Special Issue "Recent Developments in Copula Models"

A special issue of Econometrics (ISSN 2225-1146).

Deadline for manuscript submissions: closed (31 March 2017).

Special Issue Editor

Jean-David Fermanian
Website
Guest Editor
Crest-Ensae, France
Interests: dependence models; financial econometrics; credit risk

Special Issue Information

Dear Colleagues,

For twenty years, copula models have been more and more studied in the academic literature, and been used extensively among practitioners. Due to the high amount of flexibility they allow, they have been popularized in almost all domains of stochastic modeling and statistics, in particular economics, econometrics and finance. A lot of new families of models have emerged, as pair-copula constructions, and more classical frameworks (M-estimators, GARCH models, etc.) have been revisited with a copula-related point of view. The semi-parametric and/or multi-step nature of most copula model specifications has fueled the necessity of developing convenient asymptotic results and probabilistic tools, based on empirical processes for instance. Time-dependent dependence structures, typically in markovian time series, set difficult questions and recent progresses have been made towards this direction. This special issue will provide cutting-edge new results and models in this stream of research, with emphasis (but not exclusively) on their relevance in financial econometrics and economics/finance more generally. Theoretical papers are welcome, but empirical illustrations are highly encouraged.

Prof. Dr. Jean-David Fermanian
Guest Editor

Manuscript Submission Information

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Keywords

  • Copulae
  • pseudo-observations
  • dependence measures
  • vines
  • copula-GARCH

Published Papers (8 papers)

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Editorial

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Open AccessEditorial
Recent Developments in Copula Models
Econometrics 2017, 5(3), 34; https://doi.org/10.3390/econometrics5030034 - 24 Jul 2017
Cited by 2
Abstract
Copula models have become very popular and well studied among the scientific community.[...] Full article
(This article belongs to the Special Issue Recent Developments in Copula Models)

Research

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Open AccessArticle
The Realized Hierarchical Archimedean Copula in Risk Modelling
Econometrics 2017, 5(2), 26; https://doi.org/10.3390/econometrics5020026 - 15 Jun 2017
Cited by 3
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Recent Developments in Copula Models)
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Open AccessArticle
Dependence between Stock Returns of Italian Banks and the Sovereign Risk
Econometrics 2017, 5(2), 23; https://doi.org/10.3390/econometrics5020023 - 08 Jun 2017
Cited by 2
Abstract
We analyze the interdependence between the government yield spread and stock returns of the banking sector in Italy during the years 2003–2015. In a first step, we find that the Spearman’s rank correlation between the yield spread and the Italian banking system changed [...] Read more.
We analyze the interdependence between the government yield spread and stock returns of the banking sector in Italy during the years 2003–2015. In a first step, we find that the Spearman’s rank correlation between the yield spread and the Italian banking system changed significantly after September 2008. According to this finding, we split the time window in two sub-periods. While we show that the dependence between the banking industry and changes in the yield spread increased significantly in the second time interval, we find no contagion effects from changes in the yield spread to returns of the banking system. Full article
(This article belongs to the Special Issue Recent Developments in Copula Models)
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Open AccessArticle
Bayesian Inference for Latent Factor Copulas and Application to Financial Risk Forecasting
Econometrics 2017, 5(2), 21; https://doi.org/10.3390/econometrics5020021 - 23 May 2017
Cited by 6
Abstract
Factor modeling is a popular strategy to induce sparsity in multivariate models as they scale to higher dimensions. We develop Bayesian inference for a recently proposed latent factor copula model, which utilizes a pair copula construction to couple the variables with the latent [...] Read more.
Factor modeling is a popular strategy to induce sparsity in multivariate models as they scale to higher dimensions. We develop Bayesian inference for a recently proposed latent factor copula model, which utilizes a pair copula construction to couple the variables with the latent factor. We use adaptive rejection Metropolis sampling (ARMS) within Gibbs sampling for posterior simulation: Gibbs sampling enables application to Bayesian problems, while ARMS is an adaptive strategy that replaces traditional Metropolis-Hastings updates, which typically require careful tuning. Our simulation study shows favorable performance of our proposed approach both in terms of sampling efficiency and accuracy. We provide an extensive application example using historical data on European financial stocks that forecasts portfolio Value at Risk (VaR) and Expected Shortfall (ES). Full article
(This article belongs to the Special Issue Recent Developments in Copula Models)
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Open AccessArticle
Copula-Based Factor Models for Multivariate Asset Returns
Econometrics 2017, 5(2), 20; https://doi.org/10.3390/econometrics5020020 - 17 May 2017
Cited by 1
Abstract
Recently, several copula-based approaches have been proposed for modeling stationary multivariate time series. All of them are based on vine copulas, and they differ in the choice of the regular vine structure. In this article, we consider a copula autoregressive (COPAR) approach to [...] Read more.
Recently, several copula-based approaches have been proposed for modeling stationary multivariate time series. All of them are based on vine copulas, and they differ in the choice of the regular vine structure. In this article, we consider a copula autoregressive (COPAR) approach to model the dependence of unobserved multivariate factors resulting from two dynamic factor models. However, the proposed methodology is general and applicable to several factor models as well as to other copula models for stationary multivariate time series. An empirical study illustrates the forecasting superiority of our approach for constructing an optimal portfolio of U.S. industrial stocks in the mean-variance framework. Full article
(This article belongs to the Special Issue Recent Developments in Copula Models)
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Open AccessArticle
Goodness-of-Fit Tests for Copulas of Multivariate Time Series
Econometrics 2017, 5(1), 13; https://doi.org/10.3390/econometrics5010013 - 17 Mar 2017
Cited by 15
Abstract
In this paper, we study the asymptotic behavior of the sequential empirical process and the sequential empirical copula process, both constructed from residuals of multivariate stochastic volatility models. Applications for the detection of structural changes and specification tests of the distribution of innovations [...] Read more.
In this paper, we study the asymptotic behavior of the sequential empirical process and the sequential empirical copula process, both constructed from residuals of multivariate stochastic volatility models. Applications for the detection of structural changes and specification tests of the distribution of innovations are discussed. It is also shown that if the stochastic volatility matrices are diagonal, which is the case if the univariate time series are estimated separately instead of being jointly estimated, then the empirical copula process behaves as if the innovations were observed; a remarkable property. As a by-product, one also obtains the asymptotic behavior of rank-based measures of dependence applied to residuals of these time series models.
Full article
(This article belongs to the Special Issue Recent Developments in Copula Models)
Open AccessArticle
Regime Switching Vine Copula Models for Global Equity and Volatility Indices
Econometrics 2017, 5(1), 3; https://doi.org/10.3390/econometrics5010003 - 04 Jan 2017
Cited by 21
Abstract
For nearly every major stock market there exist equity and implied volatility indices. These play important roles within finance: be it as a benchmark, a measure of general uncertainty or a way of investing or hedging. It is well known in the academic [...] Read more.
For nearly every major stock market there exist equity and implied volatility indices. These play important roles within finance: be it as a benchmark, a measure of general uncertainty or a way of investing or hedging. It is well known in the academic literature that correlations and higher moments between different indices tend to vary in time. However, to the best of our knowledge, no one has yet considered a global setup including both equity and implied volatility indices of various continents, and allowing for a changing dependence structure. We aim to close this gap by applying Markov-switching R-vine models to investigate the existence of different, global dependence regimes. In particular, we identify times of “normal” and “abnormal” states within a data set consisting of North-American, European and Asian indices. Our results confirm the existence of joint points in a time at which global regime switching between two different R-vine structures takes place. Full article
(This article belongs to the Special Issue Recent Developments in Copula Models)
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Review

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Open AccessReview
Pair-Copula Constructions for Financial Applications: A Review
Econometrics 2016, 4(4), 43; https://doi.org/10.3390/econometrics4040043 - 29 Oct 2016
Cited by 18
Abstract
This survey reviews the large and growing literature on the use of pair-copula constructions (PCCs) in financial applications. Using a PCC, multivariate data that exhibit complex patterns of dependence can be modeled using bivariate copulae as simple building blocks. Hence, this model represents [...] Read more.
This survey reviews the large and growing literature on the use of pair-copula constructions (PCCs) in financial applications. Using a PCC, multivariate data that exhibit complex patterns of dependence can be modeled using bivariate copulae as simple building blocks. Hence, this model represents a very flexible way of constructing higher-dimensional copulae. In this paper, we survey inference methods and goodness-of-fit tests for such models, as well as empirical applications of the PCCs in finance and economics. Full article
(This article belongs to the Special Issue Recent Developments in Copula Models)
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