COPAR—multivariate time series modeling using the copula autoregressive model

Brechmann, Eike Christian; Czado, Claudia

doi:10.1002/asmb.2043

Cited by 73 publications

(73 citation statements)

References 34 publications

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“…Moreover, Brechmann and Czado (2015), Beare and Seo (2015), and Smith (2015) have applied vine copulas to model temporal dependence of multivariate time series as well as the cross-sectional dependence between univariate time series. In this section, we review the COPAR model of Brechmann and Czado (2015), which is used to describe the stochastic dynamics and the dependence structure of the estimated factors from Section 2. We start with the concept of regular vines from Kurowicka and Cooke (2006).…”

Section: Vine Copulasmentioning

confidence: 99%

“…Recently, Brechmann and Czado (2015), Beare and Seo (2015), and Smith (2015) simultaneously developed copula-based models for stationary multivariate time series. Although these models differ from each other, their generality consists of an underlying R-vine pair-copula construction (see Aas et al 2009) to describe the cross-sectional and temporal dependence jointly.…”

Section: Introductionmentioning

confidence: 99%

“…Although these models differ from each other, their generality consists of an underlying R-vine pair-copula construction (see Aas et al 2009) to describe the cross-sectional and temporal dependence jointly. To capture the cross-sectional dependence, Brechmann and Czado (2015) employ C-Vine, while Beare and Seo (2015) and Smith (2015) consider D-vine. Further, Brechmann and Czado (2015) and Beare and Seo (2015) assume the existence of a key variable, whose temporal dependence was explicitly modeled, and this assumption combined with C-or D-vine for the cross-sectional dependence results in a corresponding R-vine for a multivariate time series.…”

Section: Introductionmentioning

confidence: 99%

“…To capture the cross-sectional dependence, Brechmann and Czado (2015) employ C-Vine, while Beare and Seo (2015) and Smith (2015) consider D-vine. Further, Brechmann and Czado (2015) and Beare and Seo (2015) assume the existence of a key variable, whose temporal dependence was explicitly modeled, and this assumption combined with C-or D-vine for the cross-sectional dependence results in a corresponding R-vine for a multivariate time series. In contrast, Smith (2015) explicitly modeled the temporal dependence between multivariate observations and constructed a general D-vine for them consisting of D-vines for the cross-sectional dependence.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we apply the copula autoregressive (COPAR) model of Brechmann and Czado (2015) to quantify the dependence structure of estimated unobserved factors in dynamic factor models. More precisely, we consider two dynamic factor models and estimate them separately with the maximum likelihood by employing the Kalman filter and smoother.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Copula-Based Factor Models for Multivariate Asset Returns

2017

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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 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.

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Section: Vine Copulasmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Copula-Based Factor Models for Multivariate Asset Returns

2017

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Robust pair‐copula based forecasts of realized volatility

Mendes

Accioly²

2013

Appl Stoch Models Bus & Ind

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A useful application for copula functions is modeling the dynamics in the conditional moments of a time series. Using copulas, one can go beyond the traditional linear ARMA (p,q) modeling, which is solely based on the behavior of the autocorrelation function, and capture the entire dependence structure linking consecutive observations. This type of serial dependence is best represented by a canonical vine decomposition, and we illustrate this idea in the context of emerging stock markets, modeling linear and nonlinear temporal dependences of Brazilian series of realized volatilities. However, the analysis of intraday data collected from e‐markets poses some specific challenges. The large amount of real‐time information calls for heavy data manipulation, which may result in gross errors. Atypical points in high‐frequency intraday transaction prices may contaminate the series of daily realized volatilities, thus affecting classical statistical inference and leading to poor predictions. Therefore, in this paper, we propose to robustly estimate pair‐copula models using the weighted minimum distance and the weighted maximum likelihood estimates (WMLE). The excellent performance of these robust estimates for pair‐copula models are assessed through a comprehensive set of simulations, from which the WMLE emerged as the best option for members of the elliptical copula family. We evaluate and compare alternative volatility forecasts and show that the robustly estimated canonical vine‐based forecasts outperform the competitors. Copyright © 2013 John Wiley & Sons, Ltd.

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Modeling high‐dimensional time‐varying dependence using dynamic D‐vine models

Almeida

Czado²,

Manner

2016

Appl Stoch Models Bus & Ind

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We consider the problem of modeling the dependence among many time series. We build high-dimensional time-varying copula models by combining pair-copula constructions with stochastic autoregressive copula and generalized autoregressive score models to capture dependence that changes over time. We show how the estimation of this highly complex model can be broken down into the estimation of a sequence of bivariate models, which can be achieved by using the method of maximum likelihood. Further, by restricting the conditional dependence parameter on higher cascades of the pair copula construction to be constant, we can greatly reduce the number of parameters to be estimated without losing much flexibility. Applications to five MSCI stock market indices and to a large dataset of daily stock returns of all constituents of the Dax 30 illustrate the usefulness of the proposed model class in-sample and for density forecasting.

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COPAR—multivariate time series modeling using the copula autoregressive model

Cited by 73 publications

References 34 publications

Copula-Based Factor Models for Multivariate Asset Returns

Copula-Based Factor Models for Multivariate Asset Returns

Robust pair‐copula based forecasts of realized volatility

Modeling high‐dimensional time‐varying dependence using dynamic D‐vine models

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