Comparison of conditional distributions in portfolios of dependent risks

Sordo, Miguel Á.; Suárez‐Llorens, Alfonso; Bello, Alfonso J.

doi:10.1016/j.insmatheco.2014.11.008

Cited by 24 publications

(11 citation statements)

References 28 publications

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“…The excess wealth order was independently introduced by Fernandez- Ponce et al (1998) and Shaked and Shanthikumar (1998) and has received great attention in the literature. Properties and applications in several contexts such as reliability, risks, and auction theory can be found in Belzunce (1999), Denuit and Vermandele (1999), Kochar et al (2002), Li (2005), Sordo (2008Sordo ( ), (2009, Hu et al (2012), Kochar and Xu (2013), Balakrishnan and Zao (2013), Singpurwalla and Gordon (2014), and Sordo et al (2015).…”

Section: Introductionmentioning

confidence: 99%

On sufficient conditions for the comparison in the excess wealth order and spacings

et al. 2016

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The purpose of this paper is twofold. On the one hand, we provide sufficient conditions for the excess wealth order. These conditions are based on properties of the quantile functions which are useful when the dispersive order does not hold. On the other hand, we study sufficient conditions for the comparison in the increasing convex order of spacings of generalized order statistics. These results will be combined to show how we can provide comparisons of quantities of interest in reliability and insurance.

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Section: Introductionmentioning

confidence: 99%

On sufficient conditions for the comparison in the excess wealth order and spacings

et al. 2016

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“…The CoVaR approach measures the value-at-risk of an institution conditional on another individual institution being in financial distress. The literature on co-movement risk measures has proliferated during the last few years (see, e.g., Bernardi et al [15]; Bernal et al [10]; Castro and Ferrari [16]; Girardi and Ergün [17]; Jäger-Ambrożewicz [18]; and Sordo et al [19]). Bisias et al [20] provide an extensive and up to date survey of the systemic risk measures that have been recently proposed.…”

Section: Introductionmentioning

confidence: 99%

Interconnected Risk Contributions: A Heavy-Tail Approach to Analyze U.S. Financial Sectors

Bernardi

Petrella

2015

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This paper investigates the dynamic evolution of tail risk interdependence among U.S. banks, financial services and insurance sectors. Life and non-life insurers have been considered separately to account for their different characteristics. The tail risk interdependence measurement framework relies on the multivariate Student-t Markov switching (MS) model and the multiple-conditional value-at-risk (CoVaR) (conditional expected shortfall (CoES)) risk measures introduced in Bernardi et al. (2013), accounting for both the stylized facts of financial data and the contemporaneous multiple joint distress events. The Shapley value methodology is then applied to compose the puzzle of individual risk attributions, providing a synthetic measure of tail interdependence. Our empirical investigation finds that banks appear to contribute more to the tail risk evolution of all of the remaining sectors, followed by the financial services and the insurance sectors, showing that the insurance sector significantly contributes as well to the overall risk. We also find that the role of each sector in contributing to other sectors' distress evolves over time according to the current predominant financial condition, implying different interdependence strength.

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“…Proof. Since the dispersive order is preserved by distortion functions (Theorem 13 in [27]), we have X 1 ≤ disp Y 1 and X 2 ≤ disp Y 2 . Since X and Y have the same copula, it follows from Definition 2.1 in [28] and Theorem 1 in [29] that there exists a function Φ that maps stochastically (X 1 , X 2 ) into (Y 1 , Y 2 ), i.e., Φ(X 1 , X 2 ) = st (Y 1 , Y 2 ), defined as…”

Section: A Family Of Measures Of Variabilitymentioning

confidence: 99%

Stochastic Comparisons of Some Distances between Random Variables

2021

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The aim of this paper is twofold. First, we show that the expectation of the absolute value of the difference between two copies, not necessarily independent, of a random variable is a measure of its variability in the sense of Bickel and Lehmann (1979). Moreover, if the two copies are negatively dependent through stochastic ordering, this measure is subadditive. The second purpose of this paper is to provide sufficient conditions for comparing several distances between pairs of random variables (with possibly different distribution functions) in terms of various stochastic orderings. Applications in actuarial and financial risk management are given.

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Comparison of conditional distributions in portfolios of dependent risks

Cited by 24 publications

References 28 publications

On sufficient conditions for the comparison in the excess wealth order and spacings

On sufficient conditions for the comparison in the excess wealth order and spacings

Interconnected Risk Contributions: A Heavy-Tail Approach to Analyze U.S. Financial Sectors

Stochastic Comparisons of Some Distances between Random Variables

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