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A conditional-SGT-VaR approach with alternative GARCH models

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  • Turan Bali
  • Panayiotis Theodossiou

Abstract

This paper proposes a conditional technique for the estimation of VaR and expected shortfall measures based on the skewed generalized t (SGT) distribution. The estimation of the conditional mean and conditional variance of returns is based on ten popular variations of the GARCH model. The results indicate that the TS-GARCH and EGARCH models have the best overall performance. The remaining GARCH specifications, except in a few cases, produce acceptable results. An unconditional SGT-VaR performs well on an in-sample evaluation and fails the tests on an out-of-sample evaluation. The latter indicates the need to incorporate time-varying mean and volatility estimates in the computation of VaR and expected shortfall measures. Copyright Springer Science+Business Media, LLC 2007

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  • Turan Bali & Panayiotis Theodossiou, 2007. "A conditional-SGT-VaR approach with alternative GARCH models," Annals of Operations Research, Springer, vol. 151(1), pages 241-267, April.
  • Handle: RePEc:spr:annopr:v:151:y:2007:i:1:p:241-267:10.1007/s10479-006-0118-4
    DOI: 10.1007/s10479-006-0118-4
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    5. Laura Garcia-Jorcano & Alfonso Novales, 2020. "A dominance approach for comparing the performance of VaR forecasting models," Computational Statistics, Springer, vol. 35(3), pages 1411-1448, September.
    6. Laura Garcia‐Jorcano & Alfonso Novales, 2021. "Volatility specifications versus probability distributions in VaR forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(2), pages 189-212, March.
    7. Nieto, Maria Rosa & Ruiz, Esther, 2016. "Frontiers in VaR forecasting and backtesting," International Journal of Forecasting, Elsevier, vol. 32(2), pages 475-501.
    8. Benjamin Mögel & Benjamin R. Auer, 2018. "How accurate are modern Value-at-Risk estimators derived from extreme value theory?," Review of Quantitative Finance and Accounting, Springer, vol. 50(4), pages 979-1030, May.
    9. Carole Toque & Virginie Terraza, 2014. "Histogram-valued data on value at risk measures: a symbolic approach for risk attribution," Applied Economics Letters, Taylor & Francis Journals, vol. 21(17), pages 1243-1251, November.
    10. Laura Garcia-Jorcano & Alfonso Novales, 0. "A dominance approach for comparing the performance of VaR forecasting models," Computational Statistics, Springer, vol. 0, pages 1-38.
    11. Dilip Kumar, 2016. "Estimating and forecasting value-at-risk using the unbiased extreme value volatility estimator," Proceedings of Economics and Finance Conferences 3205528, International Institute of Social and Economic Sciences.
    12. Han Lin Shang & Yang Yang & Fearghal Kearney, 2019. "Intraday forecasts of a volatility index: functional time series methods with dynamic updating," Annals of Operations Research, Springer, vol. 282(1), pages 331-354, November.
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    14. Vijverberg, Chu-Ping C. & Vijverberg, Wim P.M. & Taşpınar, Süleyman, 2016. "Linking Tukey’s legacy to financial risk measurement," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 595-615.
    15. Ahmed BenSaïda & Sabri Boubaker & Duc Khuong Nguyen & Skander Slim, 2018. "Value‐at‐risk under market shifts through highly flexible models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(8), pages 790-804, December.
    16. Lorne N. Switzer & Cagdas Tahaoglu & Yun Zhao, 2017. "Volatility measures as predictors of extreme returns," Review of Financial Economics, John Wiley & Sons, vol. 35(1), pages 1-10, November.
    17. Dilip Kumar, 2020. "Value-at-Risk in the Presence of Structural Breaks Using Unbiased Extreme Value Volatility Estimator," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 18(3), pages 587-610, September.
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    19. Joelle Miffre, 2008. "Conditional Risk Premia in International Government Bond Markets," Multinational Finance Journal, Multinational Finance Journal, vol. 12(3-4), pages 185-204, September.

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