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Evaluating the RiskMetrics Methodology in Measuring Volatility and Value-at-Risk in Financial Markets

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  • Szilard Pafka
  • Imre Kondor

Abstract

We analyze the performance of RiskMetrics, a widely used methodology for measuring market risk. Based on the assumption of normally distributed returns, the RiskMetrics model completely ignores the presence of fat tails in the distribution function, which is an important feature of financial data. Nevertheless, it was commonly found that RiskMetrics performs satisfactorily well, and therefore the technique has become widely used in the financial industry. We find, however, that the success of RiskMetrics is the artifact of the choice of the risk measure. First, the outstanding performance of volatility estimates is basically due to the choice of a very short (one-period ahead) forecasting horizon. Second, the satisfactory performance in obtaining Value-at-Risk by simply multiplying volatility with a constant factor is mainly due to the choice of the particular significance level.

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Paper provided by arXiv.org in its series Papers with number cond-mat/0103107.

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Date of creation: Mar 2001
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Handle: RePEc:arx:papers:cond-mat/0103107

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  1. Stefano Galluccio & Jean-Philippe Bouchaud & Marc Potters, 1998. "Rational Decisions, Random Matrices and Spin Glasses," Papers cond-mat/9801209, arXiv.org.
  2. Galluccio, Stefano & Bouchaud, Jean-Philippe & Potters, Marc, 1998. "Rational decisions, random matrices and spin glasses," Physica A: Statistical Mechanics and its Applications, Elsevier, Elsevier, vol. 259(3), pages 449-456.
  3. Nelson, Daniel B., 1992. "Filtering and forecasting with misspecified ARCH models I : Getting the right variance with the wrong model," Journal of Econometrics, Elsevier, Elsevier, vol. 52(1-2), pages 61-90.
  4. Laurent Laloux & Pierre Cizeau & Jean-Philippe Bouchaud & Marc Potters, 1998. "Noise dressing of financial correlation matrices," Science & Finance (CFM) working paper archive 500051, Science & Finance, Capital Fund Management.
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Cited by:
  1. Viviana Fernandez & Brian M. Lucey, 2006. "Portfolio management implications of volatility shifts: Evidence from simulated data," The Institute for International Integration Studies Discussion Paper Series, IIIS iiisdp131, IIIS.
  2. Fernandez, Viviana & Lucey, Brian M., 2007. "Portfolio management under sudden changes in volatility and heterogeneous investment horizons," Physica A: Statistical Mechanics and its Applications, Elsevier, Elsevier, vol. 375(2), pages 612-624.
  3. Med Imen Gallali & Raggad Zahraa, 2012. "Evaluation of VaR models' forecasting performance: the case of oil markets," International Journal of Financial Services Management, Inderscience Enterprises Ltd, Inderscience Enterprises Ltd, vol. 5(3), pages 197-215.
  4. Del Brio, Esther B. & Mora-Valencia, Andrés & Perote, Javier, 2014. "Semi-nonparametric VaR forecasts for hedge funds during the recent crisis," Physica A: Statistical Mechanics and its Applications, Elsevier, Elsevier, vol. 401(C), pages 330-343.
  5. Matei, Marius, 2010. "Risk analysis in the evaluation of the international investment opportunities. Advances in modelling and forecasting volatility for risk assessment purposes," Working Papers of Institute for Economic Forecasting, Institute for Economic Forecasting 100201, Institute for Economic Forecasting.
  6. Stavros Degiannakis & Christos Floros & Alexandra Livada, 2012. "Evaluating value-at-risk models before and after the financial crisis of 2008: International evidence," Managerial Finance, Emerald Group Publishing, Emerald Group Publishing, vol. 38(3), pages 436-452, March.
  7. Dimitrakopoulos, Dimitris N. & Kavussanos, Manolis G. & Spyrou, Spyros I., 2010. "Value at risk models for volatile emerging markets equity portfolios," The Quarterly Review of Economics and Finance, Elsevier, Elsevier, vol. 50(4), pages 515-526, November.

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