Sensitivity Analysis of CoVaR

Han Fu; L. Jeff Hong; Guangxin Jiang

doi:10.1109/CASE56687.2023.10260403

Sensitivity Analysis of CoVaR

Han Fu, L. Jeff Hong, Guangxin Jiang

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Scopus citations

Abstract

CoVaR is an important measure for assessing the systemic risk of a network composed of many systems. To optimize and control the systemic risk of the network, we need to know the sensitivity of CoVaR. In this paper, we derive closed-form expressions of the CoVaR sensitivities and design batched estimators using the infinitesimal perturbation analysis (IPA) and finite-difference methods. We establish the consistency and asymptotic normality of the proposed estimators and show that the convergence rate of the estimators is strictly slower than n-1/6. Numerical experiments show the effectiveness of our estimator and support the theoretical results.

Original language	English (US)
Title of host publication	2023 IEEE 19th International Conference on Automation Science and Engineering, CASE 2023
Publisher	IEEE Computer Society
ISBN (Electronic)	9798350320695
DOIs	https://doi.org/10.1109/CASE56687.2023.10260403
State	Published - 2023
Externally published	Yes
Event	19th IEEE International Conference on Automation Science and Engineering, CASE 2023 - Auckland, New Zealand Duration: Aug 26 2023 → Aug 30 2023

Publication series

Name	IEEE International Conference on Automation Science and Engineering
Volume	2023-August
ISSN (Print)	2161-8070
ISSN (Electronic)	2161-8089

Conference

Conference	19th IEEE International Conference on Automation Science and Engineering, CASE 2023
Country/Territory	New Zealand
City	Auckland
Period	8/26/23 → 8/30/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Publisher link

10.1109/CASE56687.2023.10260403

Find It

Find It at U of M Libraries

Cite this

Sensitivity Analysis of CoVaR. / Fu, Han; Hong, L. Jeff; Jiang, Guangxin.
2023 IEEE 19th International Conference on Automation Science and Engineering, CASE 2023. IEEE Computer Society, 2023. (IEEE International Conference on Automation Science and Engineering; Vol. 2023-August).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Fu, H, Hong, LJ & Jiang, G 2023, Sensitivity Analysis of CoVaR. in 2023 IEEE 19th International Conference on Automation Science and Engineering, CASE 2023. IEEE International Conference on Automation Science and Engineering, vol. 2023-August, IEEE Computer Society, 19th IEEE International Conference on Automation Science and Engineering, CASE 2023, Auckland, New Zealand, 8/26/23. https://doi.org/10.1109/CASE56687.2023.10260403

@inproceedings{18fc2887284e4561bd4883c25f11acba,

title = "Sensitivity Analysis of CoVaR",

abstract = "CoVaR is an important measure for assessing the systemic risk of a network composed of many systems. To optimize and control the systemic risk of the network, we need to know the sensitivity of CoVaR. In this paper, we derive closed-form expressions of the CoVaR sensitivities and design batched estimators using the infinitesimal perturbation analysis (IPA) and finite-difference methods. We establish the consistency and asymptotic normality of the proposed estimators and show that the convergence rate of the estimators is strictly slower than n-1/6. Numerical experiments show the effectiveness of our estimator and support the theoretical results.",

author = "Han Fu and Hong, {L. Jeff} and Guangxin Jiang",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 19th IEEE International Conference on Automation Science and Engineering, CASE 2023 ; Conference date: 26-08-2023 Through 30-08-2023",

year = "2023",

doi = "10.1109/CASE56687.2023.10260403",

language = "English (US)",

series = "IEEE International Conference on Automation Science and Engineering",

publisher = "IEEE Computer Society",

booktitle = "2023 IEEE 19th International Conference on Automation Science and Engineering, CASE 2023",

}

TY - GEN

T1 - Sensitivity Analysis of CoVaR

AU - Fu, Han

AU - Hong, L. Jeff

AU - Jiang, Guangxin

PY - 2023

Y1 - 2023

N2 - CoVaR is an important measure for assessing the systemic risk of a network composed of many systems. To optimize and control the systemic risk of the network, we need to know the sensitivity of CoVaR. In this paper, we derive closed-form expressions of the CoVaR sensitivities and design batched estimators using the infinitesimal perturbation analysis (IPA) and finite-difference methods. We establish the consistency and asymptotic normality of the proposed estimators and show that the convergence rate of the estimators is strictly slower than n-1/6. Numerical experiments show the effectiveness of our estimator and support the theoretical results.

AB - CoVaR is an important measure for assessing the systemic risk of a network composed of many systems. To optimize and control the systemic risk of the network, we need to know the sensitivity of CoVaR. In this paper, we derive closed-form expressions of the CoVaR sensitivities and design batched estimators using the infinitesimal perturbation analysis (IPA) and finite-difference methods. We establish the consistency and asymptotic normality of the proposed estimators and show that the convergence rate of the estimators is strictly slower than n-1/6. Numerical experiments show the effectiveness of our estimator and support the theoretical results.

UR - http://www.scopus.com/inward/record.url?scp=85174411908&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85174411908&partnerID=8YFLogxK

U2 - 10.1109/CASE56687.2023.10260403

DO - 10.1109/CASE56687.2023.10260403

M3 - Conference contribution

AN - SCOPUS:85174411908

T3 - IEEE International Conference on Automation Science and Engineering

BT - 2023 IEEE 19th International Conference on Automation Science and Engineering, CASE 2023

PB - IEEE Computer Society

T2 - 19th IEEE International Conference on Automation Science and Engineering, CASE 2023

Y2 - 26 August 2023 through 30 August 2023

ER -

Sensitivity Analysis of CoVaR

Abstract

Publication series

Conference

Bibliographical note

Publisher link

Find It

Other files and links

Fingerprint

Cite this