[Konstantinos Spiliopoulos] Risk and Default Clustering In Large Financial Networks
Risk and Default Clustering In Large Financial Networks
Konstantinos Spiliopoulos
Junior Faculty Fellow, Hariri Institute for Computing
Assistant Professor, Department of Mathematics & Statistics
Boston University
Abstract: The past several years have made clear the need to better understand the behavior of risk in large interconnected financial networks. Interconnections often make a system robust, but they can act as conduits for risk. In this talk, I will present recent results on modeling the dynamics of correlated default events in the financial market. An empirically motivated system of interacting point processes is introduced and we study how different types of risk, like contagion and exposure to systematic risk, compete and interact in large-scale systems. Network effects are analyzed and computational methods are developed to assess the behavior and the stability of the network. We develop methods for (a) approximating the distribution of the loss from default in large, potentially heterogeneous, portfolios, (b) capturing the most likely paths to systemic failure, (c) quantifying large portfolio losses (e.g., provably efficient Monte Carlo methods), and (d) understanding the network effects. Numerical results illustrate the accuracy of the approximations. The results give insights into how different sources of default correlation interact to generate typical and atypical portfolio losses. Understanding of these issues can be useful to regulators and give useful insights into how to optimally safeguard against bad events such as large portfolio losses.
Bio: Konstantinos Spiliopoulos is a Junior Fellow of the Hariri Institute and an Assistant Professor in the Department of Mathematics and Statistics at BU, which he joined in 2012. After receiving his Ph.D. at the University of Maryland at College Park, he was a Prager Assistant Professor at Brown University. His current research work focuses in two main areas: Monte Carlo methods, rare event simulation and mathematical analysis of multi-scale systems and equilibrium problems, and development of mathematical and computational tools for the quantification of systemic risk in large financial networks.