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

Mária Bohdalová, Michal Greguš

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 Risk, leptokurtic distribution, skewed distribution, normal mixture distribution, Monte Carlo simulation

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DOI: http://dx.doi.org/10.12955/cbup.v3.601

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