A simulation analytics approach to dynamic risk monitoring

Guangxin Jiang; L. Jeff Hong; Barry L. Nelson

doi:10.1109/WSC.2016.7822110

A simulation analytics approach to dynamic risk monitoring

Guangxin Jiang
, L. Jeff Hong
, Barry L. Nelson

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

8 Scopus citations

Abstract

Simulation has been widely used as a tool to estimate risk measures of financial portfolios. However, the sample paths generated in the simulation study are often discarded after the estimate of the risk measure is obtained. In this article, we suggest to store the simulation data and propose a logistic regression based approach to mining them. We show that, at any time and conditioning on the market conditions at the time, we can quickly estimate the portfolio risk measures and classify the portfolio into either low risk or high risk categories. We call this problem dynamic risk monitoring. We study the properties of our estimators and classifiers, and demonstrate the effectiveness of our approach through numerical studies.

Original language	English (US)
Title of host publication	2016 Winter Simulation Conference
Subtitle of host publication	Simulating Complex Service Systems, WSC 2016
Editors	Theresa M. Roeder, Peter I. Frazier, Robert Szechtman, Enlu Zhou
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	437-447
Number of pages	11
ISBN (Electronic)	9781509044863
DOIs	https://doi.org/10.1109/WSC.2016.7822110
State	Published - Jul 2 2016
Externally published	Yes
Event	2016 Winter Simulation Conference, WSC 2016 - Arlington, United States Duration: Dec 11 2016 → Dec 14 2016

Publication series

Name	Proceedings - Winter Simulation Conference
Volume	0
ISSN (Print)	0891-7736

Conference

Conference	2016 Winter Simulation Conference, WSC 2016
Country/Territory	United States
City	Arlington
Period	12/11/16 → 12/14/16

Bibliographical note

Publisher Copyright:
© 2016 IEEE.

Publisher link

10.1109/WSC.2016.7822110

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Jiang, G., Hong, L. J., & Nelson, B. L. (2016). A simulation analytics approach to dynamic risk monitoring. In T. M. Roeder, P. I. Frazier, R. Szechtman, & E. Zhou (Eds.), 2016 Winter Simulation Conference: Simulating Complex Service Systems, WSC 2016 (pp. 437-447). Article 7822110 (Proceedings - Winter Simulation Conference; Vol. 0). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/WSC.2016.7822110

A simulation analytics approach to dynamic risk monitoring. / Jiang, Guangxin; Hong, L. Jeff; Nelson, Barry L.
2016 Winter Simulation Conference: Simulating Complex Service Systems, WSC 2016. ed. / Theresa M. Roeder; Peter I. Frazier; Robert Szechtman; Enlu Zhou. Institute of Electrical and Electronics Engineers Inc., 2016. p. 437-447 7822110 (Proceedings - Winter Simulation Conference; Vol. 0).

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

Jiang, G, Hong, LJ & Nelson, BL 2016, A simulation analytics approach to dynamic risk monitoring. in TM Roeder, PI Frazier, R Szechtman & E Zhou (eds), 2016 Winter Simulation Conference: Simulating Complex Service Systems, WSC 2016., 7822110, Proceedings - Winter Simulation Conference, vol. 0, Institute of Electrical and Electronics Engineers Inc., pp. 437-447, 2016 Winter Simulation Conference, WSC 2016, Arlington, United States, 12/11/16. https://doi.org/10.1109/WSC.2016.7822110

Jiang G, Hong LJ, Nelson BL. A simulation analytics approach to dynamic risk monitoring. In Roeder TM, Frazier PI, Szechtman R, Zhou E, editors, 2016 Winter Simulation Conference: Simulating Complex Service Systems, WSC 2016. Institute of Electrical and Electronics Engineers Inc. 2016. p. 437-447. 7822110. (Proceedings - Winter Simulation Conference). doi: 10.1109/WSC.2016.7822110

Jiang, Guangxin ; Hong, L. Jeff ; Nelson, Barry L. / A simulation analytics approach to dynamic risk monitoring. 2016 Winter Simulation Conference: Simulating Complex Service Systems, WSC 2016. editor / Theresa M. Roeder ; Peter I. Frazier ; Robert Szechtman ; Enlu Zhou. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 437-447 (Proceedings - Winter Simulation Conference).

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abstract = "Simulation has been widely used as a tool to estimate risk measures of financial portfolios. However, the sample paths generated in the simulation study are often discarded after the estimate of the risk measure is obtained. In this article, we suggest to store the simulation data and propose a logistic regression based approach to mining them. We show that, at any time and conditioning on the market conditions at the time, we can quickly estimate the portfolio risk measures and classify the portfolio into either low risk or high risk categories. We call this problem dynamic risk monitoring. We study the properties of our estimators and classifiers, and demonstrate the effectiveness of our approach through numerical studies.",

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N2 - Simulation has been widely used as a tool to estimate risk measures of financial portfolios. However, the sample paths generated in the simulation study are often discarded after the estimate of the risk measure is obtained. In this article, we suggest to store the simulation data and propose a logistic regression based approach to mining them. We show that, at any time and conditioning on the market conditions at the time, we can quickly estimate the portfolio risk measures and classify the portfolio into either low risk or high risk categories. We call this problem dynamic risk monitoring. We study the properties of our estimators and classifiers, and demonstrate the effectiveness of our approach through numerical studies.

AB - Simulation has been widely used as a tool to estimate risk measures of financial portfolios. However, the sample paths generated in the simulation study are often discarded after the estimate of the risk measure is obtained. In this article, we suggest to store the simulation data and propose a logistic regression based approach to mining them. We show that, at any time and conditioning on the market conditions at the time, we can quickly estimate the portfolio risk measures and classify the portfolio into either low risk or high risk categories. We call this problem dynamic risk monitoring. We study the properties of our estimators and classifiers, and demonstrate the effectiveness of our approach through numerical studies.

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A simulation analytics approach to dynamic risk monitoring

Abstract

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