Estimating non-simplified vine copulas using penalized splines

Schellhase, Christian; Spanhel, Fabian

doi:10.1007/s11222-017-9737-7

Cited by 23 publications

(21 citation statements)

References 46 publications

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“…[13], [36] studied bayesian additive models of conditional copulas. Recently, [39] invokes B-splines to manage vectors of conditioning variables. In a semiparametric framework, i.e.…”

Section: Remark 1 the Simplifying Assumption H Does Not Imply That Cmentioning

confidence: 99%

About tests of the “simplifying” assumption for conditional copulas

Derumigny

Fermanian

2017

Dependence Modeling

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Abstract:We discuss the so-called "simplifying assumption" of conditional copulas in a general framework. We introduce several tests of the latter assumption for non-and semiparametric copula models. Some related test procedures based on conditioning subsets instead of point-wise events are proposed. The limiting distributions of such test statistics under the null are approximated by several bootstrap schemes, most of them being new. We prove the validity of a particular semiparametric bootstrap scheme. Some simulations illustrate the relevance of our results.

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“…[13], [36] studied bayesian additive models of conditional copulas. Recently, [39] invokes B-splines to manage vectors of conditioning variables. In a semiparametric framework, i.e.…”

Section: Remark 1 the Simplifying Assumption H Does Not Imply That Cmentioning

confidence: 99%

About tests of the “simplifying” assumption for conditional copulas

Derumigny

Fermanian

2017

Dependence Modeling

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“…In this light, we also test the performance of the VaR model using R-Vine copula functions. For more details on R-Vine copula functions, see Schellhase and Spanhel (2018), Barthel et al (2018), Allen et al (2017) and the complete R-package by Schepsmeier et al (2018). Table 11 presents the unconditional and conditional pair copula type selection following R-Vine copula functions.…”

Section: Back-testingmentioning

confidence: 99%

Forecasting robust value-at-risk estimates: evidence from UK banks

Sampid

Hasim

2019

Quantitative Finance

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In this paper, we present a novel approach for forecasting Value-at-Risk (VaR) by combining a Bayesian GARCH(1,1) model with Student's-t distribution for the underlying volatility models, vine copula functions to model dependence, and peaks-over-threshold (POT) method of extreme value theory (EVT) to model the tail behaviour of asset returns. We further propose a new approach for threshold selection in extreme value analysis, which we call a hybrid method. The empirical results and back-testing analysis show that the model captures VaR quite well through periods of calmness and crisis; therefore, it is suitable for use as a measure of risk. Our results also suggest that with a correct implementation of the VaR model, Basel III is not needed.

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“…Additionally several nonparametric methods for the estimation of the pair‐copulas in a vine model have been developed, e.g. kernel density based estimation (Nagler & Czado ), estimation using splines (Kauermann & Schellhase ; Schellhase & Spanhel ) and the empirical copula (Hobæk Haff & Segers ). Furthermore there is a multitude of real data applications, especially in the context of finance (e.g.…”

Section: Vine Copulas and The Simplifying Assumptionmentioning

confidence: 99%

Examination and visualisation of the simplifying assumption for vine copulas in three dimensions

Killiches

Kraus

Czado

2017

Aus NZ J of Statistics

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Vine copulas are a highly flexible class of dependence models, which are based on the decomposition of the density into bivariate building blocks. For applications one usually makes the simplifying assumption that copulas of conditional distributions are independent of the variables on which they are conditioned. However this assumption has been criticised for being too restrictive. We examine both simplified and non-simplified vine copulas in three dimensions and investigate conceptual differences. We show and compare contour surfaces of three-dimensional vine copula models, which prove to be much more informative than the contour lines of the bivariate marginals. Our investigation shows that non-simplified vine copulas can exhibit arbitrarily irregular shapes, whereas simplified vine copulas appear to be smooth extrapolations of their bivariate margins to three dimensions. In addition to a variety of constructed examples, we also investigate a three-dimensional subset of the well-known uranium data set and visually detect that a non-simplified vine copula is necessary to capture its complex dependence structure.

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Estimating non-simplified vine copulas using penalized splines

Cited by 23 publications

References 46 publications

About tests of the “simplifying” assumption for conditional copulas

About tests of the “simplifying” assumption for conditional copulas

Forecasting robust value-at-risk estimates: evidence from UK banks

Examination and visualisation of the simplifying assumption for vine copulas in three dimensions

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