A simulation analytics approach to dynamic risk monitoring

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)Not applicablepeer-review

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Author(s)

Detail(s)

Original languageEnglish
Title of host publicationProceedings - Winter Simulation Conference
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages437-447
ISBN (Print)9781509044863
StatePublished - 17 Jan 2017

Publication series

Name
ISSN (Print)0891-7736

Conference

Title2016 Winter Simulation Conference, WSC 2016
PlaceUnited States
CityWashington
Period11 - 14 December 2016

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.

Citation Format(s)

A simulation analytics approach to dynamic risk monitoring. / Jiang, Guangxin; Hong, L. Jeff; Nelson, Barry L.

Proceedings - Winter Simulation Conference. Institute of Electrical and Electronics Engineers Inc., 2017. p. 437-447.

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)Not applicablepeer-review