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Contributions of a noisy chaotic model to the stressed Value-at-Risk

Author

Listed:
  • Rachida Hennani

    (Université de Montpellier, UMR 5474 LAMETA, F-34000 Montpellier, France)

  • Michel Terraza

    (Université de Montpellier, UMR 5474 LAMETA, F-34000 Montpellier, France)

Abstract

The weaknesses of current Value-at-Risk (VaR) measure led the Basel Committee to revise the Basel II market risk framework. A stressed VaR measure is introduced to incorporate the violent behaviour of financial markets during crisis periods. This requirement allows the pro-cyclicality of the current VaR to be removed. However, this solution does not solve the problem related to the VaR estimation, including the choice of an appropriate model in a parametric approach. The forecasts of those models must comply with the assumptions of unconditional coverage and independence. In this paper, we evaluate the contribution of a noisy chaotic model for estimating the VaR measure in a crisis period. The simultaneous consideration of heteroskedastic and chaotic structures leads to a better forecast of the returns (Kyrtsou and Terraza (2010)). This clarification relative to the GARCH (1,1) model is used in this paper for predicting the stressed VaR of a portfolio built according to the mean-Gini criterion. The forecasting exercise, evaluated by backtesting tests, shows an outperformance of the Mackey-Glass-GARCH (1,1) model.

Suggested Citation

  • Rachida Hennani & Michel Terraza, 2015. "Contributions of a noisy chaotic model to the stressed Value-at-Risk," Economics Bulletin, AccessEcon, vol. 35(2), pages 1262-1273.
    • Handle: RePEc:ebl:ecbull:eb-14-00096
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    References listed on IDEAS

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    More about this item

    Keywords

    Value-at-risk; Mackey-Glass model; GARCH; backtesting tests; Mean-Gini portfolio; dynamical and chaotic systems;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • G0 - Financial Economics - - General

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