An Application of Extreme Value Theory as a Risk Measurement Approach in Frontier Markets

In this paper, we consider the application of Extreme
Value Theory as a risk measurement tool. The Value at Risk, for a set
of indices, from six Stock Exchanges of Frontier markets is
calculated using the Peaks over Threshold method and the
performance of the model index-wise is evaluated using coverage
tests and loss functions. Our results show that “fattailedness” alone of
the data is not enough to justify the use of EVT as a VaR approach.
The structure of the returns dynamics is also a determining factor.
This approach works fine in markets which have had extremes
occurring in the past thus making the model capable of coping with
extremes coming up (Colombo, Tunisia and Zagreb Stock
Exchanges). On the other hand, we find that indices with lower past
than present volatility fail to adequately deal with future extremes
(Mauritius and Kazakhstan). We also conclude that using EVT alone
produces quite static VaR figures not reflecting the actual dynamics
of the data.





References:
[1] Aboura, S. (2009), `The extreme downside risk of the s & p 500 stock
index', Journal of Financial Transformation 26, 104-107.
[2] Andjelic, G., Milosev, I. &Djakovic, V. (2010), `Extreme Value Theory
in Emerging markets', Economic Annals 55.
[3] Balkema, A. & de Haan, L. (1974), ‘Residual lifetime at great age',
Annals of Probability 2, 792-804.
[4] Christoffersen, P. (1998), `Evaluating interval forecast', Journal of
Business and Economic Statistics 39, 841-862.
[5] da Silva, A. L. C. & de Melo Mendes, B. V. (2003), ‘Value at risk and
extreme returns in asian stock markets', International Journal of Business
8(1), 17-40.
[6] Embrechts, P. (2000), `Extreme Value Theory: Potentials and
Limitations as an Integrated Risk Management Tool', Derivatives Use,
Trading and Regulation 6, 449-456.
[7] Embrechts, P., Resnick, S. &Samoronitstky, G. (1999), ‘Extreme Value
Theory As a Risk Management Tool', North American Actuarial Journal
3, 30 - 41.
[8] Gencay, R.&Selcuk, F. (2004), ‘Extreme value theory and value-at-risk:
Relative performance in emerging markets', International Journal of
Forecasting 20, 287 - 303.
[9] Gencay, R., Selcuk, F. &Ulugulyagci, A. (2003), ‘High volatility, thick
tails and extreme value theory in value-at-risk estimation', Insurance:
Mathematics and Economics 33, 337 - 356.
[10] Kupiec, P. (1995), ‘Techniques for Verifying the Accuracy of Risk
Measurement Models', Journal of Derivatives 3, 73 - 84.
[11] Mutu, S., Balogh, P.& Moldovan, D. (2011), ‘The Efficiency of value at
risk models on central and eastern European markets', International
Journal of Mathematics and Computers 5, 110 - 117.
[12] Pickands, J. (1975). "Statistical inference using extreme order
statistics", Annals of Statistics, 3, 119–131.
[13] Tsay, R. S. (2010), Financial Time Series and Their Characteristics,
John Wiley and Sons, Inc., pp. 1 - 27.
[14] Wentzel, B. & Mare, E. (2007), ‘Extreme Value Theory - An
Application to the South African equity market', Investment Analysts
Journal 66, 73 - 77.