Robust Monte Carlo, Model Risk, and Counterparty Risk
Paul Glasserman , Columbia University, United States
Date:
16 Apr 2015
Time:
3.00pm – 4.30pm
Venue:
Executive Seminar Room, Level 4, I3 Building
(This interdisciplinary PhD Lecture is organized jointly with the Risk Management Institute and NUS Finance & Risk Management Cluster)
About the Speaker
Paul Glasserman is the Jack R. Anderson Professor of Business at Columbia Business School, where he serves as research director of the Program on Financial Studies. His research interests include risk management, financial stability, and Monte Carlo methods. In 2011-2012, he was on leave from Columbia, working at the Office of Financial Research in the U.S. Treasury department, where he currently serves as a part-time consultant. His work with the OFR has included research on stress testing, financial networks, contingent capital, and counterparty risk. He has previously held visiting positions at the Federal Reserve Bank of New York, Princeton University, and NYU. Paul received the 2008 Lanchester Prize for his book “Monte Carlo Methods in Financial Engineering.” He is also a past recipient of the Erlang Prize in applied probability and Risk magazine’s Quant of the Year award. Paul served as senior vice dean of Columbia Business School in 2004-2008 and was interim director its Sanford C. Bernstein Center for Leadership and Ethics in 2005-2007.
Abstract
Simulation methodology has traditionally focused on measuring and reducing sampling error in simulating well-specified models; it has given less attention to quantifying the effect of model error or model uncertainty. But simulation actually lends itself well to bounding this sort of model risk. In particular, if the set of alternative models consists of all models within a certain “distance” of a baseline model, then the potential effect of model risk can be estimated at low cost within a simulation of the baseline model. I will illustrate this approach to making Monte Carlo robust with examples from finance, where concerns about model risk have received heightened attention. The problem of bounding “wrong-way risk” in counterparty risk presents a related question in which model uncertainty is limited to the nature of the dependence between two otherwise certain marginal models for market and credit risk. The effect of uncertain dependence can be bounded through a convenient combination of simulation and optimization. This talk is based on work with Xingbo Xu and Linan Yang.
