High dimensional dynamic stochastic copula models

Creal, Drew; Tsay, Ruey S.

doi:10.1016/j.jeconom.2015.03.027

Cited by 98 publications

(73 citation statements)

References 40 publications

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“…Creal and Tsay (2014) propose a stochastic copula model based on a factor structure, and use Bayesian estimation methods to apply it to an unbalanced panel of CDS spreads and equity returns on 100 firms. Archimedean copulas such as the Clayton or Gumbel allow for tail dependence and particular forms of asymmetry, but usually have only a one or two parameter(s) to characterize the dependence between all variables, and are thus very restrictive in higher-dimension applications.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

Modeling Dependence in High Dimensions With Factor Copulas

Patton

2017

Journal of Business & Economic Statistics

170

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This paper presents flexible new models for the dependence structure, or copula, of economic variables based on a latent factor structure. The proposed models are particularly attractive for relatively high dimensional applications, involving fifty or more variables, and can be combined with semiparametric marginal distributions to obtain flexible multivariate distributions. Factor copulas generally lack a closed-form density, but we obtain analytical results for the implied tail dependence using extreme value theory, and we verify that simulation-based estimation using rank statistics is reliable even in high dimensions. We consider "scree" plots to aid the choice of the number of factors in the model. The model is applied to daily returns on all 100 constituents of the S&P 100 index, and we find significant evidence of tail dependence, heterogeneous dependence, and asymmetric dependence, with dependence being stronger in crashes than in booms. We also show that factor copula models provide superior estimates of some measures of systemic risk.

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Section: Accepted Manuscriptmentioning

confidence: 99%

Modeling Dependence in High Dimensions With Factor Copulas

Patton

2017

Journal of Business & Economic Statistics

170

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“…This implies that there are gains to be had by modelling linear dependence, as captured by covariances, using high frequency data. Second, copula methods have been shown to be useful for constructing flexible distribution models in high dimensions, see Christoffersen et al (2013), Oh and Patton (2016) and Creal and Tsay (2014). These two findings naturally lead to the question of whether high frequency data and copula methods can be combined to improve the modelling and forecasting of highdimensional return distributions.…”

mentioning

confidence: 99%

High-dimensional copula-based distributions with mixed frequency data

Patton

2016

Journal of Econometrics

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a r t i c l e i n f oArticle history: Available online xxxx JEL classification: C32 C51 C58 Keywords: High frequency data Forecasting Composite likelihood Nonlinear dependence a b s t r a c tThis paper proposes a new model for high-dimensional distributions of asset returns that utilizes mixed frequency data and copulas. The dependence between returns is decomposed into linear and nonlinear components, enabling the use of high frequency data to accurately forecast linear dependence, and a new class of copulas designed to capture nonlinear dependence among the resulting uncorrelated, low frequency, residuals. Estimation of the new class of copulas is conducted using composite likelihood, facilitating applications involving hundreds of variables. In-and out-of-sample tests confirm the superiority of the proposed models applied to daily returns on constituents of the S&P 100 index.

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“…Recently, Creal and Tsay (2015), Patton (2017, 2018), and Lucas et al (2017) put forward a general approach to modeling time-varying dependence in high cross-sectional dimensions using a factor copula structure. The factor copula structure describes the dependence between a large number of observed variables by a smaller set of latent variables (or factors) with time-varying loadings.…”

Section: Introductionmentioning

confidence: 99%

“…This requires considerable computational effort, particularly if multiple factors are used. Creal and Tsay (2015) face a different challenge as they use a standard parameter driven recurrence equation for the factor loading dynamics.…”

Section: Introductionmentioning

confidence: 99%

Closed-Form Multi-Factor Copula Models with Observation-Driven Dynamic Factor Loadings

et al. 2019

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We develop new multi-factor dynamic copula models with time-varying factor loadings and observation-driven dynamics. The new models are highly flexible, scalable to high dimensions, and ensure positivity of covariance and correlation matrices. A closed-form likelihood expression allows for straightforward parameter estimation and likelihood inference. We apply the new model to a large panel of 100 U.S. stocks over the period 2001-2014. The proposed multi-factor structure is much better than existing (singlefactor) models at describing stock return dependence dynamics in high-dimensions. The new factor models also improve one-step-ahead copula density forecasts and global minimum variance portfolio performance. Finally, we investigate different mechanisms to allocate firms into groups and find that a simple industry classification outperforms alternatives based on observable risk factors, such as size, value or momentum.

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High dimensional dynamic stochastic copula models

Cited by 98 publications

References 40 publications

Modeling Dependence in High Dimensions With Factor Copulas

Modeling Dependence in High Dimensions With Factor Copulas

High-dimensional copula-based distributions with mixed frequency data

Closed-Form Multi-Factor Copula Models with Observation-Driven Dynamic Factor Loadings

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