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Open AccessArticle

The Realized Hierarchical Archimedean Copula in Risk Modelling

1
Chair of Econometrics and Statistics esp. Transportation, Institute of Economics and Transport, Faculty of Transportation, Dresden University of Technology, Helmholtzstraße 10, 01069 Dresden, Germany
2
Chair of Mathematics and Statistics, University of St Gallen, Bodanstrasse 6, 9000 St Gallen, Switzerland
*
Author to whom correspondence should be addressed.
Academic Editor: Jean-David Fermanian
Econometrics 2017, 5(2), 26; https://doi.org/10.3390/econometrics5020026
Received: 31 December 2016 / Revised: 2 June 2017 / Accepted: 6 June 2017 / Published: 15 June 2017
(This article belongs to the Special Issue Recent Developments in Copula Models)
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. View Full-Text
Keywords: multivariate dependence; copula; HAC; realized copula; realized covariance; value at risk multivariate dependence; copula; HAC; realized copula; realized covariance; value at risk
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Okhrin, O.; Tetereva, A. The Realized Hierarchical Archimedean Copula in Risk Modelling. Econometrics 2017, 5, 26.

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