Extreme and Inference for Tail Gini Functionals With Applications in Tail Risk Measurement

Hou, Yanxi; Wang, Xing

doi:10.1080/01621459.2020.1730855

Cited by 7 publications

(15 citation statements)

References 30 publications

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“…if the limit exists. Hou and Wang [13] have established the following asymptotic behavior, for 𝑄 1 , the quantile function of 𝑋,…”

Section: Tail-gini Functionalmentioning

confidence: 99%

“…where 𝐹 1 is the distribution of a continuous random variable 𝑋 and 𝑝 is a small probability. When it comes to the case of bivariate random vector (𝑋, 𝑌), where 𝑋 is the objective variable and 𝑌 is a benchmark variable, the tail-Gini functional in a bivariate setup was introduced by Hou and Wang [13], that is…”

Section: Introductionmentioning

confidence: 99%

“…( 2) is to define a good tail variability measure incorporating both the marginal risk severity of 𝑋 and the tail structure of (𝑋, 𝑌) to reflect the tail variability of 𝑋 under the tail scenarios of the benchmark variable 𝑌 . Under the assumption that 𝑋 is in the Fréchet domain of attraction, Hou and Wang [13] studied the asymptotic behavior of the tail-Gini functional when the two random variables are asymptotic dependent. This result can help us better understand this variability measure.…”

Section: Introductionmentioning

confidence: 99%

“…The tail Gini-type variability measures play an important role in tail risk and variability theory which has been presented in the recent literature, see Furman et al [11], Berkhouch et al [2] and Hou and Wang [13]. The joint tail-Gini functional as a new variability measure also belongs to the family of tail Gini-type variability measures.…”

Section: Introductionmentioning

confidence: 99%

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Extreme Behaviors of the Tail Gini-Type Variability Measures

Sun¹,

Chen²

2022

Prob. Eng. Inf. Sci.

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For a bivariate random vector $(X, Y)$ , suppose $X$ is some interesting loss variable and $Y$ is a benchmark variable. This paper proposes a new variability measure called the joint tail-Gini functional, which considers not only the tail event of benchmark variable $Y$ , but also the tail information of $X$ itself. It can be viewed as a class of tail Gini-type variability measures, which also include the recently proposed tail-Gini functional. It is a challenging and interesting task to measure the tail variability of $X$ under some extreme scenarios of the variables by extending the Gini's methodology, and the two tail variability measures can serve such a purpose. We study the asymptotic behaviors of these tail Gini-type variability measures, including tail-Gini and joint tail-Gini functionals. The paper conducts this study under both tail dependent and tail independent cases, which are modeled by copulas with so-called tail order property. Some examples are also shown to illuminate our results. In particular, a generalization of the joint tail-Gini functional is considered to provide a more flexible version.

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“…if the limit exists. Hou and Wang [13] have established the following asymptotic behavior, for 𝑄 1 , the quantile function of 𝑋,…”

Section: Tail-gini Functionalmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Extreme Behaviors of the Tail Gini-Type Variability Measures

Sun¹,

Chen²

2022

Prob. Eng. Inf. Sci.

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“…Presently, there are several studies related to systemic financial risk, such as the extension of Gini's methodology (Hou & Wang, 2020) and the examination of switching regime copula (Mensi et al, 2020). However, there is no consensus on the definition of systemic financial risk.…”

Section: Literature Reviewmentioning

confidence: 99%

Systemic risk in Chinese financial industries: a vine copula grouped CoVaR approach

Hao

Chen

2021

Economic Research-Ekonomska Istraživanja

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This paper investigates systemic risk in Chinese financial industries by constructing a vine copula grouped CoVaR model, which accounts for the fact that various sub-industries are comprised of multiple financial institutions. The backtesting results indicate that the vine copula grouped model performs better in measuring the systemic risk in comparison to the vine copula model, which in turn validates the accuracy and effectiveness of the former. Moreover, the results indicate that banking is a major systemic risk contributor, even though it has a strong ability to resist risk. Additionally, the potential loss faced by the securities industry is big, but its systemic risk contribution is small. These results are of significance to investment decision and risk management.

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