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Performance of fat-tailed Value-at-risk: A comparison using backtesting on the OMXS30
Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
2014 (English)Independent thesis Advanced level (degree of Master (One Year)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

The aim of this thesis is to test if the application of fat tailed distributions in value-at-risk models is of better use for risk managers than the Normal distribution. Value-at-risk is a regulatory tool used in Basel regulations. Basel II and III regulate capital required by banks according to value-at-risk backtest results. Value-at-risk is therefore of great importance for financial institutions and banks. The models used for the value-at-risk estimation are rolling ARMA(1,1)-GARCH(1,1) models with Normal, Student’s t, and Generalized hyperbolic distributed errors. The performance of the value-at-risk models was estimated using backtest forecasting on a thousand day out-of-sample window, based on the OMXS30 index. Results reveal that the normal value-at-risk model performs worse compared to the non-normal value-at-risk models. Density forecasts show that value-at-risk estimates directly benefit from including parameters of kurtosis. However, evaluation tests show that none of the models underestimate value-at-risk, and therefore the rejection of the Normal distribution in value-at-risk estimation is not sufficiently justified.

Place, publisher, year, edition, pages
2014. , 62 p.
Keyword [en]
Risk management, Value-at-Risk, VaR, Fat-tails, Backtesting, GARCH
National Category
Business Administration
URN: urn:nbn:se:hj:diva-24002OAI: diva2:725959
Subject / course
IHH, Business Administration
Available from: 2015-06-22 Created: 2014-06-05 Last updated: 2015-06-22Bibliographically approved

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