Copula-Based Top-Down Approaches in Financial Risk Aggregation

Cech, Christian

doi:10.2139/ssrn.953888

Cited by 28 publications

(18 citation statements)

References 35 publications

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“…This copula can only capture a strong lower tail and positive dependence, but it can be rotated and used in capture negative dependence or reflect strong upper tail dependence (see [8,16]). The corresponding Kendall's tau and lower tail dependence coefficients are, respectively, τ CL = θ/(θ + 2) and λ L = 2 −1/θ .…”

Section: A3 Clayton Copulamentioning

confidence: 99%

“…It is essentially a linear regression model with two independent error components: a two-sided term that captures random variation of the production frontier across firms and a one-sided term that measures inefficiency relative to the frontier. In recent decades, most studies about production, cost or profit efficiency have used the conventional SFM (see, e.g., [1,9,10,15,19,[22][23][24][27][28][29][30][33][34][35][36]). In all these studies, it is assumed that the onesided and two-sided error terms are independent.…”

Section: Introductionmentioning

confidence: 99%

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Modeling dependence between error components of the stochastic frontier model using copula: Application to intercrop coffee production in Northern Thailand

Wiboonpongse

Liu

Sriboonchitta

et al. 2015

International Journal of Approximate Reasoning

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In the standard stochastic frontier model, the two-sided error term V and the one-sided technical inefficiency error term W are assumed to be independent. In this paper, we relax this assumption by modeling the dependence between V and W using copulas. Nine copula families are considered and their parameters are estimated using maximum simulated likelihood. The best model is then selected using the AIC or BIC criteria. This methodology was applied to coffee production data from Northern Thailand. For these data, the best model was the one based on the Clayton copula. The main finding of this study is that the dependence between V and W is significant and cannot be ignored. In particular, the standard stochastic frontier model with independence assumption grossly overestimated the technical efficiency of coffee production. These results call for a reappraisal of previous production efficiency studies using the SFM with independence assumption, which may occasionally lead to overoptimistic conclusions.

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Section: A3 Clayton Copulamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modeling dependence between error components of the stochastic frontier model using copula: Application to intercrop coffee production in Northern Thailand

Wiboonpongse

Liu

Sriboonchitta

et al. 2015

International Journal of Approximate Reasoning

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“…furthermore Archimean copulas allow easily to model skewed distributions with nonsymmetric tails. Thus, the panel of considered copulas was restricted to: independent, Clayton, Gumbel, Frank, Joe and rotated Joe (180 degrees) copulas (see Cech [7] for more about rotated copulas).…”

Section: Selection Of a Copula Modelmentioning

confidence: 99%

Modeling non-linear spectral domain dependence using copulas with applications to rat local field potentials

Fontaine

Frostig

Ombao

2020

Econometrics and Statistics

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This paper intends to develop tools for characterizing non-linear spectral dependence between spontaneous brain signals. We use parametric copula models (both bivariate and vine models) applied on the magnitude of Fourier coefficients rather than using coherence. The motivation behind this work is an experiment on rats that studied the impact of stroke on the connectivity structure (dependence) between local field potentials recorded at various channels. We address the following major questions.First, we ask whether one can detect any changepoint in the regime of a brain channel for a given frequency band based on a difference between the cumulative distribution functions modeled for each epoch (small window of time). Our proposed approach is an iterative algorithm which compares each successive bivariate copulas on all the epochs range, using a bivariate Kolmogorov-Smirnov statistic. Second, we ask whether stroke can alter the dependence structure of brain signals; and examine whether changes in dependence are present only in some channels or generalized across channels. These questions are addressed by comparing Vine-copulas models fitted for each epoch. We provide the necessary framework and show the effectiveness of our methods through the results for the local field potential data analysis of a rat.

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“…θ is the parameter from the original copula. Further information about the rotated copula can be found in Brechmann and Schepsmeier (2013), Cech (2006), Georges et al (2001) and Luo (2010).…”

Section: Ecb Working Paper Series No 2296 / July 2019mentioning

confidence: 99%

Disentangling the role of the exchange rate in oil-related scenarios for the European stock market

Ferreiro¹

2020

Energy Economics

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Copula-Based Top-Down Approaches in Financial Risk Aggregation

Cited by 28 publications

References 35 publications

Modeling dependence between error components of the stochastic frontier model using copula: Application to intercrop coffee production in Northern Thailand

Modeling dependence between error components of the stochastic frontier model using copula: Application to intercrop coffee production in Northern Thailand

Modeling non-linear spectral domain dependence using copulas with applications to rat local field potentials

Disentangling the role of the exchange rate in oil-related scenarios for the European stock market

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