, arXiv.org, revised Jan 2022.
    4. Akif Ince & Ilaria Peri & Silvana Pesenti, 2021. "Risk contributions of lambda quantiles," Papers 2106.14824, arXiv.org, revised Nov 2022.

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