On the invariant properties of notions of positive dependence and copulas under increasing transformations

Jun Cai, Wei Wei

Research output: Contribution to journalArticlepeer-review

Abstract

Notions of positive dependence and copulas play important roles in modeling dependent risks. The invariant properties of notions of positive dependence and copulas under increasing transformations are often used in the studies of economics, finance, insurance and many other fields. In this paper, we examine the notions of the conditionally increasing (CI), the conditionally increasing in sequence (CIS), the positive dependence through the stochastic ordering (PDS), and the positive dependence through the upper orthant ordering (PDUO). We first use counterexamples to show that the statements in Theorem 3.10.19 of Müller and Stoyan (2002) about the invariant properties of CIS and CI under increasing transformations are not true. We then prove that the invariant properties of CIS and CI hold under strictly increasing transformations. Furthermore, we give rigorous proofs for the invariant properties of PDS and PDUO under increasing transformations. These invariant properties enable us to show that a continuous random vector is PDS (PDUO) if and only of its copula is PDS (PDUO). In addition, using the properties of generalized left-continuous and right-continuous inverse functions, we give a rigorous proof for the invariant property of copulas under increasing transformations on the components of any random vector. This result generalizes Proposition 4.7.4 of Denuit etal. (2005) and Proposition 5.6. of McNeil etal. (2005).

Original languageEnglish (US)
Pages (from-to)43-49
Number of pages7
JournalInsurance: Mathematics and Economics
Volume50
Issue number1
DOIs
StatePublished - Jan 2012
Externally publishedYes

Keywords

  • CI
  • CIS
  • Copula
  • Dependent risk
  • Generalized left-continuous inverse function
  • Generalized right-continuous inverse function
  • PDS
  • PDUO
  • Positive dependence
  • Survival copula

ASJC Scopus subject areas

  • Statistics and Probability
  • Economics and Econometrics
  • Statistics, Probability and Uncertainty

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