A Comparative Analysis of Value at Risk Measurement on Emerging Stock Markets: Case of Montenegro

• Authors: Julija Cerović , Milena Lipovina-Božović , Saša Vujošević
• Languages of publication: EN

Abstracts

EN

Background: The concept of value at risk gives estimation of the maximum loss of financial position at a given time for a given probability. The motivation for this analysis lies in the desire to devote necessary attention to risks in Montenegro, and to approach to quantifying and managing risk more thoroughly. Objectives: This paper considers adequacy of the most recent approaches for quantifying market risk, especially of methods that are in the basis of extreme value theory, in Montenegrin emerging market before and during the global financial crisis. In particular, the purpose of the paper is to investigate whether extreme value theory outperforms econometric and quantile evaluation of VaR in emerging stock markets such as Montenegrin market. Methods/Approach: Daily return of Montenegrin stock market index MONEX20 is analyzed for the period January, 2004 - February, 2014. Value at Risk results based on GARCH models, quantile estimation and extreme value theory are compared. Results: Results of the empirical analysis show that the assessments of Value at Risk based on extreme value theory outperform econometric and quantile evaluations. Conclusions: It is obvious that econometric evaluations (ARMA(2,0)- GARCH(1,1) and RiskMetrics) proved to be on the lower bound of possible Value at Risk movements. Risk estimation on emerging markets can be focused on methodology using extreme value theory that is more sophisticated as it has been proven to be the most cautious model when dealing with turbulent times and financial turmoil.

Keywords

EN

extreme value theory; Value at Risk; fat tails; GARCH; RiskMetrics; peak over threshold; generalized Pareto distribution

• Publisher: Sciendo
• Journal: Business Systems Research Journal
• Year: 2015
• Volume: 6
• Issue: 1
• Pages: 36-55

Dates

published

2015-03-01

received

2014-05-10

accepted

2014-10-31

online

2015-05-09

Contributors

author

Julija Cerović

• University of Montenegro, Faculty of Economics, Podgorica, Montenegro

author

Milena Lipovina-Božović

• University of Montenegro, Faculty of Economics, Podgorica, Montenegro

author

Saša Vujošević

• University of Montenegro, Faculty of Economics, Podgorica, Montenegro

References

• 1. Alexander, C. O., Leigh, C. T. (1997), “On the covariance matrices used in Value at Risk models”, Journal of Derivatives, Vol. 4, No. 3, pp. 50-62.[Crossref]
• 2. Anđelić, G., Djaković, V., Radišić, S. (2010), “Application of VaR in Emerging markets: A Case of Selected Central and Eastern European Countries”, African Journal of Business Management, Vol. 4, No. 17, pp. 3666-3680.
• 3. Anđelić, G., Milošev, I., Djaković, V. (2010), “Extreme value theory in emerging markets”, Economic Annals, Vol. LV, No. 185, pp. 63-106.
• 4. Angelidis, T., Benos, A., Degiannakis, S. (2004), “The use of GARCH models in VaR estimation”, Statistical Methodology, Vol. 1, No. 2, pp. 105-128.
• 5. Bollerslev, T. (1986), “Generalized autoregressive conditional heteroskedasticity”, Journal of Econometrics, Vol. 31, No. 3, pp. 307-327.[WoS][Crossref]
• 6. Bao, Y., Lee, T., Saltoglu, B. (2006), “Evaluating Predictive Performance of Value-at-Risk Models in Emerging Markets: A Reality Check”, Journal of Forecasting, Vol. 25, No. 2, pp. 101-128.[Crossref]
• 7. Bučevska, V. (2013), “An Empirical Evaluation of GARCH Models in Value-at-Risk Estimation: Evidence from the Macedonian Stock Exchange”, Business Systems Research, Vol. 4, No. 1, pp. 49-64.
• 8. Cerović, J. (2014), “Value at risk measuring and Extreme value theory: Evidence from Montenegro”, Facta Universitatis, Series: Economics and Organization, Vol. 11, No. 2, pp. 175-189.
• 9. Christoffersen, P., Hahn, J., Inoue, A. (2001), “Testing and Comparing Value-at-Risk Measures”, Journal of Empirical Finance, Vol. 8, No. 3, pp. 325-342.
• 10. Da Silva, A., Beatriz, V., de Melo Mendes, B., (2003), “Value-at-Risk and Extreme Returns in Asian Stock markets”, International Journal of Business, Vol. 8, No. 1, pp. 17-40.
• 11. De Haan, L., Ferreira, A. (2006). Extreme Value Theory: an Introduction. New York: Springer Science+Business Media LLC.
• 12. Embrechts, P., Resnick, S. I., Samorodnitsky, G. (1998), “Living on the edge”, Risk, Vol. 11, No. 1, pp. 96-100.
• 13. Engle, R. F. (1982), “Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom inflation”, Econometrica, Vol. 50, No. 4, pp. 987-1007.[Crossref]
• 14. Gençay, R., Selçuk, F. (2004), „Extreme value theory and value-at-risk: Relative performance in emerging markets“, International Journal of Forecasting, Vol. 20, No. 2, pp. 287-303.[Crossref]
• 15. Gilli, M., Kellezi, E. (2006), “An Application of Extreme Value Theory for Measuring Financial Risk”, Computational Economics, Vol. 27, No. 2, pp. 207-228.[Crossref]
• 16. Guermat, C., Harris, D. F. (2002), “Forecasting value at risk allowing for time and kurtosis of portfolio returns”, International Journal of Forecasting, Vol. 18, No. 3, pp. 409-419.[Crossref]
• 17. Harmantzis, F., L. Miao, Chien, Y. (2006), “Empirical Study of Value-at-Risk and Expected Shortfall Models with Heavy Tails”, Journal of Risk Finance, Vol. 7, No. 2, pp. 117-135.
• 18. Jorion, P. (2007). Value at Risk: The New Benchmark for Managing Financial Risk, 3rd ed., New York: McGraw-Hill.
• 19. Karadžić, V., Cerović, J. (2014), “Market Risk of the Western Balkans countries during the global financial crisis”, Economic Annals-XXI, Vol. 2014, No. 11-12, pp. 19-23.
• 20. Longerstaey, J., More, L. (1995). Introduction to RiskMetrics. Morgan Guaranty Trust Company.
• 21. Longin, F. M. (1996), “The Asymptotic Distribution of Extreme Stock Market Returns”, Journal of Business, Vol. 69, No. 3, pp. 383-408.[Crossref]
• 22. Manganelli, S., Engle, R. F. (2001), “Value at Risk Models in Finance”, available at: http://ssrn.com/abstract=356220 (12 December 2011)
• 23. McNeil, A. J., Frey, R. (2000), “Estimation of tail-related risk measures for heteroscedastic financial time series: An extreme value approach”, Journal of Empirical Finance, Vol. 7, No. 3-4, pp. 271- 300.
• 24. McNeil, A.J., Frey, R., Embrechts, P., (2005). Quantitative Risk Management. Princeton: Princeton University Press.
• 25. Mladenović Z., Miletić, M., Miletić, S. (2012), “Value at Risk in European Emerging Economies: Empirical Assessment of Financial Crisis Period”, paper presented at Scientific Conference: from Global Crisis to Economic Growth: which way to take?, 20.09-22.09.2012, Faculty of Economics, Belgrade.
• 26. Montenegroberza AD Podgorica, available at http://www.montenegroberza.com (18.06.2013).
• 27. Nikolić-Đorić, E., Đorić, D. (2011), “Dynamic Value at Risk Estimation for BELEX15”, Metodološki zvezki, Vol. 8, No. 1, pp. 79-98.
• 28. Onour, I. A. (2010), “Extreme Risk and Fat-Tails Distribution: Empirical Analysis”, Journal of Money, Investment and Banking, Vol. 13, No. 13, pp. 27-34.
• 29. Taylor, S. (1986). Modelling Financial Time Series. Chichester: Wiley.
• 30. Tsay, Ruey, S. (2010). Analysis of Financial Time Series, Third Edition. Hoboken, New Jersey: John Wiley & Sons, Inc.
• 31. Žiković, S., Aktan, B. (2009), “Global financial crisis and VaR performance in emerging markets: A case of EU candidate state - Turkey and Croatia”, Zbornik radova Ekonomskog fakulteta u Rijeci, Vol. 27, No. 1, pp.145-170.
• 32. Žiković, S., (2007), “Measuring Market Risk in EU New Member States”, available at: http://www.hnb.hr/dub-konf/13-konferencija/zivkovic.pdf (12 December 2011)
• 33. Wong, C. S., Cheng, Y. W., Wong, Y. P. (2002), “Market risk management of banks: Implications from the accuracy of VaR forecasts”, Journal of Forecasting, Vol. 22, No. 1, pp. 22-33.

Identifiers

DOI

10.1515/bsrj-2015-0003