Background Risk Models and Stepwise Portfolio Construction

Asimit, Alexandru V.; Vernic, Raluca; Zitikis, Ričardas

doi:10.1007/s11009-015-9458-3

Cited by 21 publications

(9 citation statements)

References 42 publications

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“…Theorem 2.2 establishes the multiplicative background risk representation of the multivariate probabilistic structure of main interest herein (see, Franke et al, 2006;Meyers, 2007, Asimit et al, 2013, 2016 for applications of the multiplicative background risk models in economics and actuarial science).…”

Section: (21) and It Is Thus The N-variate Pareto Distribution Intrmentioning

confidence: 89%

A Form of Multivariate Pareto Distribution With Applications to Financial Risk Measurement

Furman

2016

ASTIN Bull.

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Mathematics and Economics 46(2), 308 -316], the structure in this paper is absolutely continuous with respect to the corresponding Lebesgue measure. The distribution is of importance to actuaries through its connections to the popular frailty models, as well as because of the capacity to describe dependent heavy-tailed risks. The genesis of the new distribution is linked to a number of existing probability models, and useful characteristic results are proved. Expressions for, e.g., the decumulative distribution and probability density functions, (joint) moments and regressions are developed.The distributions of minima and maxima, as well as, some weighted risk measures are employed to exemplify possible applications of the distribution in insurance.

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Section: (21) and It Is Thus The N-variate Pareto Distribution Intrmentioning

confidence: 89%

A Form of Multivariate Pareto Distribution With Applications to Financial Risk Measurement

Furman

2016

ASTIN Bull.

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“…. , X n ) in Definition 1 admits the following multiplicative background risk model representation (see, Asimit et al 2016;Frank et al 2006, for the corresponding economic implication and application)…”

Section: Definition and Basic Propertiesmentioning

confidence: 99%

On a Multiplicative Multivariate Gamma Distribution with Applications in Insurance

Semenikhine

Furman

2018

Risks

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Abstract:One way to formulate a multivariate probability distribution with dependent univariate margins distributed gamma is by using the closure under convolutions property. This direction yields an additive background risk model, and it has been very well-studied. An alternative way to accomplish the same task is via an application of the Bernstein-Widder theorem with respect to a shifted inverse Beta probability density function. This way, which leads to an arguably equally popular multiplicative background risk model (MBRM), has been by far less investigated. In this paper, we reintroduce the multiplicative multivariate gamma (MMG) distribution in the most general form, and we explore its various properties thoroughly. Specifically, we study the links to the MBRM, employ the machinery of divided differences to derive the distribution of the aggregate risk random variable explicitly, look into the corresponding copula function and the measures of nonlinear correlation associated with it, and, last but not least, determine the measures of maximal tail dependence. Our main message is that the MMG distribution is (1) very intuitive and easy to communicate, (2) remarkably tractable, and (3) possesses rich dependence and tail dependence characteristics. Hence, the MMG distribution should be given serious considerations when modelling dependent risks.

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“…For applications and discussions of additive models in Economic Theory, we refer to Gollier and Pratt (1996) and references therein, and to problems in Actuarial Science, we refer to Landsman (2005, 2010); Tsanakas (2008), and references therein. Our current research is essentially based on the multiplicative model, which has been extensively explored and utilized in the literature (see, e.g., Tsetlin and Winkler 2005;Franke et al 2006Franke et al , 2011Asimit et al 2016; references therein). It is worth noting that a number of important parametric multiplicative models incorporate elements of both Pareto and gamma distributions, and we refer to Asimit et al (2016); Su (2016) and Su and Furman (2017) for details and further references.…”

Section: Note 41 We Have Reserved F For Denoting Cdf 'S As Is Usualmentioning

confidence: 99%

Optimal two-stage pricing strategies from the seller’s perspective under the uncertainty of buyer’s decisions

Egozcue

Zitikis

2017

J Stat Distrib App

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In Punta del Este, a resort town in Uruguay, real-estate property is in demand by both domestic and foreign buyers. There are several stages of selling residential units: before, during, and after the actual construction. Different pricing strategies are used at every stage. Our goal in this paper is to derive, under various scenarios of practical relevance, optimal strategies for setting prices within two-stage selling framework, as well as to explore the optimal timing for accomplishing these tasks in order to maximize the overall seller's expected revenue. Specifically, we put forward a general two-period pricing model and explore pricing strategies from the seller's perspective, when the buyer's decisions in the two periods are uncertain: commodity valuations may or may not be independent, may or may not follow the same distribution, be heavily or just lightly influenced by exogenous economic conditions, and so on. Our theoretical findings are illustrated with numerical and graphical examples using appropriately constructed parametric models.

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Background Risk Models and Stepwise Portfolio Construction

Abstract: This is the accepted version of the paper.This version of the publication may differ from the final published version. Permanent

Cited by 21 publications

References 42 publications

A Form of Multivariate Pareto Distribution With Applications to Financial Risk Measurement

A Form of Multivariate Pareto Distribution With Applications to Financial Risk Measurement

On a Multiplicative Multivariate Gamma Distribution with Applications in Insurance

Optimal two-stage pricing strategies from the seller’s perspective under the uncertainty of buyer’s decisions

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