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Value at risk (VaR) analysis for fat tails and long memory in returns

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  • Samet Günay

    (American University of the Middle East)

Abstract

In this study, different value at risk models (VaR), which are used to measure downside investment risk, have been analyzed under different methods and stylized facts of financial time series. Downside investment risk of a single asset and of a hypothetical portfolio have first been measured by conventional VaR models (Parametrical VaR, Historical VaR, Historical Simulation VaR and Monte Carlo Simulation VaR) and then by alternative simulation models that consider fat tails (Alpha-Stable Simulation VaR) in return distributions and long memory in returns (Long Memory Simulation VaR). Empirical findings and the Duration Based Backtesting procedure indicate that the largest VaR value is obtained under Long Memory Simulation VaR that is based on the long memory in returns. This result is consistent with the findings of Mandelbrot’s various studies.

Suggested Citation

  • Samet Günay, 2017. "Value at risk (VaR) analysis for fat tails and long memory in returns," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 7(2), pages 215-230, August.
  • Handle: RePEc:spr:eurase:v:7:y:2017:i:2:d:10.1007_s40822-017-0067-z
    DOI: 10.1007/s40822-017-0067-z
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    References listed on IDEAS

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    1. Bertrand Candelon & Gilbert Colletaz & Christophe Hurlin & Sessi Tokpavi, 2011. "Backtesting Value-at-Risk: A GMM Duration-Based Test," Journal of Financial Econometrics, Oxford University Press, vol. 9(2), pages 314-343, Spring.
    2. Korkmaz, Turhan & Bostanci, Ahmet, 2011. "The Comparison of Volatility Forecasting Models in VaR Calculations and Backtesting according to Basel II: An Application on ISE 100 Index," Business and Economics Research Journal, Uludag University, Faculty of Economics and Administrative Sciences, vol. 2(3), pages 1-1, July.
    3. Gourieroux, C. & Laurent, J. P. & Scaillet, O., 2000. "Sensitivity analysis of Values at Risk," Journal of Empirical Finance, Elsevier, vol. 7(3-4), pages 225-245, November.
    4. Peter Christoffersen, 2004. "Backtesting Value-at-Risk: A Duration-Based Approach," Journal of Financial Econometrics, Oxford University Press, vol. 2(1), pages 84-108.
    5. 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.
    6. Mert Ural, 2009. "Alternative Approaches for Estimating Value at Risk," Journal of BRSA Banking and Financial Markets, Banking Regulation and Supervision Agency, vol. 3(2), pages 63-86.
    7. Theodore Syriopoulos & Michael Tsatsaronis, 2012. "Corporate Governance Mechanisms and Financial Performance: CEO Duality in Shipping Firms," Eurasian Business Review, Springer;Eurasia Business and Economics Society, vol. 2(1), pages 1-30, June.
    8. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    9. Jörg Rieger & Kirsten Rüchardt & Bodo Vogt, 2011. "Comparing High Frequency Data of Stocks that are Traded Simultaneously in the US and Germany: Simulated Versus Empirical Data," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 1(2), pages 126-142, December.
    10. 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.).
    11. Samet Günay, 2014. "Are the Scaling Properties of Bull and Bear Markets Identical? Evidence from Oil and Gold Markets," IJFS, MDPI, vol. 2(4), pages 1-20, October.
    12. Bostancı, Ahmet & Korkmaz, Turhan, 2014. "Comparison of Value at Risk Calculation Models in Terms of Banks’ Capital Adequacy Ratio," Business and Economics Research Journal, Uludag University, Faculty of Economics and Administrative Sciences, vol. 5(3), pages 15-41, July.
    13. R. Cont, 2001. "Empirical properties of asset returns: stylized facts and statistical issues," Quantitative Finance, Taylor & Francis Journals, vol. 1(2), pages 223-236.
    14. Benoit Mandelbrot, 2015. "The Variation of Certain Speculative Prices," World Scientific Book Chapters, in: Anastasios G Malliaris & William T Ziemba (ed.), THE WORLD SCIENTIFIC HANDBOOK OF FUTURES MARKETS, chapter 3, pages 39-78, World Scientific Publishing Co. Pte. Ltd..
    15. Benoit B. Mandelbrot, 1972. "Statistical Methodology for Nonperiodic Cycles: From the Covariance To R/S Analysis," NBER Chapters, in: Annals of Economic and Social Measurement, Volume 1, number 3, pages 259-290, National Bureau of Economic Research, Inc.
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    More about this item

    Keywords

    Value at risk; Alpha stable distributions; Long memory; Backtesting; Turkish stock market;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • F30 - International Economics - - International Finance - - - General

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