Efficient Risk Estimation via Nested Sequential Simulation

Mark Broadie (), Yiping Du () and Ciamac C. Moallemi ()
Additional contact information
Mark Broadie: Graduate School of Business, Columbia University, New York, New York 10027
Yiping Du: Industrial Engineering and Operations Research, Columbia University, New York, New York 10027
Ciamac C. Moallemi: Graduate School of Business, Columbia University, New York, New York 10027

Management Science, 2011, vol. 57, issue 6, 1172-1194

Abstract: We analyze the computational problem of estimating financial risk in a nested simulation. In this approach, an outer simulation is used to generate financial scenarios, and an inner simulation is used to estimate future portfolio values in each scenario. We focus on one risk measure, the probability of a large loss, and we propose a new algorithm to estimate this risk. Our algorithm sequentially allocates computational effort in the inner simulation based on marginal changes in the risk estimator in each scenario. Theoretical results are given to show that the risk estimator has a faster convergence order compared to the conventional uniform inner sampling approach. Numerical results consistent with the theory are presented. This paper was accepted by Gérard Cachon, stochastic models and simulation.

Keywords: simulation; decision analysis; risk; risk management; sequential analysis (search for similar items in EconPapers)
Date: 2011
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (45)

Downloads: (external link)
http://dx.doi.org/10.1287/mnsc.1110.1330 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:57:y:2011:i:6:p:1172-1194

Access Statistics for this article

More articles in Management Science from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().