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Forecasting Value-At-Risk With Two-Step Method: Garch-Exponentiated Odd Log-Logistic Normal Model

Author

Listed:
  • Emrah ALTUN

    (Department of Statistics, Hacettepe University, Turkey. Corresponding Author.)

  • Morad ALIZADEH

    (Department of Statistics, Persian Gulf University, Bushehr, Iran.)

  • Gamze OZEL

    (Department of Statistics, Hacettepe University, Turkey.)

  • Hüseyin TATLIDIL

    (Department of Statistics, Hacettepe University, Turkey.)

  • Najmieh MAKSAYI

    (Faculty of Mathematics, University of Sistan and Balouchestan, Iran.)

Abstract

The purpose of this study is to evaluate the forecasting ability of GARCH-type models in estimating the Value-at-Risk (VaR) by introducing a new four-parameter distribution, called Exponentiated Odd Log-Logistic Normal distribution. The statistical properties of new heavytailed distribution are investigated and a simulation study is performed to assess the maximum likelihood estimations of introduced distribution. Then, the VaR is forecasted by using mean and volatility forecasts and quantile estimation of introduced distribution. Daily VaR forecasting ability of proposed two-stage model is compared with the GARCH models specified under heavy-tailed distributions by means of two backtesting methods. Empirical findings show that proposed two-stage model outperforms to well-known distributions such as normal, Student’s-t, generalized error, and skewed generalized error distributions at high quantiles.

Suggested Citation

  • Emrah ALTUN & Morad ALIZADEH & Gamze OZEL & Hüseyin TATLIDIL & Najmieh MAKSAYI, 2017. "Forecasting Value-At-Risk With Two-Step Method: Garch-Exponentiated Odd Log-Logistic Normal Model," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 97-115, December.
    • Handle: RePEc:rjr:romjef:v::y:2017:i:4:p:97-115
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    References listed on IDEAS

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    1. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    2. Keith Kuester & Stefan Mittnik & Marc S. Paolella, 2006. "Value-at-Risk Prediction: A Comparison of Alternative Strategies," Journal of Financial Econometrics, Oxford University Press, vol. 4(1), pages 53-89.
    3. 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.).
    4. Marimoutou, Velayoudoum & Raggad, Bechir & Trabelsi, Abdelwahed, 2009. "Extreme Value Theory and Value at Risk: Application to oil market," Energy Economics, Elsevier, vol. 31(4), pages 519-530, July.
    5. Nadarajah, Saralees, 2008. "Explicit expressions for moments of order statistics," Statistics & Probability Letters, Elsevier, vol. 78(2), pages 196-205, February.
    6. Manuela Braione & Nicolas K. Scholtes, 2016. "Forecasting Value-at-Risk under Different Distributional Assumptions," Econometrics, MDPI, vol. 4(1), pages 1-27, January.
    7. Angelidis, Timotheos & Benos, Alexandros & Degiannakis, Stavros, 2004. "The Use of GARCH Models in VaR Estimation," MPRA Paper 96332, University Library of Munich, Germany.
    8. McMillan, David G. & Kambouroudis, Dimos, 2009. "Are RiskMetrics forecasts good enough? Evidence from 31 stock markets," International Review of Financial Analysis, Elsevier, vol. 18(3), pages 117-124, June.
    9. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    10. Fotios C. Harmantzis & Linyan Miao & Yifan Chien, 2006. "Empirical study of value-at-risk and expected shortfall models with heavy tails," Journal of Risk Finance, Emerald Group Publishing, vol. 7(2), pages 117-135, March.
    11. Hung, Jui-Cheng & Lee, Ming-Chih & Liu, Hung-Chun, 2008. "Estimation of value-at-risk for energy commodities via fat-tailed GARCH models," Energy Economics, Elsevier, vol. 30(3), pages 1173-1191, May.
    12. Bollerslev, Tim, 1987. "A Conditionally Heteroskedastic Time Series Model for Speculative Prices and Rates of Return," The Review of Economics and Statistics, MIT Press, vol. 69(3), pages 542-547, August.
    13. BRAIONE, Manuela & SCHOLTES, Nicolas K., 2016. "Forecasting Value-at-Risk under Different Distributional Assumptions," LIDAM Reprints CORE 2733, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    14. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
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    More about this item

    Keywords

    Value-at-Risk; GARCH model; log–logistic distribution; maximum likelihood; estimation; normal distribution;
    All these keywords.

    JEL classification:

    • C46 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Specific Distributions
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • 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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