The study evaluated the sensitivity of the Value- at- Risk (VaR) and Expected Shortfalls (ES) with respect to portfolio allocation in emerging markets with an index portfolio of a developed market. This study utilised different models for VaR and ES techniques using various scenario-based models such as Covariance Methods, Historical Simulation and the GARCH (1, 1) for the predictive ability of these models in both relatively stable market conditions and extreme market conditions. The results showed that Expected Shortfall has less risk tolerance than VaR based on the same scenario-based market risk measures
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Barry C. B. & Rodriguez M. 2004. Risk and return characteristics of property indices in emerging markets. Emerging Markets Review 5: 131-159.
Berger T. 2013. Forecasting value-at-risk using time varying copulas and EVT return distributions. Journal of International Economics 133: 93-106.
Chen Q. & Chen R. 2013. Method of Value-at-Risk and empirical research for Shanghai stock market. Procedia Computer Science 17: 671-677.
Cheng G. Li P. & Shi P. 2007. A new algorithm based on copulas for VaR valuation with empirical calculations. Journal of Theoretical Computer Science 378: 190-197.
Fan Y. Wei Y. & Xu W. 2004. Application of VaR methodology to risk management in the stock market in China. Journal of Computers & Industrial Engineering 46: 383-388.
Girard E. Rahman H. & Zaher T. 2003. On market price of risk in Asian capital markets around Asian Flu. International Review of Financial Analysis 12: 241-265.
Gourieroux C. Laurent J. P. & Scaillet O. 2000. Sensitivity analysis of Value at Risk.Journal of Empirical Finance 7: 225-245.
Haas M. 2009. Value- at- Risk via mixture distributions reconsidered. Journal of applied mathematics and Computation 215: 2103-2119.
Lee C. Shie F. S. & Chang Y. C. 2012. How Close a relationship does a capital market have with another such market? The case of Taiwan from the Asian Financial crisis. Pacific- Basin Finance Journal 20: 349-362.
Longin F. M. 2000. From value at risk to stress testing: The extreme value approach. Journal of Banking & Finance 24: 1097-1130.
McNeil A. J. Frey R. & Embrechts P. 2005. Quantitative Risk Management: Concepts Techniques and Tools. Oxfordshire Princeton University Press.
McNeil A.J. Frey R. and Embrechts P. 2006. Quantitative Risk Management: Concepts Techniques and Tools. Oxfordshire Princeton University Press.
Prem K. P. Ng D. Pasman H. J. Sawyer M. Guo Y. &Mannan M. S. 2010. Risk measures constituting a risk metrics which improved decision making: Value- at- Risk. Journal of Loss Prevention in the Process Industries 23: 211-219.
Rockafellar T. R.& Uryasev S. 2002. Conditional Value-atrisk for general loss distributions. Journal of Banking & Finance 26: 1443-1471.
Rossignolo A. F. Fethi M. D. & Shaban M. 2012. Value- at- Risk models and Basel capital charges Evidence from Emerging and Frontier stock markets. Journal of Financial Stability 8: 303-319.
Yamai Y. and Yoshiba T. 2005. Value- at- risk versus expected shortfall: A practical perspective. Journal of Banking & Finance 29: 997-1015.