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The Realized Hierarchical Archimedean Copula in Risk Modelling

Author

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  • Ostap Okhrin

    (Chair of Econometrics and Statistics esp. Transportation, Institute of Economics and Transport, Faculty of Transportation, Dresden University of Technology, Helmholtzstraße 10, 01069 Dresden, Germany)

  • Anastasija Tetereva

    (Chair of Mathematics and Statistics, University of St Gallen, Bodanstrasse 6, 9000 St Gallen, Switzerland)

Abstract

This paper introduces the concept of the realized hierarchical Archimedean copula (rHAC). The proposed approach inherits the ability of the copula to capture the dependencies among financial time series, and combines it with additional information contained in high-frequency data. The considered model does not suffer from the curse of dimensionality, and is able to accurately predict high-dimensional distributions. This flexibility is obtained by using a hierarchical structure in the copula. The time variability of the model is provided by daily forecasts of the realized correlation matrix, which is used to estimate the structure and the parameters of the rHAC. Extensive simulation studies show the validity of the estimator based on this realized correlation matrix, and its performance, in comparison to the benchmark models. The application of the estimator to one-day-ahead Value at Risk (VaR) prediction using high-frequency data exhibits good forecasting properties for a multivariate portfolio.

Suggested Citation

  • Ostap Okhrin & Anastasija Tetereva, 2017. "The Realized Hierarchical Archimedean Copula in Risk Modelling," Econometrics, MDPI, vol. 5(2), pages 1-31, June.
    • Handle: RePEc:gam:jecnmx:v:5:y:2017:i:2:p:26-:d:101602
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    References listed on IDEAS

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    4. Alessandro Barbiero, 2021. "Inducing a desired value of correlation between two point-scale variables: a two-step procedure using copulas," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 105(2), pages 307-334, June.
    5. Perreault, Samuel & Duchesne, Thierry & Nešlehová, Johanna G., 2019. "Detection of block-exchangeable structure in large-scale correlation matrices," Journal of Multivariate Analysis, Elsevier, vol. 169(C), pages 400-422.
    6. Dodo Natatou Moutari & Hassane Abba Mallam & Diakarya Barro & Bisso Saley, 2021. "Dependence Modeling and Risk Assessment of a Financial Portfolio with ARMA-APARCH-EVT models based on HACs," Papers 2105.09473, arXiv.org.

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