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Value-at-Risk in Emerging Equity Markets: Comparative Evidence for Symmetric, Asymmetric, and Long-Memory GARCH Models

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  • DAVID G. McMILLAN
  • ALAN E. H. SPEIGHT

Abstract

This paper extends research concerned with the evaluation of alternative volatility forecasting methods under value at risk (VaR) modeling in the context of the Basle Committee adequacy criteria by broadening the class of generalized autoregressive conditional heteroscedasticity models, to include both asymmetric models and long memory models, in addition to the statistical methods commonly used in financial institutions. In the analysis of daily index data for eight emerging stock markets in the Asia - Pacific region, in addition to US and UK benchmark comparators, we find both asymmetric and long memory features to be important considerations in providing improved VaR estimates that minimize occasions when the minimum capital requirement identified by the VaR methodology would have fallen short of actual trading losses. More generally, our results illustrate the importance of adopting the stringent probability level stipulated in the regulatory framework, and of using fully out-of-sample forecast evaluation methods for the identification of forecasting models that mitigate the likelihood of inappropriately small VaRs and consequent regulatory intervention. Copyright (c) International Review of Finance Ltd. 2007.

Suggested Citation

  • DAVID G. McMILLAN & ALAN E. H. SPEIGHT, 2007. "Value-at-Risk in Emerging Equity Markets: Comparative Evidence for Symmetric, Asymmetric, and Long-Memory GARCH Models," International Review of Finance, International Review of Finance Ltd., vol. 7(1-2), pages 1-19.
  • Handle: RePEc:bla:irvfin:v:7:y:2007:i:1-2:p:1-19
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    Cited by:

    1. 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.
    2. Bucevska Vesna, 2013. "An Empirical Evaluation of GARCH Models in Value-at-Risk Estimation: Evidence from the Macedonian Stock Exchange," Business Systems Research, Sciendo, vol. 4(1), pages 49-64, March.
    3. Maghyereh Aktham Issa & Awartani Basel, 2012. "Modeling and Forecasting Value-at-Risk in the UAE Stock Markets: The Role of Long Memory, Fat Tails and Asymmetries in Return Innovations," Review of Middle East Economics and Finance, De Gruyter, vol. 8(1), pages 1-22, August.
    4. Aloui, Chaker & Hamida, Hela ben, 2014. "Modelling and forecasting value at risk and expected shortfall for GCC stock markets: Do long memory, structural breaks, asymmetry, and fat-tails matter?," The North American Journal of Economics and Finance, Elsevier, vol. 29(C), pages 349-380.
    5. Nico Katzke & Chris Garbers, 2015. "Do Long Memory and Asymmetries Matter When Assessing Downside Return Risk?," Working Papers 06/2015, Stellenbosch University, Department of Economics.

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