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Decay factor optimisation in time weighted simulation -- Evaluating VaR performance

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  • Zikovic, Sasa
  • Aktan, Bora

Abstract

We propose an optimisation approach for determining the optimal decay factor in time weighted (BRW) simulation. The backtesting of the BRW simulation, which involves different decay factors, together with a broad range of competing VaR models, has been performed on a sample of seven stock indexes and two commodities: gold and WTI oil. The results obtained show that the BRW simulation with an optimised decay factor relative to the Lopez (1998) size-adjusted function is among the best performing VaR models, second only to the conditional extreme value approach (McNeil & Frey, 2000). The optimised decay factors are sufficiently stable over time, giving economic justification to the optimisation because they do not change over longer time periods. Unlike most of the VaR models tested, in the large majority of cases, the optimised BRW model passes the Basel II criteria but yields significantly lower VaR forecasts than the extreme value approaches, thus resulting in a lower idle capital, i.e. lower costs.

Suggested Citation

  • Zikovic, Sasa & Aktan, Bora, 2011. "Decay factor optimisation in time weighted simulation -- Evaluating VaR performance," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1147-1159, October.
    • Handle: RePEc:eee:intfor:v:27:y:2011:i:4:p:1147-1159
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    References listed on IDEAS

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    Cited by:

    1. Gery Geenens & Richard Dunn, 2017. "A nonparametric copula approach to conditional Value-at-Risk," Papers 1712.05527, arXiv.org, revised Oct 2019.
    2. Saša ŽIKOVIÆ & Randall K. FILER, 2013. "Ranking of VaR and ES Models: Performance in Developed and Emerging Markets," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 63(4), pages 327-359, August.
    3. Geenens, Gery & Dunn, Richard, 2022. "A nonparametric copula approach to conditional Value-at-Risk," Econometrics and Statistics, Elsevier, vol. 21(C), pages 19-37.
    4. Zolotko, Mikhail & Okhrin, Ostap, 2014. "Modelling the general dependence between commodity forward curves," Energy Economics, Elsevier, vol. 43(C), pages 284-296.
    5. Nieto, Maria Rosa & Ruiz, Esther, 2016. "Frontiers in VaR forecasting and backtesting," International Journal of Forecasting, Elsevier, vol. 32(2), pages 475-501.

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