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Developing a wide easy-to-generate class of bivariate copulas

Developing a wide easy-to-generate class of bivariate copulas
Developing a wide easy-to-generate class of bivariate copulas
As of late, copulas have drawn great attention in stochastic simulation, financial engineering, and risk management. Their power lies under their ability of modeling dependent random variables. Using a known theorem in probability which proves that the fractional part of the sum of a uniform and an arbitrary independent continuous random variable follows a uniform distribution, we construct a wide class of bivariate copulas in which bivariate random vector generation can be performed easily. Some important members of this new class and their properties together with two invariant correlation measures and some insights in their application are presented
0361-0926
1919-1929
Izady, Navid
bca6a7c0-064b-4502-a273-5645723a0b02
Mahlooji, Hashem
b19f6443-613f-4e65-91fd-0b7abc29bf4c
Izady, Navid
bca6a7c0-064b-4502-a273-5645723a0b02
Mahlooji, Hashem
b19f6443-613f-4e65-91fd-0b7abc29bf4c

Izady, Navid and Mahlooji, Hashem (2008) Developing a wide easy-to-generate class of bivariate copulas. Communications in Statistics: Theory and Methods, 37 (12), 1919-1929. (doi:10.1080/03610920801893814).

Record type: Article

Abstract

As of late, copulas have drawn great attention in stochastic simulation, financial engineering, and risk management. Their power lies under their ability of modeling dependent random variables. Using a known theorem in probability which proves that the fractional part of the sum of a uniform and an arbitrary independent continuous random variable follows a uniform distribution, we construct a wide class of bivariate copulas in which bivariate random vector generation can be performed easily. Some important members of this new class and their properties together with two invariant correlation measures and some insights in their application are presented

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Published date: January 2008
Organisations: Operational Research

Identifiers

Local EPrints ID: 176775
URI: http://eprints.soton.ac.uk/id/eprint/176775
ISSN: 0361-0926
PURE UUID: 233e298e-8c48-4ffd-ba1e-b8daf8684b66

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Date deposited: 11 Mar 2011 09:08
Last modified: 19 Jul 2019 23:09

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