Single-index copulas

Fermanian, Jean‐David; Lopez, Olivier

doi:10.1016/j.jmva.2017.11.004

Cited by 20 publications

(22 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The following estimators of the parameter 𝛼 are compared: The latter estimator α(pl * ) is inspired by the estimator introduced in the context of single index conditional copulas by Fermanian and Lopez (2018). In our situation this estimator coincides with the MPL estimator computed only from Ûi which lie in [𝛿 n , 1 − 𝛿 n ] 2 , where 𝛿 n = Dn −1∕𝜆 .…”

Section: Settingsmentioning

confidence: 86%

Maximum pseudo‐likelihood estimation based on estimated residuals in copula semiparametric models

Omelka

Hudecová

Neumeyer

2020

Scandinavian J Statistics

View full text Add to dashboard Cite

This paper deals with an estimation of the dependence structure of a multidimensional response variable in the presence of a multivariate covariate. It is assumed that the covariate affects only the marginal distributions through regression models while the dependence structure, which is described by a copula, is unaffected.A parametric estimation of the copula function is considered with focus on the maximum pseudo-likelihood method. It is proved that under some appropriate regularity assumptions the estimator calculated from the residuals has the same asymptotic distribution as the estimator based on the unobserved errors. In such case one can ignore the fact that the response is first adjusted for the effect of the covariate. The theoretical results are accompanied by a Monte Carlo simulation study which illustrates that the maximum pseudo-likelihood estimator based on residuals may behave poorly when the stated regularity assumptions are not satisfied.

show abstract

Section: Settingsmentioning

confidence: 86%

Maximum pseudo‐likelihood estimation based on estimated residuals in copula semiparametric models

Omelka

Hudecová

Neumeyer

2020

Scandinavian J Statistics

View full text Add to dashboard Cite

show abstract

“…This paper proposes a semiparametric conditional mixture copula model in which both weight and copula parameters can vary with a covariate in a nonparametric way. The conditional mixture copula exploits the advantages of both the conditional copula which can capture a covariates impact on the degree of dependence (see , and Fermanian & Lopez, 2018, and the mixture copula which can combine copula families with different dependence patterns (see Hu, 2006, andCai &Wang, 2014). Therefore, it provides extra flexibility and an unified way for practitioners to measure the dependence pattern and the degree of dependence.…”

Section: Discussionmentioning

confidence: 99%

Semiparametric estimation and model selection for conditional mixture copula models

Liu

Long

Yang³

et al. 2021

Scandinavian J Statistics

View full text Add to dashboard Cite

Conditional copula models allow the dependence structure among variables to vary with covariates, and thus can describe the evolution of the dependence structure with those factors. This paper proposes a conditional mixture copula which is a weighted average of several individual conditional copulas. We allow both the weights and copula parameters to vary with a covariate so that the conditional mixture copula offers additional flexibility and accuracy in describing the dependence structure. We propose a two-step semiparametric estimation method and develop asymptotic properties of the estimators. Moreover, we introduce model selection procedures to select the component copulas of the conditional mixture copula model. Simulation results suggest that the proposed procedures have a good performance in estimating and selecting conditional mixture copulas with different model specifications. The proposed model is then applied to investigate how the dependence structures among international equity markets evolve with the volatility in the exchange rate markets.

show abstract

“…Hafner and Reznikova (2010) propose a semiparametric dynamic copula (SDC) model in which the copula parameter changes over time in a nonparametric way. Other dynamic copulas include dynamic stochastic copula models (Hafner and Manner, 2012), stochastic copula autoregressive models (Almeida and Czado, 2012), generalized autoregressive score models (Creal, Koopman and Lucas, 2013), variational mode decomposition methods (Mensi et al, 2016), single-index copula models (Fermanian and Lopez, 2018), and semiparametric copula models under non-stationarity (Nasri, Rémillard and Bouezmarni, 2019), among others. For a comprehensive survey of dynamic copulas and their applications in financial time series analysis, readers are referred to the survey paper by Patton (2012a).…”

Section: Introductionmentioning

confidence: 99%

Time-Varying Mixture Copula Models with Copula Selection

Yang¹,

Cai²,

Hafner³

et al. 2021

STAT SINICA

View full text Add to dashboard Cite

Modeling the joint tails of multiple financial time series has many important implications for risk management. Classical models for dependence often encounter a lack of fit in the joint tails, calling for additional flexibility. This paper introduces a new semiparametric time-varying mixture copula model, in which both weights and dependence parameters are deterministic and unspecified functions of time. We propose penalized time-varying mixture copula models with group smoothly clipped absolute deviation penalty functions to do the estimation and copula selection simultaneously. Monte Carlo simulation results suggest that the shrinkage estimation procedure performs well in selecting and estimating both constant and time-varying mixture copula models. Using the proposed model and method, we analyze the evolution of the dependence among four international stock markets, and find substantial changes in the levels and patterns of the dependence, in particular around crisis periods.

show abstract

Single-index copulas

Cited by 20 publications

References 42 publications

Maximum pseudo‐likelihood estimation based on estimated residuals in copula semiparametric models

Maximum pseudo‐likelihood estimation based on estimated residuals in copula semiparametric models

Semiparametric estimation and model selection for conditional mixture copula models

Time-Varying Mixture Copula Models with Copula Selection

Contact Info

Product

Resources

About