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Time-varying extreme value dependence with applications to leading European stock markets

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Time-varying extreme value dependence with applications to leading European stock markets. / de Carvalho, Miguel; Castro Camilo, Daniela; Wadsworth, Jennifer Lynne.
In: Annals of Applied Statistics, Vol. 12, No. 1, 01.03.2018, p. 283-309.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

de Carvalho, M, Castro Camilo, D & Wadsworth, JL 2018, 'Time-varying extreme value dependence with applications to leading European stock markets', Annals of Applied Statistics, vol. 12, no. 1, pp. 283-309. https://doi.org/10.1214/17-AOAS1089

APA

de Carvalho, M., Castro Camilo, D., & Wadsworth, J. L. (2018). Time-varying extreme value dependence with applications to leading European stock markets. Annals of Applied Statistics, 12(1), 283-309. https://doi.org/10.1214/17-AOAS1089

Vancouver

de Carvalho M, Castro Camilo D, Wadsworth JL. Time-varying extreme value dependence with applications to leading European stock markets. Annals of Applied Statistics. 2018 Mar 1;12(1):283-309. doi: 10.1214/17-AOAS1089

Author

de Carvalho, Miguel ; Castro Camilo, Daniela ; Wadsworth, Jennifer Lynne. / Time-varying extreme value dependence with applications to leading European stock markets. In: Annals of Applied Statistics. 2018 ; Vol. 12, No. 1. pp. 283-309.

Bibtex

@article{9f4f152ffb0d402d8f97c883197ef37a,
title = "Time-varying extreme value dependence with applications to leading European stock markets",
abstract = "Extremal dependence between international stock markets is of particular interest in today{\textquoteright}s global financial landscape. However, previous studies have shown this dependence is not necessarily stationary over time. We concern ourselves with modeling extreme value dependence when that dependence is changing over time, or other suitable covariate. Working within a framework of asymptotic dependence, we introduce a regression model for the angular density of a bivariate extreme value distribution that allows us to assess how extremal dependence evolves over a covariate. We apply the proposed model to assess the dynamics governing extremal dependence of some leading European stock markets over the last three decades, and find evidence of an increase in extremal dependence over recent years.",
keywords = "Angular measur, bivariate extreme values, European stock market integration , risk, statistics of extremes",
author = "{de Carvalho}, Miguel and {Castro Camilo}, Daniela and Wadsworth, {Jennifer Lynne}",
year = "2018",
month = mar,
day = "1",
doi = "10.1214/17-AOAS1089",
language = "English",
volume = "12",
pages = "283--309",
journal = "Annals of Applied Statistics",
issn = "1932-6157",
publisher = "Institute of Mathematical Statistics",
number = "1",

}

RIS

TY - JOUR

T1 - Time-varying extreme value dependence with applications to leading European stock markets

AU - de Carvalho, Miguel

AU - Castro Camilo, Daniela

AU - Wadsworth, Jennifer Lynne

PY - 2018/3/1

Y1 - 2018/3/1

N2 - Extremal dependence between international stock markets is of particular interest in today’s global financial landscape. However, previous studies have shown this dependence is not necessarily stationary over time. We concern ourselves with modeling extreme value dependence when that dependence is changing over time, or other suitable covariate. Working within a framework of asymptotic dependence, we introduce a regression model for the angular density of a bivariate extreme value distribution that allows us to assess how extremal dependence evolves over a covariate. We apply the proposed model to assess the dynamics governing extremal dependence of some leading European stock markets over the last three decades, and find evidence of an increase in extremal dependence over recent years.

AB - Extremal dependence between international stock markets is of particular interest in today’s global financial landscape. However, previous studies have shown this dependence is not necessarily stationary over time. We concern ourselves with modeling extreme value dependence when that dependence is changing over time, or other suitable covariate. Working within a framework of asymptotic dependence, we introduce a regression model for the angular density of a bivariate extreme value distribution that allows us to assess how extremal dependence evolves over a covariate. We apply the proposed model to assess the dynamics governing extremal dependence of some leading European stock markets over the last three decades, and find evidence of an increase in extremal dependence over recent years.

KW - Angular measur

KW - bivariate extreme values

KW - European stock market integration

KW - risk

KW - statistics of extremes

U2 - 10.1214/17-AOAS1089

DO - 10.1214/17-AOAS1089

M3 - Journal article

VL - 12

SP - 283

EP - 309

JO - Annals of Applied Statistics

JF - Annals of Applied Statistics

SN - 1932-6157

IS - 1

ER -