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Herausforderungen des finanziellen Risikomanagements: Eine empirische Untersuchung des Value at Risk-Ansatzes in Stresssituationen

Author

Listed:
  • Till Barz
  • Andreas Nastansky

    (Hochschule für Wirtschaft und Recht (HWR) Berlin)

Abstract

Die Quantifizierung und Begrenzung extremer Wertverluste sind von zentraler Bedeutung für das finanzielle Risikomanagement. Besonders während volatiler Marktphasen tendieren traditionelle Risikomaße dazu, Risiken fehlerhaft einzuschätzen. Die Arbeit untersucht die Risikomaße Value at Risk (VaR) und Expected Shortfall (ES) hinsichtlich ihrer Fähigkeit, Verlustpotenziale während Stresssituationen präzise abzubilden. Dazu wird die Prognosefähigkeit dieser Maße unter verschiedenen Verteilungsannahmen und Gewichtungsmethoden analysiert. Unter anderem erfolgt eine systematische Überprüfung mittels Backtesting für den Untersuchungszeitraum. Die Analyse zeigt, dass Modelle mit einer t-Verteilung und einer exponentiellen Gewichtung historischer Daten eine höhere Vorhersagegenauigkeit aufweisen. Modelle mit Normalverteilungsannahme sind in Krisenzeiten besonders anfällig für Fehlprognosen. Alle untersuchten Modelle passen sich verzögert an veränderte Marktsituationen an, was zu einer anfänglichen Unterschätzung und späteren Überschätzung der Risiken führt. Die Anpassungslatenz variiert dabei je nach gewählter Gewichtung der historischen Daten. Implikationen für das Risikomanagement beinhalten eine regelmäßige Modellüberprüfung und die Implementierung umfassender Stresstests, um systematische Risikounterbewertungen zu vermeiden. Die Ergebnisse verdeutlichen die Notwendigkeit dynamischer Risikomodelle, die sich an volatile Marktbedingungen anpassen, um die finanzielle Stabilität der Kreditinstitute langfristig zu sichern.

Suggested Citation

  • Till Barz & Andreas Nastansky, 2024. "Herausforderungen des finanziellen Risikomanagements: Eine empirische Untersuchung des Value at Risk-Ansatzes in Stresssituationen," Statistische Diskussionsbeiträge 57, Universität Potsdam, Wirtschafts- und Sozialwissenschaftliche Fakultät.
    • Handle: RePEc:pot:statdp:57
    DOI: 10.25932/publishup-66666
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    References listed on IDEAS

    as
    1. Liang Ding & Hiroyoki Miyake & Hao Zou, 2011. "Asymmetric correlations in equity returns: a fundamental-based explanation," Applied Financial Economics, Taylor & Francis Journals, vol. 21(6), pages 389-399.
    2. Juan Carlos Escanciano & Zaichao Du, 2015. "Backtesting Expected Shortfall: Accounting for Tail Risk," CAEPR Working Papers 2015-001, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
    3. Carlo Acerbi & Dirk Tasche, 2002. "Expected Shortfall: A Natural Coherent Alternative to Value at Risk," Economic Notes, Banca Monte dei Paschi di Siena SpA, vol. 31(2), pages 379-388, July.
    4. Alex Huang, 2013. "Value at risk estimation by quantile regression and kernel estimator," Review of Quantitative Finance and Accounting, Springer, vol. 41(2), pages 225-251, August.
    5. Peter Bernholz & Ernst Baltensperger & David Iselin & Oliver Landmann & Rudolf Minsch, 2015. "Der »Franken-Schock«: Die Freigabe des Schweizer Franken –wer gewinnt und wer verliert?," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 68(05), pages 03-19, March.
    6. Najah Attig & Oumar Sy, 2023. "Diversification during Hard Times," Financial Analysts Journal, Taylor & Francis Journals, vol. 79(2), pages 45-64, April.
    7. Paul H. Kupiec, 1995. "Techniques for verifying the accuracy of risk measurement models," Finance and Economics Discussion Series 95-24, Board of Governors of the Federal Reserve System (U.S.).
    8. François Longin & Bruno Solnik, 2001. "Extreme Correlation of International Equity Markets," Journal of Finance, American Finance Association, vol. 56(2), pages 649-676, April.
    9. William F. Sharpe, 1964. "Capital Asset Prices: A Theory Of Market Equilibrium Under Conditions Of Risk," Journal of Finance, American Finance Association, vol. 19(3), pages 425-442, September.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Backtesting; Historische Simulation; Risikomanagement; Value at Risk; Varianz-Kovarianz-Methode;
    All these keywords.

    JEL classification:

    • C46 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Specific Distributions
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

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