Tail risk in emerging markets of Southeastern Europe

ABSTRACT

This article examines the left-tail behaviour of returns on stocks in Southeastern Europe (SEE). We apply conditional extreme value theory (EVT) approach on daily returns of six stock market indices from SEE between 2004 and 2013. Predictive performance of value-at-risk (VaR) and expected shortfall (ES) based on EVT is compared against several alternatives, such as historical simulation and analytical approach based on GARCH with a single conditional distribution. Model backtesting with daily returns shows that EVT-based models provide more reliable VaR and ES forecasts than the alternative models in all six markets. Unlike the alternatives, the EVT-based models cannot be rejected as VaR confidence level is increased. This emphasizes the importance of extreme events in SEE markets and indicates that the ability of a model to capture volatility clustering accurately is not sufficient for a correct assessment of risk in these markets.

KEYWORDS:

• Fat tails
• extreme value theory
• peaks over threshold
• backtesting

JEL Classification:

• G15
• G17
• G32

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

¹ This choice can be also justified by the mean-excess plots. See Fig. A1 in Appendix 1.

² See, for example, in Artzner et al. (1999) or Acerbi and Tasche (2002).

³ This can be also seen from the violation ratios in Table 2.

⁴ Other parametric alternatives can be also considered. For instance, we can assume different distributions (such as skewed Student’s t distribution), alternative GARCH specifications (such as GJR model of Glosten, Jagannathan, and Runkle 1993; EGARCH of Nelson 1991; or a more general APARCH model of Ding, Granger, and Engle 1993) or work with a higher number of lags. However, our intention here is to emphasize the role of tail risk in SEE markets, rather than finding the best fitting model.

Additional information

Funding

MB acknowledges financial support of the Ministry of Education, Science and Technological Development of the Republic of Serbia [grant number 179005].