A Statistical Analysis of Global Economies Using Time Varying Copulas

Emmanuel Afuecheta; Saralees Nadarajah; Stephen Chan

doi:10.1007/s10614-020-09996-y

A Statistical Analysis of Global Economies Using Time Varying Copulas

Emmanuel Afuecheta, Saralees Nadarajah^*, Stephen Chan

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

1 Scopus citations

Abstract

The application of time varying copulas has become popular in recent years. Here, we illustrate an application involving stock indices of ten major economies covering all of the six continents. The dependence among them and its variation with respect to time are modeled using ten different copulas. The Gaussian copula is found to give the best fit. Predictions are given in terms of correlations and value at risk.

Original language	English
Pages (from-to)	1167-1194
Number of pages	28
Journal	Computational Economics
Volume	58
Issue number	4
DOIs	https://doi.org/10.1007/s10614-020-09996-y
State	Published - Dec 2021

Bibliographical note

Publisher Copyright:
© 2020, Springer Science+Business Media, LLC, part of Springer Nature.

Keywords

Estimation
Linear trend
Value at risk

ASJC Scopus subject areas

Economics, Econometrics and Finance (miscellaneous)
Computer Science Applications

Access to Document

10.1007/s10614-020-09996-y

Cite this

@article{10d5727daf5647bbb123864940ae06c0,

title = "A Statistical Analysis of Global Economies Using Time Varying Copulas",

abstract = "The application of time varying copulas has become popular in recent years. Here, we illustrate an application involving stock indices of ten major economies covering all of the six continents. The dependence among them and its variation with respect to time are modeled using ten different copulas. The Gaussian copula is found to give the best fit. Predictions are given in terms of correlations and value at risk.",

keywords = "Estimation, Linear trend, Value at risk",

author = "Emmanuel Afuecheta and Saralees Nadarajah and Stephen Chan",

note = "Publisher Copyright: {\textcopyright} 2020, Springer Science+Business Media, LLC, part of Springer Nature.",

year = "2021",

month = dec,

doi = "10.1007/s10614-020-09996-y",

language = "English",

volume = "58",

pages = "1167--1194",

journal = "Computational Economics",

issn = "0927-7099",

publisher = "Springer Netherlands",

number = "4",

}

TY - JOUR

T1 - A Statistical Analysis of Global Economies Using Time Varying Copulas

AU - Afuecheta, Emmanuel

AU - Nadarajah, Saralees

AU - Chan, Stephen

PY - 2021/12

Y1 - 2021/12

N2 - The application of time varying copulas has become popular in recent years. Here, we illustrate an application involving stock indices of ten major economies covering all of the six continents. The dependence among them and its variation with respect to time are modeled using ten different copulas. The Gaussian copula is found to give the best fit. Predictions are given in terms of correlations and value at risk.

AB - The application of time varying copulas has become popular in recent years. Here, we illustrate an application involving stock indices of ten major economies covering all of the six continents. The dependence among them and its variation with respect to time are modeled using ten different copulas. The Gaussian copula is found to give the best fit. Predictions are given in terms of correlations and value at risk.

KW - Estimation

KW - Linear trend

KW - Value at risk

UR - https://www.scopus.com/pages/publications/85086169347

U2 - 10.1007/s10614-020-09996-y

DO - 10.1007/s10614-020-09996-y

M3 - Article

AN - SCOPUS:85086169347

SN - 0927-7099

VL - 58

SP - 1167

EP - 1194

JO - Computational Economics

JF - Computational Economics

IS - 4

ER -

A Statistical Analysis of Global Economies Using Time Varying Copulas

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

Fingerprint

Cite this