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讲座预告 | Operational Risk Management: A Stochastic Control Framework with Preventive and Corrective Controls

发布时间:2019-06-13    来源:
讲座时间:20196月18日 10:00

讲座地点:25教学楼A3B教室

主讲人:Prof. Michael Pinedo




主讲人简介:Michael Pinedo is the Julius Schlesinger Professor of Operations Management at New York University's Stern School of Business.  He received an Ir. degree in Mechanical Engineering from Delft University of Technology (in the Netherlands) in 1973 and a Ph.D. in Operations Research from the University of California at Berkeley in 1978.  He has taught at Columbia University from 1982 till 1997 and at New York University since 1997.  His research focuses on the modeling of service systems, and in the development of planning and scheduling systems, as well as systems for measuring operational risk. Over the last decade his research has focused on operational risk in financial services.  He is co-editor of Creating Value in Financial Services: Strategies, Operations, and Technologies (Kluwer), and co-editor of Global Asset Management: Strategies, Risks, Processes, and Technologies   (Palgrave/McMillan).  He has co-authored the book Operations in Financial Services - Processes, Technologies, and Risks (NOW Publishers) together with Yuqian Xu.  Professor Pinedo has been actively involved in industrial system development. He supervised the development of systems at Goldman Sachs, Siemens, and at Merck. Professor Pinedo is Editor of the Journal of Scheduling (Springer), Associate Editor of the Journal of Operational Risk, Department Editor of Production and Operations Management and Associate Editor of Annals of Operations Research.


讲座内容: We propose a general modeling framework for operational risk management of financial firms. We consider operational risk events as shocks to a financial firm's value process, and then study capital investments in preventive and corrective controls to mitigate risk losses. The optimal decisions are made in three scenarios: (i) preventive control only, (ii) corrective control only, and (iii) joint controls. We characterize the optimal control policies within a general modeling framework that comprises these three scenarios, and then discuss an exponential risk reduction function. We conclude our work with an application of our model to a data set from a commercial bank. We find that through a proper investment strategy, we can achieve a significant performance improvement, especially when the risk severity level is high. Moreover, with controls, the value of the firm tends to increase relative to the value of the firm without controls.  Hence the controls are essentially smoothing out the jump losses and increasing the value of the firm. At the bank we analyze we find that with a joint control strategy the bank can achieve profit increases from 7.45% to 11.62% when the risk reduction efficiencies of the two controls are high. In general, our modeling framework, which combines a typical operational risk process with stochastic control, may suggest a new research direction in operations management and operational risk management.

 

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