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On the implementation of asymmetric VaR models for managing and forecasting market risk

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  • Vasilios Sogiakas

Abstract

This paper investigates the implementation of asymmetric models and skewed distributions when managing market risk using the Value-at-Risk. The comparative analysis of the VaR estimations is executed by consideration of the time dynamics and the sequence of potential violation of the model. The findings of the paper suggest that the consideration of skewed distributions of time series and asymmetric volatility specification result to more accurate estimations of the VaR and hence provide the means for more efficient estimators of the potential losses that an institution is likely to exhibit. The importance of the paper lies on the fact that according to the regulative authorities financial institutions are supposed to adopt internally models for managing more efficiently market risk and this could be achieved by applying asymmetric models on both the volatility of their assets and on the distributions of the examined time series.JEL classification numbers: G10, G15, C22, C32, C58Keywords: VaR; GARCH models; leverage effect; asymmetric distribution

Suggested Citation

  • Vasilios Sogiakas, 2017. "On the implementation of asymmetric VaR models for managing and forecasting market risk," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 7(6), pages 1-2.
    • Handle: RePEc:spt:apfiba:v:7:y:2017:i:6:f:7_6_2
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    References listed on IDEAS

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    1. Pierre Giot & Sébastien Laurent, 2003. "Value-at-risk for long and short trading positions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(6), pages 641-663.
    2. 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.
    3. Glosten, Lawrence R & Jagannathan, Ravi & Runkle, David E, 1993. "On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks," Journal of Finance, American Finance Association, vol. 48(5), pages 1779-1801, December.
    4. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    5. Geweke, John, 1986. "Exact Inference in the Inequality Constrained Normal Linear Regression Model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 1(2), pages 127-141, April.
    6. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    7. Ding, Zhuanxin & Granger, Clive W. J. & Engle, Robert F., 1993. "A long memory property of stock market returns and a new model," Journal of Empirical Finance, Elsevier, vol. 1(1), pages 83-106, June.
    8. 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.).
    9. French, Kenneth R. & Schwert, G. William & Stambaugh, Robert F., 1987. "Expected stock returns and volatility," Journal of Financial Economics, Elsevier, vol. 19(1), pages 3-29, September.
    10. Milhoj, Anders, 1987. "A Conditional Variance Model for Daily Deviations of an Exchange Rate," Journal of Business & Economic Statistics, American Statistical Association, vol. 5(1), pages 99-103, January.
    11. van Mierlo, J.G.A., 2001. "Over de verhouding tussen overheid, marktwerking en privatisering. Een economische meta-analyse," Research Memorandum 014, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
    12. Philippe Jorion, 2000. "Risk management lessons from Long‐Term Capital Management," European Financial Management, European Financial Management Association, vol. 6(3), pages 277-300, September.
    13. Hansen, Bruce E, 1994. "Autoregressive Conditional Density Estimation," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 35(3), pages 705-730, August.
    14. Christie, Andrew A., 1982. "The stochastic behavior of common stock variances : Value, leverage and interest rate effects," Journal of Financial Economics, Elsevier, vol. 10(4), pages 407-432, December.
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    More about this item

    Keywords

    var; garch models; leverage effect; asymmetric distribution;
    All these keywords.

    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • 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

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