ESTIMATING VALUE-AT-RISK BASED ON NON-NORMAL DISTRIBUTIONS

  • Mária Bohdalová Faculty of Management, Comenius University in Bratislava
  • Michal Greguš Faculty of Management, Comenius University in Bratislava
Keywords: Value at Risk, leptokurtic distribution, skewed distribution, normal mixture distribution, Monte Carlo simulation

Abstract

The article presents a comparative study of parametric linear value-at-risk (VaR) models used for estimating the risk of financial portfolios. We illustrate how to adjust VaR for auto-correlation in portfolio returns. The article presents static and dynamic methodology to compute VaR, based on the assumption that daily changes are independent and identically distributed (normal or non-normal) or auto-correlated in terms of the risk factor dynamics. We estimate the parametric linear VaR over a risk horizon of 1 day and 10 days at 99% and 95% confidence levels for the same data. We compare the parametric VaR and a VaR obtained using Monte Carlo simulations with historical simulations and use the maximum likelihood method to calibrate the distribution parameters of our risk factors. The study investigated whether the parametric linear VaR applies to contemporary risk factor analysis and pertained to selected foreign rates.

References

Allen, S. (2003). Financial risk management. A practitioner’s guide to managing market and credit risk. New Jersey: John Wiley & Sons.

Alexander, C. (2008). Market risk analysis. Chichester: John Wiley & Sons.

Barker, J.T. (2015). Why is bitcoin's value so volatile? Investopedia, LLC. Retrieved May 20, 2015 from http://www.investopedia.com/articles/investing/052014/why-bitcoins-value-so-volatile.asp

Bohdalová, M., & Greguš, M. (2013). VaR based risk management. In P. Hájek (Ed.), CBU International Conference Proceedings (pp. 25-33). Prague, Czeck Republic Central Bohemia University. doi: 10.12955/cbup.2013.11

Cable News Network (2015, January 5). 3 reasons the euro is plunging [CNN Money update]. Retrieved July 26, 2015 from http://money.cnn.com/2015/01/05/investing/euro-slump-deepens/index.html

Dowd, K. (2002). An introduction to market risk measurement. Chichester: John Wiley & Sons.

Duffie, D., & Pan, J. (1997). An overview of value at risk. Journal of Derivatives, Spring, 7-48.

Easwaran, S., Dixit, M., & Sinha, S. (2015). Bitcoin dynamics: the inverse square law of price fluctuations and other stylized facts. In F. Abergel, H. Aoyama, B. K. Chakrabarti, A. Chakraborti & A. Ghosh (Eds.) Econophysics and data driven modelling of market dynamics (pp. 121-128). Switzerland: Springer International Publishing. doi: 10.1007/978-3-319-08473-2_4

European Commission. (n.d.). Economic and financial affairs. Retrieved July 26, 2015 from http://ec.europa.eu/economy_finance/euro/index_en.html

Fusion Media Ltd. (n.d.). Currency Rates. Retrieved from http://www.investing.com/currencies

Jorion, P. (2003). Financial risk manager handbook. New Jersey: John Wiley & Sons.

Jorion, P. (2006). Value at risk: the new benchmark for managing financial risk. New York: McGraw-Hill.

Říhová, V., Mošová, V., Valentová, I., Říha, J., Slezák, V., Pászto, V., Chmela, L., Zielina, M., & Smrčka, D. (2015). Software mathematica for economist. Olomouc: EPAVA.

Tsay, R. S. (2010). Analysis of financial times series. New Jersey: John Wiley & Sons.

Published
2015-09-19