Journal Article10.1016/J.JBANKFIN.2015.01.008
Mixture pair-copula-constructions
Gregor N.F. Weiß,Marcus Scheffer +1 more
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TL;DR: In this paper, convex combinations of parametric copulas are used as pair-copulas in high-dimensional vine copula models to circumvent the error-prone need to choose and estimate a parametriccopula for each paircopula in a vine model.
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Abstract: We propose the use of convex combinations of parametric copulas as pair-copulas in high-dimensional vine copula models. By doing so, we circumvent the error-prone need to choose and estimate a parametric copula for each pair-copula in a vine model. We show in simulations that our proposed model fits the dependence structure in a given data sample significantly better than a competing benchmark. In our empirical study on the models’ accuracy for forecasting the Value-at-Risk of financial portfolios, we show that our proposed mixture pair-copula construction yields significantly better results in backtesting while the benchmark overestimates portfolio risk.
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Citations
Measuring systemic risk in the European banking sector: A Copula CoVaR approach
TL;DR: In this paper, a new methodology based on copula functions to estimate CoVaR, the value-at-risk (VaR) of the financial system conditional on an institution being under financial distress, is proposed.
The profitability of pairs trading strategies: distance, cointegration and copula methods
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Probabilistic optimal power flow considering dependences of wind speed among wind farms by pair-copula method
TL;DR: Based on the pair-copula method to construct high dimensional dependences, the average relative errors of POPF results is smaller than that by other methods and the distribution curve of output variables is close to that obtained by using actual wind speed data.
72
Measuring Value-at-Risk and Expected Shortfall of crude oil portfolio using extreme value theory and vine copula
TL;DR: In this paper, the authors measured the Value-at-Risk (VaR) and Expected Shortfall (ES) of a portfolio consisting of four crude oil assets by using GARCH-type models, extreme value theory (EVT) and vine copulas.
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Structure and estimation of Lévy subordinated hierarchical Archimedean copulas (LSHAC): Theory and empirical tests
TL;DR: In this article, the authors propose a three-stage estimation procedure to determine the hierarchical structure and the parameters of a LSHAC model and empirically examine the modeling performances of the model using exchange traded funds.
17
References
Vines: A new graphical model for dependent random variables
Tim Bedford,Roger M. Cooke +1 more
TL;DR: A new graphical model, called a vine, for dependent random variables, which generalize the Markov trees often used in modelling high-dimensional distributions and is weakened to allow for various forms of conditional dependence.
A semiparametric estimation procedure of dependence parameters in multivariate families of distributions
TL;DR: In this article, the authors investigated the properties of a semiparametric method for estimating the dependence parameters in a family of multivariate distributions and proposed an estimator, obtained as a solution of a pseudo-likelihood equation, which is consistent, asymptotically normal and fully efficient at independence.
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On Default Correlation: A Copula Function Approach
TL;DR: In this paper, the authors introduce a random variable called "time-until-default" to denote the survival time of each defaultable entity or financial instrument, and define the default correlation between two credit risks as the correlation coefficient between their survival times.
Measuring reproducibility of high-throughput experiments
TL;DR: In this paper, the authors propose a unified approach to measure the reproducibility of findings identified from replicate experiments and identify putative discoveries using Reproducibility, which creates a curve, which quantitatively assesses when the findings are no longer consistent across replicates.
Probability Density Decomposition for Conditionally Dependent Random Variables Modeled by Vines
Tim Bedford,Roger M. Cooke +1 more
TL;DR: A general formula for the density of a vine dependent distribution is derived, which generalizes the well-known density formula for belief nets based on the decomposition of belief nets into cliques and allows a simple proof of the Information Decomposition Theorem for a regular vine.
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