Online risk monitoring using offline simulation

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

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Estimating portfolio risk measures and classifying portfolio risk levels in real time are important yet challenging tasks. In this paper, we propose to build a logistic regression model using data generated in past simulation experiments and to use the model to predict portfolio risk measures and classify risk levels at any time. We further explore regularization techniques, simulation model structure, and additional simulation budget to enhance the estimators of the logistic regression model to make its predictions more precise. Our numerical results show that the proposed methods work well. Our work may be viewed as an example of the recently proposed idea of simulation analytics, which treats a simulation model as a data generator and proposes to apply data analytics tools to the simulation outputs to uncover conditional statements. Our work shows that the simulation analytics idea is viable and promising in the field of financial risk management.

Original languageEnglish (US)
Pages (from-to)356-375
Number of pages20
JournalINFORMS Journal on Computing
Volume32
Issue number2
DOIs
StatePublished - Mar 2020

Keywords

  • Classification
  • Lasso
  • Logistic regression
  • Monte Carlo simulation
  • Simulation analytics
  • Variance reduction

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Computer Science Applications
  • Management Science and Operations Research

Fingerprint Dive into the research topics of 'Online risk monitoring using offline simulation'. Together they form a unique fingerprint.

Cite this