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Portfolio risk assessment using multivariate extreme value methods

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Portfolio risk assessment using multivariate extreme value methods. / Hilal, Sawsan; Poon, Ser-Huang; Tawn, Jonathan.
In: Extremes, Vol. 17, No. 4, 12.2014, p. 531-556.

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

Harvard

Hilal, S, Poon, S-H & Tawn, J 2014, 'Portfolio risk assessment using multivariate extreme value methods', Extremes, vol. 17, no. 4, pp. 531-556. https://doi.org/10.1007/s10687-014-0194-9

APA

Hilal, S., Poon, S-H., & Tawn, J. (2014). Portfolio risk assessment using multivariate extreme value methods. Extremes, 17(4), 531-556. https://doi.org/10.1007/s10687-014-0194-9

Vancouver

Hilal S, Poon S-H, Tawn J. Portfolio risk assessment using multivariate extreme value methods. Extremes. 2014 Dec;17(4):531-556. Epub 2014 Jun 10. doi: 10.1007/s10687-014-0194-9

Author

Hilal, Sawsan ; Poon, Ser-Huang ; Tawn, Jonathan. / Portfolio risk assessment using multivariate extreme value methods. In: Extremes. 2014 ; Vol. 17, No. 4. pp. 531-556.

Bibtex

@article{bc9794ec8a8e4a64912ad1d3f31e56c5,
title = "Portfolio risk assessment using multivariate extreme value methods",
abstract = "This paper presents a model for the joint distribution of a portfolio by inferring extreme movements in financial markets. The core of our proposal is a statistical model for the tail of the joint distribution that attempts to capture accurately the data generating process through an extremal modelling for the univariate margins and for the multivariate dependence structure. The model addresses several features of financial returns by encompassing methods from both econometrics and extreme value theory, and hence, taking into account the asymmetric behaviour of extreme negative and positive returns, and the heterogeneous temporal as well as cross-sectional lead-lag extremal dependencies among portfolio constituents. The model facilitates scenario generation for future returns through extrapolation beyond the empirical observations upon which portfolio risk assessment is based. We provide empirical evidence for the proposed model by an application to stock market returns for the G5 economies.",
keywords = "ARMA-GARCH filtering, Asymptotic dependence, Asymptotic independence, Copula, Multivariate extreme values, 62G32 (Statistics of extreme values and tail inference), 62HXX (Multivariate analysis), 97M30 (Financial and insurance mathematics)",
author = "Sawsan Hilal and Ser-Huang Poon and Jonathan Tawn",
year = "2014",
month = dec,
doi = "10.1007/s10687-014-0194-9",
language = "English",
volume = "17",
pages = "531--556",
journal = "Extremes",
issn = "1386-1999",
publisher = "Springer Netherlands",
number = "4",

}

RIS

TY - JOUR

T1 - Portfolio risk assessment using multivariate extreme value methods

AU - Hilal, Sawsan

AU - Poon, Ser-Huang

AU - Tawn, Jonathan

PY - 2014/12

Y1 - 2014/12

N2 - This paper presents a model for the joint distribution of a portfolio by inferring extreme movements in financial markets. The core of our proposal is a statistical model for the tail of the joint distribution that attempts to capture accurately the data generating process through an extremal modelling for the univariate margins and for the multivariate dependence structure. The model addresses several features of financial returns by encompassing methods from both econometrics and extreme value theory, and hence, taking into account the asymmetric behaviour of extreme negative and positive returns, and the heterogeneous temporal as well as cross-sectional lead-lag extremal dependencies among portfolio constituents. The model facilitates scenario generation for future returns through extrapolation beyond the empirical observations upon which portfolio risk assessment is based. We provide empirical evidence for the proposed model by an application to stock market returns for the G5 economies.

AB - This paper presents a model for the joint distribution of a portfolio by inferring extreme movements in financial markets. The core of our proposal is a statistical model for the tail of the joint distribution that attempts to capture accurately the data generating process through an extremal modelling for the univariate margins and for the multivariate dependence structure. The model addresses several features of financial returns by encompassing methods from both econometrics and extreme value theory, and hence, taking into account the asymmetric behaviour of extreme negative and positive returns, and the heterogeneous temporal as well as cross-sectional lead-lag extremal dependencies among portfolio constituents. The model facilitates scenario generation for future returns through extrapolation beyond the empirical observations upon which portfolio risk assessment is based. We provide empirical evidence for the proposed model by an application to stock market returns for the G5 economies.

KW - ARMA-GARCH filtering

KW - Asymptotic dependence

KW - Asymptotic independence

KW - Copula

KW - Multivariate extreme values

KW - 62G32 (Statistics of extreme values and tail inference)

KW - 62HXX (Multivariate analysis)

KW - 97M30 (Financial and insurance mathematics)

U2 - 10.1007/s10687-014-0194-9

DO - 10.1007/s10687-014-0194-9

M3 - Journal article

VL - 17

SP - 531

EP - 556

JO - Extremes

JF - Extremes

SN - 1386-1999

IS - 4

ER -