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Capturing information in extreme events

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  • Ardakani, Omid M.

Abstract

This study integrates information theory and extreme value theory to enhance the prediction of extreme events. Information-theoretic measures provide a foundation for model comparison in tails. The theoretical findings suggest that (1) the entropy of block maxima converges to the entropy of the generalized extreme value distribution, (2) the rate of convergence is controlled by its shape parameter, and (3) the entropy of block maxima is a monotonically decreasing function of the block size. Empirical analysis of E-mini S&P, 500 futures data evaluates the financial risk, capturing information content of extreme events using entropy and Kullback–Leibler divergence.

Suggested Citation

  • Ardakani, Omid M., 2023. "Capturing information in extreme events," Economics Letters, Elsevier, vol. 231(C).
    • Handle: RePEc:eee:ecolet:v:231:y:2023:i:c:s0165176523003269
    DOI: 10.1016/j.econlet.2023.111301
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    References listed on IDEAS

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    More about this item

    Keywords

    Entropy; Generalized extreme value distribution; Generalized Pareto; Kullback–Leibler divergence; Tail risk;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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