报告题目：Uncertain Linear Optimization, Wealth Inequality and Systemic Risk in
报告题目：Assessment and Control of Systemic Risk under Uncertain Liabilities（在不确定性负债下系统风险的评估与控制 ）
Abstract: Since the financial crisis in 2007-2008, the assessment and control of systemic risk in a financial network has become a major concern in finance and economics. In this talk, we study the vulnerability of a financial network based on the linear optimization model introduced by Eisenberg and Noe (2001), where the right hand side of the constraints is subject to market shock and only partial information regarding the liability matrix is revealed. We develop a new extended sensitivity analysis to characterize the conditions under which a bank is solvent, default or bankrupted, and estimate the probability of insolvency and the probability of bankruptcy under mild conditions on the market shock and the network structure. Particularly, we show that while an increment in the social asset may not able to improve the stability of the financial system, a larger asset inequality in the system will reduce its stability. Moreover, under certain assumption on the market shock and the network structure, we show that the least stable network can be attained at some monopoly network, which also has the highest probability of insolvency. The probability of bankruptcy in the network when all the nodes receive shocks is estimated. We also study the vulnerability of a well-balanced ring network and explore the domino effect of bankruptcy in it. Numerical experiments are presented to verify the theoretical conclusions.
The assessment and mitigation of systemic risk in a financial network has been a major concern since the financial crisis in 2007-2008. One main challenge in assessing the systemic risk lies in the unavailability of the complete information with respect to the underlying financial network. In this work, we first consider how to assess the systemic risk based on the linear optimization model introduced by Eisenberg and Noe (2001), where only partial information regarding the liability matrix is revealed. We develop an algorithm to identify the most vulnerable structure in the network. Numerical experiments illustrate that the contagious risk in the identified vulnerable network is much more significant than what underestimated in the current literature. Secondly, we develop an algorithm to identify the most stable network structure, and use it to develop a strategy for systemic risk mitigation. We evaluate the performance of the new strategy on a multi-period risk mitigation problem in which both the asset vector and the liability matrix are assumed to be uncertain. Numerical experiment shows that compared with other strategies in the literature, the proposed strategy reaches the minimal bailout cost under a full bailout policy.
Biography: Jiming Peng is an associate professor in the department of industrial engineering, University of Houston. Prior to this, he had worked in McMaster University in Canada and University of Illinois at Urbana-Champaign. His recent research interest lies mainly in optimization modeling, theoretical analysis and algorithm design with applications to healthcare, big data and finance. He has published a research monograph by Princeton University Press and over sixty papers in major optimization journals and various CS/IEEE conference proceedings. His research has been recognized by numerous awards from academic communities and supported by various funding agencies in Canada and USA.
简历: 彭积明博士是休士顿大学工业工程系的副教授。在此之前，他曾在加拿大McMaster大学和伊利诺伊大学香槟分校工作。他的最近研究兴趣主要在优化建模, 理论分析和算法设计, 以及在医疗保健，大数据和金融工程的应用。他已发表了一本学术专著(普林斯顿大学出版社) 和六十多篇研究论文。他的研究工作受到学术界的多此奖励, 和加拿大和美国自然科学基金会的支持。