演讲主题：Simulating bounds for conditional Value-at-Risk

主 讲 人：刘光梧副教授香港城市大学

时    间：2018年5月24日（周四）15:30-17:00

地    点：文泉楼南106

摘    要：Portfolio risk measurement under the nested setting is a challenging computational problem, and has received increasing attention in recent years. This nested setting often requires mark-to-market reevaluation of the portfolio for a large number of possible scenarios of risk factors up to a future time horizon. When closed-form formula is not available, reevaluation may require intensive simulations, and existing literature has been focused on efficient allocation of simulation budgets and development of metamodeling methods for approximating portfolio loss function.

In this paper, we study the estimation of a widely used risk measure, the so-called conditional Value-at-Risk (CVaR) from a different perspective. By leveraging existing reevaluation methods, we propose a lower-bound estimator for any approximating function of portfolio loss. We further show that CVaR can be represented as the optimal value of an appropriately constructed optimal stopping, based on which we propose a new upper-bound estimator that does not require nested simulations. Numerical experiments show the proposed lower- and upper-bound estimators are quite tight.

演讲者简介：Dr. Guangwu Liu is currently an associate professor in the Department of Management Sciences, College of Business at City University of Hong Kong. His research interests include stochastic simulation, financial engineering and risk management. He has published in top journals of the field, including ACM Transactions on Modeling and Computer Simulation, INFORMS Journal on Computing, Management Science, and Operations Research.