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Estimating Value-At-Risk Based On Non-Normal Distributions

Author

Listed:
  • Mária Bohdalová

    (Faculty of Management, Comenius University in Bratislava)

  • Michal Greguš

    (Faculty of Management, Comenius University in Bratislava)

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.

Suggested Citation

  • Mária Bohdalová & Michal Greguš, 2015. "Estimating Value-At-Risk Based On Non-Normal Distributions," CBU International Conference Proceedings, ISE Research Institute, vol. 3(0), pages 188-195, September.
    • Handle: RePEc:aad:iseicj:v:3:y:2015:i:0:p:188-195
    DOI: 10.12955/cbup.v3.601
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    More about this item

    Keywords

    Value at Riskleptokurtic distribution; skewed distribution; normal mixture distribution; Monte Carlo simulation;
    All these keywords.

    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General

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