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Evaluating Value-at-Risk Models before and after the Financial Crisis of 2008: International Evidence

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  • Degiannakis, Stavros
  • Floros, Christos
  • Livada, Alexandra

Abstract

Τhis paper focuses on the performance of three alternative Value-at-Risk (VaR) models to provide suitable estimates for measuring and forecasting market risk. The data sample consists of five international developed and emerging stock market indices over the time period from 2004 to 2008. The main research question is related to the performance of widely-accepted and simplified approaches to estimate VaR before and after the financial crisis. VaR is estimated using daily data from UK (FTSE 100), Germany (DAX30), USA (S&P500), Turkey (ISE National 100) and Greece (GRAGENL). Methods adopted to calculate VaR are: 1) EWMA of Riskmetrics, 2) classic GARCH(1,1) model of conditional variance assuming a conditional normally distributed returns and 3) asymmetric GARCH with skewed Student-t distributed standardized innovations. The results indicate that the widely accepted and simplified ARCH framework seems to provide satisfactory forecasts of VaR not only for the pre-2008 period of the financial crisis but also for the period of high volatility of stock market returns. Thus, the blame for financial crisis should not be cast upon quantitative techniques, used to measure and forecast market risk, alone.

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  • Degiannakis, Stavros & Floros, Christos & Livada, Alexandra, 2012. "Evaluating Value-at-Risk Models before and after the Financial Crisis of 2008: International Evidence," MPRA Paper 80463, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:80463
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    Cited by:

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    2. Jean-Paul Laurent & Hassan Omidi Firouzi, 2022. "Market Risk and Volatility Weighted Historical Simulation After Basel III," Working Papers hal-03679434, HAL.
    3. Chlebus Marcin, 2017. "EWS-GARCH: New Regime Switching Approach to Forecast Value-at-Risk," Central European Economic Journal, Sciendo, vol. 3(50), pages 01-25, December.
    4. Mateusz Buczyński & Marcin Chlebus, 2019. "Old-fashioned parametric models are still the best. A comparison of Value-at-Risk approaches in several volatility states," Working Papers 2019-12, Faculty of Economic Sciences, University of Warsaw.
    5. Wijeyakulasuriya D. A. & Wickremasinghe W. N., 2015. "Measuring Extreme Market Risk: The Sri Lankan Context," Asia-Pacific Journal of Risk and Insurance, De Gruyter, vol. 9(2), pages 185-201, July.
    6. Bogdan ZUGRAVU & Dumitru Cristian OANEA & Victoria Gabriela ANGHELACHE, 2013. "Analysis Based on the Risk Metrics Model," Romanian Statistical Review Supplement, Romanian Statistical Review, vol. 61(2), pages 145-154, May.
    7. Evangelos Vasileiou, 2022. "Inaccurate Value at Risk Estimations: Bad Modeling or Inappropriate Data?," Computational Economics, Springer;Society for Computational Economics, vol. 59(3), pages 1155-1171, March.
    8. Amira Akl Ahmed & Doaa Akl Ahmed, 2016. "Modelling Conditional Volatility and Downside Risk for Istanbul Stock Exchange," Working Papers 1028, Economic Research Forum, revised Jul 2016.
    9. Fernández-Aguado, Pilar Gómez & Martínez, Eduardo Trigo & Ruíz, Rafael Moreno & Ureña, Antonio Partal, 2022. "Evaluation of European Deposit Insurance Scheme funding based on risk analysis," International Review of Economics & Finance, Elsevier, vol. 78(C), pages 234-247.
    10. Marcin Chlebus, 2016. "Can Lognormal, Weibull or Gamma Distributions Improve the EWS-GARCH Value-at-Risk Forecasts?," FindEcon Chapters: Forecasting Financial Markets and Economic Decision-Making, in: Magdalena Osińska (ed.), Statistical Review, vol. 63, 2016, 3, edition 1, volume 63, chapter 4, pages 329-350, University of Lodz.
    11. Kamaldeen Ibraheem Nageri & Azeez Tunbosun Lawal & Falilat Ajoke Abdul, 2019. "Risk - Return Relationship: Nigerian Stock Market during Pre and Post 2007-2009 Financial Meltdown," Academic Journal of Economic Studies, Faculty of Finance, Banking and Accountancy Bucharest,"Dimitrie Cantemir" Christian University Bucharest, vol. 5(2), pages 52-62, June.
    12. Mateusz Buczyński & Marcin Chlebus, 2021. "GARCHNet - Value-at-Risk forecasting with novel approach to GARCH models based on neural networks," Working Papers 2021-08, Faculty of Economic Sciences, University of Warsaw.

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    More about this item

    Keywords

    ARCH; Value-at-Risk; Volatility; Forecasting; Financial Crisis;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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