Extreme value attractors for star unimodal copulas

Cuculescu, Ioan; Theodorescu, Radu

doi:10.1016/s1631-073x(02)02322-1

Cited by 9 publications

(4 citation statements)

References 6 publications

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“…In contrast, as a tool to measure the correlation structure between multiple variables, the Copular function has been widely used in financial markets. It can capture the nonlinear and asymmetric correlation between variables, especially for distribution tails and correlations [46][47][48]. We thus use the Copula function to model the interdependent structure of China's carbon emission markets.…”

Section: Copula Functionmentioning

confidence: 99%

Modeling the Risk of Extreme Value Dependence in Chinese Regional Carbon Emission Markets

Qiu

Yang

et al. 2020

Sustainability

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In this study, we analyze the risk of extreme value dependence in Chinese regional carbon emission markets. After filtering the daily return data of six carbon markets in China using a generalized autoregressive conditional heteroscedasticity (GARCH) model, we obtain the standardized residual series. Next, the dependence structures in the markets are captured by the Copula function and the Extreme Value theory (EVT). We report high peaks, heavy tails and fluctuation aggregation in the logarithm return series of the markets, as well as significant dependent structures. There are significant extreme value risks in Chinese regional carbon markets, but the risks can be mitigated through appropriate portfolio diversification.

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Section: Copula Functionmentioning

confidence: 99%

Modeling the Risk of Extreme Value Dependence in Chinese Regional Carbon Emission Markets

Qiu

Yang

et al. 2020

Sustainability

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“…In the following proposition we introduce an important subclass of 2-copulas, namely the Archimax copulas [16]. C t;D ðx; yÞ ¼ t À1 Min tð0Þ; ðtðxÞ þ tðyÞÞD tðxÞ tðxÞ þ tðyÞ is a 2-copula.…”

Section: Remarkmentioning

confidence: 99%

Aggregation functions: Construction methods, conjunctive, disjunctive and mixed classes

Grabisch

Marichal

Mesiar

et al. 2011

Information Sciences

142

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“…Remark 3.4. An interesting probabilistic interpretation of formula (3.1) was presented in [13]: if h(t) = t 1/n for some n ≥ 1, then C h is the copula associated with componentwise maxima, X = max(X 1 ,...,X n ) and Y = max(Y 1 ,...,Y n ) of a random sample (X 1 ,Y 1 ),...,(X n ,Y n ) from some arbitrary distribution with underlying copula C. Power transformation of copulas was introduced in the theory of extreme value distributions [5,6,18]; recently Klement et al [16] have studied the copulas that are invariant under power transformations and under increasing bijections.…”

Section: The Transform Of Copulasmentioning

confidence: 99%