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Value-at-risk for long and short trading positions: Evidence from developed and emerging equity markets

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  • Diamandis, Panayiotis F.
  • Drakos, Anastassios A.
  • Kouretas, Georgios P.
  • Zarangas, Leonidas

Abstract

The financial crisis of 2007-2009 has questioned the provisions of Basel II agreement on capital adequacy requirements and the appropriateness of VaR measurement. This paper reconsiders the use of Value-at-risk as a measure for potential risk of economic losses in financial markets by estimating VaR for daily stock returns with the application of various parametric univariate models that belong to the class of ARCH models which are based on the skewed Student distribution. We used daily data for three groups of stock market indices, namely Developed, Southeast Asia and Latin America. The data covered the period 1987-2009. We conducted our analysis with the adoption of the methodology suggested by Giot and Laurent (2003). Therefore, we estimated an APARCH model based on the skewed Student distribution to fully take into account the fat left and right tails of the returns distribution. The main finding of our analysis is that the skewed Student APARCH improves considerably the forecasts of one-day-ahead VaR for long and short trading positions. Additionally, we evaluate the performance of each model with the calculation of Kupiec's (1995) Likelihood Ratio test on the empirical failure test. Moreover, for the case of the skewed Student APARCH model we computed the expected shortfall and the average multiple of tail event to risk measure. These two measures helped us to further assess the information we obtained from the estimation of the empirical failure rates.

Suggested Citation

  • Diamandis, Panayiotis F. & Drakos, Anastassios A. & Kouretas, Georgios P. & Zarangas, Leonidas, 2011. "Value-at-risk for long and short trading positions: Evidence from developed and emerging equity markets," International Review of Financial Analysis, Elsevier, vol. 20(3), pages 165-176, June.
  • Handle: RePEc:eee:finana:v:20:y:2011:i:3:p:165-176
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    3. 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.
    4. Gao, Chun-Ting & Zhou, Xiao-Hua, 2016. "Forecasting VaR and ES using dynamic conditional score models and skew Student distribution," Economic Modelling, Elsevier, vol. 53(C), pages 216-223.
    5. Nikola Radivojevic & Milena Cvjetkovic & Saša Stepanov, 2016. "The new hybrid value at risk approach based on the extreme value theory," Estudios de Economia, University of Chile, Department of Economics, vol. 43(1 Year 20), pages 29-52, June.
    6. Stavroyiannis, S. & Makris, I. & Nikolaidis, V. & Zarangas, L., 2012. "Econometric modeling and value-at-risk using the Pearson type-IV distribution," International Review of Financial Analysis, Elsevier, vol. 22(C), pages 10-17.
    7. 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.
    8. Nikola Radivojević & Nikola V. Ćurčić & Djurdjica Dj. Vukajlović, 2017. "Hull-White’s value at risk model: case study of Baltic equities market," Journal of Business Economics and Management, Taylor & Francis Journals, vol. 18(5), pages 1023-1041, September.
    9. 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.
    10. Raúl de Jesús-Gutiérrez & Roberto J. Santillán-Salgado, 2019. "Conditional Extreme Values Theory and Tail-related Risk Measures: Evidence from Latin American Stock Markets," International Journal of Economics and Financial Issues, Econjournals, vol. 9(3), pages 127-141.
    11. Slim, Skander & Koubaa, Yosra & BenSaïda, Ahmed, 2017. "Value-at-Risk under Lévy GARCH models: Evidence from global stock markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 46(C), pages 30-53.
    12. 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.

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