Asymptotics for risk capital allocations based on Conditional Tail Expectation

Asimit, Alexandru V.; Furman, Edward; Tang, Qihe; Vernic, Raluca

doi:10.1016/j.insmatheco.2011.05.002

Cited by 86 publications

(75 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…1 and the boundedness of g ij . Assuming that F 1 2 R for some > 1, relation (3.13) of Asimit et al (2011) gives an asymptotic approximation for E X k jS d > t as t ! 1, which in our tail equivalence case is reduced to…”

Section: Applications To Systemic Riskmentioning

confidence: 99%

Systemic Risk: An Asymptotic Evaluation

Asimit

2017

SSRN Journal

View full text Add to dashboard Cite

The systemic risk (SR) has been shown to play an important role in explaining the …nancial turmoils in the last several decades and understanding this source of risk has been a particular interest amongst academics, practitioners, and regulators. The precise mathematical formulation of the SR is still scrutinised, but the main purpose is to evaluate the …nancial distress of a system as a result of the failure of one component of the …nancial system in question. Many of the mathematical de…nitions of the SR are based on evaluating expectations in extreme regions and therefore, Extreme Value Theory (EVT) represents the key ingredient in producing valuable estimates of the SR and even its decomposition per individual components of the entire system. Without doubt, the prescribed dependence model amongst the system components has a major impact over our asymptotic approximations. Thus, this paper considers various well-known dependence models in the EVT literature that allow us to generate SR estimates. Interestingly, our …ndings reveal sensible results. That is, the SR has a signi…cant impact under asymptotic dependence, while weak tail dependence, known as asymptotic independence, produces an insigni…cant loss over the regulatory capital.

show abstract

Section: Applications To Systemic Riskmentioning

confidence: 99%

Systemic Risk: An Asymptotic Evaluation

Asimit

2017

SSRN Journal

View full text Add to dashboard Cite

show abstract

“…Some of the recent works include Landsman and Valdez [13], Furman and Landsman [15], Chiragiev and Landsman [20], Vernic [21], Dhaene et al [62], Asimit et al [65], and references therein. Due to the importance of the TVaR premium and allocation, in this section we specialize our earlier formulas by setting v(x) = x and w(x) = 1 [a,b] (x) for any pair 0 ≤ a < b ≤ ∞.…”

Section: Preliminariesmentioning

confidence: 99%

Evaluating Risk Measures and Capital Allocations Based on Multi-Losses Driven by a Heavy-Tailed Background Risk: The Multivariate Pareto-Ii Model

2013

Self Cite

View full text Add to dashboard Cite

This is the published version of the paper.This version of the publication may differ from the final published version. Abstract: Evaluating risk measures, premiums, and capital allocation based on dependent multi-losses is a notoriously difficult task. In this paper, we demonstrate how this can be successfully accomplished when losses follow the multivariate Pareto distribution of the second kind, which is an attractive model for multi-losses whose dependence and tail heaviness are influenced by a heavy-tailed background risk. A particular attention is given to the distortion and weighted risk measures and allocations, as well as their special cases such as the conditional layer expectation, tail value at risk, and the truncated tail value at risk. We derive formulas that are either of closed form or follow well-defined recursive procedures. In either case, their computational use is straightforward. Permanent repository link

show abstract

“…Previous studies have focused on particular probability distributions of losses (Cossette et al, 2012(Cossette et al, , 2013, on alternative dependence structures between risks (Cai and Wei, 2014), on asymptotic allocations based on commonly used risk measures (Asimit et al, 2011) or on optimization function alterations in order to overcome limitations of the loss function minimization allocation criterion (Xu and Hu, 2012;Xu and Mao, 2013). An exhaustive list is not provided here but it is the object of ongoing research.…”

Section: Introductionmentioning

confidence: 99%

Compositional methods applied to capital allocation problems

Belles-Sampera¹,

Guillén²,

Santolino³

2016

JOR

View full text Add to dashboard Cite

In this article we examine the relationship between capital allocation problems and compositional data, i.e., information that refers to the parts of a whole conveying relative information. We show that capital allocation principles can be interpreted as compositions. The natural geometry and vector space structure of compositional data are used to operate with capital allocation solutions. The distance and average that are appropriated in the geometric structure of compositions are presented. We demonstrate that these two concepts can be used to compare capital allocation principles and to merge them. An illustration is provided to show how the distance between capital allocation solutions and the average of these solutions can be computed, and interpreted, by risk managers in practice.JEL classification: C02, G22, D81.

show abstract

Asymptotics for risk capital allocations based on Conditional Tail Expectation

Cited by 86 publications

References 47 publications

Systemic Risk: An Asymptotic Evaluation

Systemic Risk: An Asymptotic Evaluation

Evaluating Risk Measures and Capital Allocations Based on Multi-Losses Driven by a Heavy-Tailed Background Risk: The Multivariate Pareto-Ii Model

Compositional methods applied to capital allocation problems

Contact Info

Product

Resources

About