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On conditional risk estimation considering model risk

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  • Fedya Telmoudi
  • Mohamed EL Ghourabi
  • Mohamed Limam

Abstract

Usually, parametric procedures used for conditional variance modelling are associated with model risk. Model risk may affect the volatility and conditional value at risk estimation process either due to estimation or misspecification risks. Hence, non-parametric artificial intelligence models can be considered as alternative models given that they do not rely on an explicit form of the volatility. In this paper, we consider the least-squares support vector regression (LS-SVR), weighted LS-SVR and Fixed size LS-SVR models in order to handle the problem of conditional risk estimation taking into account issues of model risk. A simulation study and a real application show the performance of proposed volatility and VaR models.

Suggested Citation

  • Fedya Telmoudi & Mohamed EL Ghourabi & Mohamed Limam, 2016. "On conditional risk estimation considering model risk," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(8), pages 1386-1399, June.
    • Handle: RePEc:taf:japsta:v:43:y:2016:i:8:p:1386-1399
    DOI: 10.1080/02664763.2015.1100595
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    Cited by:

    1. Fernanda Maria Müller & Marcelo Brutti Righi, 2024. "Comparison of Value at Risk (VaR) Multivariate Forecast Models," Computational Economics, Springer;Society for Computational Economics, vol. 63(1), pages 75-110, January.
    2. Fernanda Maria Müller & Marcelo Brutti Righi, 2018. "Numerical comparison of multivariate models to forecasting risk measures," Risk Management, Palgrave Macmillan, vol. 20(1), pages 29-50, February.

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