Event
ISR Special Seminar: Guangxin Jiang, "Mining Simulation Data for Dynamic Risk Monitoring"
Thursday, December 15, 2016
2:00 p.m.
2224 A.V. Williams Bldg.
Michael Fu
mfu@umd.edu
Mining Simulation Data for Dynamic Risk Monitoring
Guangxin Jiang
Postdoctoral Fellow
Dept. of Economics and Finance
City University of Hong Kong
Abstract
Dynamically estimating portfolio risk measures and dynamically classifying portfolio risk levels are important yet challenging tasks, especially when they need to be done in real time. 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 and simulation model structure to enhance the estimators of the logistic regression model to make its predictions more accurate. 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 (big) 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.
Biography
Guangxin Jiang is a postdoctoral fellow in the Department of Economics and Finance at the City University of Hong Kong. He received his PhD and B.Sc in Applied Mathematics from Tongji University, China, in 2015 and 2010, respectively. His research interests lie in simulation methodology, modeling, analytics, and optimization.
