Robust Monte Carlo, Model Risk, and Counterparty Risk
Date: Thursday, April 23, 2014
Speaker: Paul Glasserman, Jack R. Anderson Professor, Columbia Business School
SPECIAL LOCATION: Math Corner 380-380Y - 4:15 to 5:15 pm
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.
Bio :Professor Glasserman's research addresses risk management, derivative securities, Monte Carlo simulation, and financial stability. Prior to joining Columbia, Glasserman was with Bell Laboratories; he has also held visiting positions at Princeton University, NYU, the Federal Reserve Bank of New York, and the U.S. Treasury's Office of Financial Research. Glasserman's publications include the book Monte Carlo Methods in Financial Engineering (Springer, 2004), which received the 2006 Lanchester Prize and the 2005 I-Sim Outstanding Publication Award. Glasserman is a past recipient of the Erlang prize in applied probability (1996), an IMS Medallion (2006), and Risk Magazine's 2007 Quant of the Year Award. He was named an INFORMS Fellow in 2008.
Spring 2103 Lectures
Designing Incentive Systems for Truthful Information Sharing in Supply chains
Date: Thursday, April 25, 2013
Speaker: Ulrich Thonemann, Professor for Supply Chain Management at the University of Cologne, Director of the Department of Supply Chain Management
We consider a firm where sales is responsible for demand forecasting and operations is responsible for ordering. Sales has better information about the demand than operations and sends a non-binding demand forecast to operations. Based on the demand forecast, operations determines the order quantity. To incentivize truthful demand information sharing, we include a penalty for forecast errors in the incentive system of sales. In the utility function of sales, we also include the behavioral factors lying aversion and loss aversion. We model the setting as a signaling game and derive equilibria of the game. In a laboratory experiment, we observe human behavior that is in-line with the model predictions, but deviates substantially from expected payoff maximizing behavior. Finally, we use the behavioral model to design incentive systems for truthful information sharing and conduct an experiment to validate the approach with out-of-sample treatments and out-of-sample subjects.
Ulrich Thonemann is a Professor of Supply Chain Management at the University of Cologne. He started his academic career as an Assistant Professor of Industrial Engineering and Engineering Management at Stanford University and also holds Masters and PhD degrees from Stanford. Before joining academia, he worked as a management consultant at McKinsey & Company in Cologne. His current research focuses on Supply Chain Management, Service Management, and Behavioral Operations and on the application of state-of-the-art approaches in industry.
Cascading Processes and Network Structure
Date: Thursday, April 11, 2013
Speaker: Jon Kleinberg, Professor in Computer Science, Information Science,
The flow of a cascade through a network can model something desirable, such as a product or social movement that is promoted via links, or something dangerous, such as an epidemic or cascading failure that spreads contagiously. Research over the past several years has developed methods for reasoning about the connection between network structure and the ways in which cascades develop; this line of work has included probabilistic and graph-theoretic models, as well as studies of the spread of behavior in large datasets. We discuss a set of related results in this area, focusing on the distinction between "open" and "closed" network structures, and the different ways in which they can affect the outcome of a cascading process.
The talk will include joint work with Lars Backstrom, Larry Blume, David Easley, Bobby Kleinberg, Cameron Marlow, Eva Tardos, and Johan Ugander.
Systemic Risk in Networks
Date: Thursday, January 24, 2013
Speaker: Asuman Ozdaglar, Professor in Electrical Engineering and Computer Science,
Massachusetts Institute of Technology
We provide a tractable framework for studying systemic risk in networks, focusing on economic and financial networks. We first analyze the emergence of systemic risk in a production economy with an input-output structure whereby shocks to some sectors spread to their downstream sectors and beyond. We show how the nature and magnitude of systemic risk relates to the network structure of the economy, isolating the impact of first and higher order interconnections. Contrary to a common conjecture, we show that rings and other sparse regular networks are robust (i.e. as robust as a complete network) to cascades and do not generate systemic risk. Instead, systemic risk is present when some sectors are disproportionately important in the supply economy.
We then focus on interlinkages created by financial transactions (counterparty relations). We show that systemic risk in financial networks exhibits a form of phase transition as interbank connections increase. In particular, we demonstrate that as long as the magnitude and the number of negative shocks affecting financial institutions are sufficiently small, more “complete” interbank claims enhance the stability of the system. However, beyond a certain point, such interconnections start to serve as a mechanism for propagation of shocks and hence, lead to a more fragile financial system. Even in this more nonlinear financial network setting, our results thus show that the conjecture about the instability of rings is not generally true: rings are unstable when shocks are small, but it is more densely connected networks that are more unstable when shocks are large.
More Stars Stay, but the Brightest Ones Still Leave: Job Hopping in the Shadow of Patent Enforcement
Date: Thursday, December 6, 2012
Speaker: Rajshree Agarwal, Professor and Dean’s Chair in Strategy and Entrepreneurship at University of Maryland
This study investigates how a firm’s aggressiveness in enforcing patents (i.e., its “reputation for IP toughness”) affects employee-level mobility decisions. We predict and find that as a firm grows more litigious over patents, its inventive employees are less likely to ‘job hop’ to other firms within the industry. Based on evidence from the U.S. semiconductor industry, we also find that litigiousness shifts the distribution of employee exits: litigiousness operates as a sorting mechanism that aids in the retention of employees with high internal opportunities, but is less effective for employees with stronger opportunities for outside advancement. The study provides the first systematic evidence that the mobility of knowledge workers is shaped not only by state laws on non-compete agreements, but also by firm-specific reputations built through patent enforcement.
The Genomics Revolution
Date: Thursday, November 15, 2012
Speaker: David Heckerman, Senior Director, eScience Research Group, Microsoft Research
A little over a decade ago, the first human genome was sequenced at a cost of about one-hundred million dollars. Today, it costs well under ten thousand dollars. In fact, the cost is dropping much more quickly than the rate of Moore’s law. As a result, genomics data is becoming widely available and widely used. I will discuss elements of this genomics revolution relevant to Management Science and Engineering, including probabilistic modeling for the identification of genetic causes of disease. I will also touch on legal and ethical issues surrounding this revolution.
On Stochastic Insurance and Reinsurance Risk Networks
Date: Thursday, November 8, 2012
Speaker: Jose Blanchet, Associate Professor of Engineering, Industrial Engineering & Operations Research, Columbia University
In the last few years, substantial interest has been given to the study of systemic risk. In this talk we describe a class of models for systemic risk analysis in the setting of insurance and reinsurance participants. The models are constructed with the aim of capturing features such as cascading effects at the time of default due to counter-party risk and contagion. We also impose a probabilistic structure that allows us to rigorously study risk analysis questions using the theory of combinatorial optimization. In the end, we are interested in answering questions such as: a) What group of companies are the most relevant from a systemic risk standpoint? a) How do we quantify the role of reinsurance companies in systemic risk? c) How do we to understand and quantify the role of a regulator and associated capital requirements for systemic risk mitigation?/p>
A General Framework for Systemic Risk
Date: Tuesday, October 23, 2012
Speaker: Ciamac Moallemi, Associate Professor in the Decision, Risk, & Operations Division of the Graduate School of Business at Columbia University.
Systemic risk [SR] refers to the risk of collapse of an entire complex system, as a result of the actions taken by the individual component entities or agents that comprise the system. SR is an issue of great concern in modern financial markets as well as, more broadly, in the management of complex business and engineering systems. We propose an axiomatic framework for the measurement and management of SR based on the simultaneous analysis of outcomes across agents in the system and over scenarios of nature. Our framework defines a broad class of SR measures that accommodate a rich set of regulatory preferences. This general class of SR measures captures many specific measures of SR that have recently been proposed as special cases, and highlights their implicit assumptions. Moreover, the SR measures that satisfy our conditions yield decentralized decompositions, i.e., the SR can be decomposed into risk due to individual agents. Furthermore, one can associate a shadow price for SR to each agent that correctly accounts for the externalities of the agent’s individual decision-making on the entire system. This is joint work with Chen Chen and Garud Iyengar
Content Referral on the Internet
Date: Thursday, October 11, 2012
Speaker: Assaf Zeevi, Henry Kravis Professor at the Graduate School of Business, Columbia University
Search engines have been a transformative force, shaping the way we seek content and navigate the world wide web. They have also provided one of the most successful and robust revenue models on the Internet in the form of sponsored search. Recent trends in online user behavior exhibit interesting new phenomena, one of which is the increasing prevalence of "wandering'' type patterns; seeking content, yet in a less targeted manner. What are these users looking for? What current content is potentially attractive? And finally, how does one match the two in real time? This talk will explore some of these questions, with the intent of illustrating the use of data to inform modeling, and some of the analytical challenges that ensue.
Spring 2102 Lectures
Sparse Machine Learning for Large Text Corpora
Date: Tuesday, May 15
Speaker: Laurent El Ghaoui, Professor of EECS and IEOR at UC Berkeley
Sparse machine learning has recently emerged as powerful tool to obtain models of high-dimensional data with high degree of interpretability, at low computational cost. These methods can be extremely useful for understanding large collections of text documents, without requiring user expertise in machine learning. Our approach relies on three main ingredients: (a) multi-document text summarization and (b) comparative summarization of two corpora, both using sparse regression or classification; (c) sparse principal components and sparse graphical models for unsupervised analysis and visualization of large text corpora. In this talk I will describe some theoretical and algorithmic challenges related to these approaches, as well as experiments pertaining to real-life text databases, from scientific literature (PubMed), news and internal industry reports (commercial pilots reports).
Adventures in Policy Modeling!
Date: Tuesday, April 17
Speaker: Ed Kaplan, William N. and Marie A. Beach Professor of Mgmt Sciences, ChE & EnvE Engineering & Public Health, Yale University
Policy Modeling refers to the application of operations research, statistics, and other quantitative methods to model policy problems. Recognizing that analyses of all sorts often exhibit diminishing returns in insight to effort, the hope is to capture key features of various policy issues with relatively simple “first-strike” models. Problem selection and formulation thus compete with the mathematics of solution methods in determining successful applications: where do good problems come from? how can analysts tell if a particular issue is worth pursuing? In addressing these questions, I will review some personal adventures in policy modeling selected from public housing, HIV/AIDS prevention, bioterror preparedness, suicide bombings and counterterrorism, in vitro fertilization, predicting presidential elections, and March Madness.
Date: Thursday, April 12
Speaker: Rakesh Sarin, Paine Professor of Management, UCLA Anderson School
We develop and apply a unique and novel application of analytics and Decision Analysis to the study of happiness. Our results should be useful to individuals seeking to become happier and to organizations that wish to improve customer and employee satisfaction and productivity. Our model begins with the fundamental equation: HAPPINESS = REALITY – EXPECTATIONS. Following this, we propose a set of six laws that modify the fundamental equation, making it more precise and applicable to a wide range of situations and choices.
Last modified Monday, 14-Apr-2014 13:33:02 PDT