Using Dynamic Copulae for Modeling Dependency in Currency Denominations of a Diversified World Stock Index

Ignatieva, Katja; Platen, Eckhard; Rendek, Renata

doi:10.1080/15598608.2011.10412039

Cited by 7 publications

(2 citation statements)

References 23 publications

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“…We show in Fig. 2.2.10 the Student t copula for four degrees of freedom, which has been shown in Ignatieva et al (2011) to reflect extremely well the dependence of logreturns of well-diversified indices in different currencies. Compared to Fig.…”

Section: Copulasmentioning

confidence: 53%

Functionals of Multidimensional Diffusions with Applications to Finance

Baldeaux

Platen

2013

Bocconi &Amp; Springer Series

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Section: Copulasmentioning

confidence: 53%

Functionals of Multidimensional Diffusions with Applications to Finance

Baldeaux

Platen

2013

Bocconi &Amp; Springer Series

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“…Copulas have become popular tools for modelling dependence between random quantities in many application areas, including finance [4,20,29], actuarial science [15,28], risk management [13], hydrology [16], reliability analysis [34] and pattern recognition [32]. Copulas are attractive due to their ability to model dependence between random quantities separately from their marginal distributions [4,27].…”

Section: Introductionmentioning

confidence: 99%

Predictive inference for bivariate data: Combining nonparametric predictive inference for marginals with an estimated copula

Coolen‐Maturi

Coolen

Muhammad

2016

Journal of Statistical Theory and Practice

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Additional information:Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. AbstractThis paper presents a new method for prediction of an event involving a future bivariate observation. The method combines nonparametric predictive inference (NPI) applied to the marginals with a parametric copula to model and estimate the dependence structure between two random quantities, as such the method is semi-parametric. In NPI, uncertainty is quantified through imprecise probabilities. The resulting imprecision in the marginals provides robustness with regard to the assumed parametric copula. Due to the specific nature of NPI, the estimation of the copula parameter is also quite straightforward. The performance of this method is investigated via simulations, with particular attention to robustness with regard to the assumed copula in case of small data sets. The method is further illustrated via two examples, using small data sets from the literature. This paper presents several novel aspects of statistical inference. First, the link between NPI and copulas is powerful and attractive with regard to computation. Secondly, statistical methods using imprecise probability have gained substantial attention in recent years, where typically imprecision is used on aspects for which less information is available. This paper presents a different approach, namely imprecision mainly being introduced on the marginals, for which there is typically quite sufficient information, in order to provide robustness for the harder part of the inference, namely the * Corresponding author copula assumptions and estimation. Thirdly, the set up of the simulations to evaluate the performance of the proposed method is novel, key to these are frequentist comparisons of the success proportion of predictions with the corresponding data-based lower and upper predictive inferences. All these novel ideas can be applied far more generally to other inferences and models, while also many alternatives can be considered. Hence, this paper presents the starting point of an extensive research programme towards powerful predictive inference methods for multi-variate data.

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