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Predicting risk with risk measures : an empirical study

Author

Listed:
  • Marcel Bräutigam

    (LabEx MME-DII - UCP - Université de Cergy Pontoise - Université Paris-Seine, ESSEC Business School, LPSM (UMR_8001) - Laboratoire de Probabilités, Statistique et Modélisation - UPD7 - Université Paris Diderot - Paris 7 - SU - Sorbonne Université - CNRS - Centre National de la Recherche Scientifique)

  • Michel Dacorogna

    (SCOR SE - SCOR SE [Paris], DEAR Consulting)

  • Marie Kratz

    (ESSEC Business School, CREAR - Center of Research in Econo-finance and Actuarial sciences on Risk / Centre de Recherche Econo-financière et Actuarielle sur le Risque - ESSEC Business School)

Abstract

In this study we consider the risk estimation as a stochastic process based on the Sample Quantile Process (SQP) - which is a generalization of the Value-at-Risk calculated on a rolling sample. Using SQP's, we are able to show and quantify the pro-cyclicality of the current way nancial institutions measure their risk. Analysing 11 stock indices, we show that, if the past volatility is low, the historical computation of the risk measure underestimates the future risk, while in periods of high volatility, the risk measure overestimates the risk. Moreover, using a simple GARCH(1,1) model, we conclude that this pro-cyclical e ect is related to the clustering of volatility. We argue that this has important consequences for the regulation in times of crisis.

Suggested Citation

  • Marcel Bräutigam & Michel Dacorogna & Marie Kratz, 2018. "Predicting risk with risk measures : an empirical study," Working Papers hal-01791026, HAL.
    • Handle: RePEc:hal:wpaper:hal-01791026
    Note: View the original document on HAL open archive server: https://essec.hal.science/hal-01791026
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    References listed on IDEAS

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    1. Athanasoglou, Panayiotis P. & Daniilidis, Ioannis & Delis, Manthos D., 2014. "Bank procyclicality and output: Issues and policies," Journal of Economics and Business, Elsevier, vol. 72(C), pages 58-83.
    2. Gordy, Michael B. & Howells, Bradley, 2006. "Procyclicality in Basel II: Can we treat the disease without killing the patient?," Journal of Financial Intermediation, Elsevier, vol. 15(3), pages 395-417, July.
    3. Rafael Repullo & Javier Suarez, 2008. "The Procyclical Effects of Basel II," Working Papers wp2008_0809, CEMFI.
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    6. Christoffersen, Peter F, 1998. "Evaluating Interval Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 841-862, November.
    7. Philippe Artzner & Freddy Delbaen & Jean‐Marc Eber & David Heath, 1999. "Coherent Measures of Risk," Mathematical Finance, Wiley Blackwell, vol. 9(3), pages 203-228, July.
    8. Frédérique Bec & Christian Gollier, 2009. "Term Structure and Cyclicity of Value-at-Risk: Consequences for the Solvency Capital Requirement," CESifo Working Paper Series 2596, CESifo.
    9. Gençay, Ramazan & Dacorogna, Michel & Muller, Ulrich A. & Pictet, Olivier & Olsen, Richard, 2001. "An Introduction to High-Frequency Finance," Elsevier Monographs, Elsevier, edition 1, number 9780122796715.
    10. Susanne Emmer & Marie Kratz & Dirk Tasche, 2013. "What is the best risk measure in practice? A comparison of standard measures," Papers 1312.1645, arXiv.org, revised Apr 2015.
    11. Embrechts, Paul & Samorodnitsky, Gennady, 1995. "Sample quantiles of heavy tailed stochastic processes," Stochastic Processes and their Applications, Elsevier, vol. 59(2), pages 217-233, October.
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    Cited by:

    1. Marcel, Bräutigam & Marie, Kratz, 2018. "On the Dependence between Quantiles and Dispersion Estimators," ESSEC Working Papers WP1807, ESSEC Research Center, ESSEC Business School.
    2. Marcel Bräutigam & Marie Kratz, 2018. "On the Dependence between Quantiles and Dispersion Estimators," Working Papers hal-02296832, HAL.

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    More about this item

    Keywords

    risk measure; sample quantile process; stochastic model; VaR; volatility;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G01 - Financial Economics - - General - - - Financial Crises
    • 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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