Results 11 to 20 of about 2,188,856 (302)
Likelihood Inference for Factor Copula Models with Asymmetric Tail Dependence [PDF]
EntropyFor multivariate non-Gaussian involving copulas, likelihood inference is dominated by the data in the middle, and fitted models might not be very good for joint tail inference, such as assessing the strength of tail dependence.
Harry Joe, Xiaoting Li
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Tail dependence of perturbed copulas [PDF]
Journal of Statistical Theory and Applications (JSTA), 2016 In this paper, we extend our investigations of a special class of perturbations of copulas introduced in [7]. Despite a surprising fact that this kind of perturbations does not change the value of tail dependence of the original copulas, their use ...
Jozef Komorník +3 more
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Copulas, stable tail dependence functions, and multivariate monotonicity
Dependence Modeling, 2019 For functions of several variables there exist many notions of monotonicity, three of them being characteristic for resp. distribution, survival and co-survival functions. In each case the “degree” of monotonicity is just the basic one of a whole scale.
Ressel Paul
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Tail dependence structure and extreme risk spillover effects between the international agricultural futures and spot markets [PDF]
Journal of international financial markets, institutions, and money, 2023 This paper combines the Copula-CoVaR approach with the ARMA-GARCH-skewed Student-t model to investigate the tail dependence structure and extreme risk spillover effects between the international agricultural futures and spot markets, taking four main ...
Yun Dai, Peng-Fei Dai, Wei Zhou
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Econometric Theory, 2021 This paper shows that if the errors in a multiple regression model are heavy-tailed, the ordinary least squares (OLS) estimators for the regression coefficients are tail-dependent. The tail dependence arises, because the OLS estimators are stochastic linear combinations of heavy-tailed random variables. Moreover, tail dependence also exists between the
Oorschot, Jochem, Zhou, Chen
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COMET Flows: Towards Generative Modeling of Multivariate Extremes and Tail Dependence [PDF]
International Joint Conference on Artificial Intelligence, 2022 Normalizing flows—a popular class of deep generative models—often fail to represent extreme phenomena observed in real-world processes. In particular, existing normalizing flow architectures struggle to model multivariate extremes, characterized by heavy-
Andrew McDonald +2 more
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Tail-dependence, exceedance sets, and metric embeddings [PDF]
Extremes, 2022 There are many ways of measuring and modeling tail-dependence in random vectors: from the general framework of multivariate regular variation and the flexible class of max-stable vectors down to simple and concise summary measures like the matrix of ...
A. Janssen +2 more
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Links between US and Turkish agricultural commodity Markets: Nonlinear dependence and tail risk
Borsa Istanbul Review, 2023 In these unprecedented times, marred by the effects of the Covid-19 pandemic, global warming, and the war in Ukraine that began in February 2022, new approaches such as tail dependence have attracted more interest than conventional market dependence ...
Zehra Atik +2 more
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Modeling spatial tail dependence with Cauchy convolution processes [PDF]
Electronic Journal of Statistics, 2021 We study the class of dependence models for spatial data obtained from Cauchy convolution processes based on different types of kernel functions. We show that the resulting spatial processes have appealing tail dependence properties, such as tail ...
Pavel Krupskii, Raphael Huser
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Tail dependence between bitcoin and green financial assets
Economics Letters, 2021 The high power consumption of Bitcoin transactions has raised environmental and sustainable concerns of green investors and regulatory bodies. We utilize the time-varying optimal copula (TVOC) approach to showcase the dependence structure between bitcoin
M. Naeem, Sitara Karim
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