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

Mária Bohdalová () and Michal Greguš ()
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Mária Bohdalová: Faculty of Management, Comenius University in Bratislava
Michal Greguš: Faculty of Management, Comenius University in Bratislava

CBU International Conference Proceedings, 2015, vol. 3, issue 0, 188-195

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.

Keywords: Value at Riskleptokurtic distribution; skewed distribution; normal mixture distribution; Monte Carlo simulation (search for similar items in EconPapers)
JEL-codes: C13 C15 G10 G17 (search for similar items in EconPapers)
Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:aad:iseicj:v:3:y:2015:i:0:p:188-195

DOI: 10.12955/cbup.v3.601

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