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) |
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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 | |
State | Published - Jul 2 2016 |
Event | 2016 Winter Simulation Conference, WSC 2016 - Arlington, United States Duration: Dec 11 2016 → Dec 14 2016 |
Publication series
Name | Proceedings - Winter Simulation Conference |
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Volume | 0 |
ISSN (Print) | 0891-7736 |
Other
Other | 2016 Winter Simulation Conference, WSC 2016 |
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Country/Territory | United States |
City | Arlington |
Period | 12/11/16 → 12/14/16 |
Funding
The research of L. Jeff Hong was partially supported by Hong Kong Research Grants Council under projects GRF613213 and GRF16203214. The research of Barry L. Nelson was partially supported by the National Science Foundation under Grant Number CMMI-1537060 and GOALI co-sponsor SAS Institute.
ASJC Scopus subject areas
- Software
- Modeling and Simulation
- Computer Science Applications