A simulation analytics approach to dynamic risk monitoring

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations


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 languageEnglish (US)
Title of host publication2016 Winter Simulation Conference
Subtitle of host publicationSimulating Complex Service Systems, WSC 2016
EditorsTheresa M. Roeder, Peter I. Frazier, Robert Szechtman, Enlu Zhou
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages11
ISBN (Electronic)9781509044863
StatePublished - Jul 2 2016
Event2016 Winter Simulation Conference, WSC 2016 - Arlington, United States
Duration: Dec 11 2016Dec 14 2016

Publication series

NameProceedings - Winter Simulation Conference
ISSN (Print)0891-7736


Other2016 Winter Simulation Conference, WSC 2016
Country/TerritoryUnited States

ASJC Scopus subject areas

  • Software
  • Modeling and Simulation
  • Computer Science Applications


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