2015 Informs Annual Meeting

MC07

INFORMS Philadelphia – 2015

3 - Influence Maximization Revisited Paramveer Dhillon, MIT Sloan School of Management, 77 Massachusetts Avenue, Cambridge, MA, 02139, United States of America, dhillon@mit.edu, Sinan Aral Most research on influence maximization has focused on a single task: to devise algorithms with better approximation guarantees for the NP-Hard discrete optimization problem. The influence models over which the optimization operates, however, remain simplistic and disconnected from empirical evidence on influence in real networks. We propose extensions to existing models of influence propagation that incorporate the most recent empirical evidence and study the implications of these extensions. 4 - Is Exercise Contagious? Evidence from a Global Natural Experiment Christos Nicolaides, Postdoctoral Fellow, MIT Sloan School of Management, 100 Main St, E62-489, Cambridge, MA, 02142, United States of America, chrisnic@mit.edu, Sinan Aral Health-related behaviors, such as fitness habits, cluster amongst connected peers in social networks. Clustering of behaviors is in part attributable to causal social influence but can also arise through alternate means like homophily of preferences. Using fined grain individual running data from Nike+ we devise a natural experiment to quantify social contagion, identify influential members and groups and determine under which conditions influence is the dominant factor in behavior clustering.

4 - Accounting for Estimation Risk when Pricing under Adverse Selection Richard Neuberg, Columbia University, 1255 Amsterdam Avenue,

Dept of Statistics, 10th Floor, New York, NY, 10027, United States of America, rn2325@columbia.edu

Financial product prices often depend on unknown parameters. Their estimation introduces the risk that a better informed counterparty may strategically pick mispriced products. We discuss how overall estimation risk can be minimized by selecting a probability model of appropriate complexity. Such a model has small bias, which allows measuring product-specific estimation risk. We illustrate how to determine a premium for estimation risk, using a simple example from pricing regime credit scoring. 5 - Combined Estimation-Optimization (CEO) Approach for High Dimensional Portfolio Selection Chi Seng Pun, PhD Candidate, The Chinese University of Hong Kong, Department of Statistics, Lady Shaw Building, Shatin, N.T., Hong Kong - PRC, cspun@link.cuhk.edu.hk We propose a combined estimation-optimization (CEO) approach that directly estimates the optimal trading strategy (optimal control), instead of separating the estimation and optimization procedures. This paper investigates a constrained $\ell_1$-minimization for estimating the optimal control and applies it to the mean-variance portfolio (MVP) problems under static and dynamic settings when the number of assets (p) is larger than the number of observation times (n). We prove that the classical sample-based MVP strategy makes the probability that the optimal portfolio will outperform the bank account tend to 50% for p>>n and a large n. The CEO approach, however, converges to the true optimal solution. In addition, the CEO scheme automatically filters out unfavorable stocks based on historical data, and works for dynamic portfolio problems and non-Gaussian distributions. Simulations validate the theory and the behavior of the proposed approach. Empirical studies show that the CEO-based portfolios outperform the equally-weighted portfolio, the MVP with shrinkage estimators and other competitive approaches. MC07 07-Room 307, Marriott Modeling and Quantification of Risk Cluster: Risk Management Invited Session Chair: Patrick Cheridito, Princeton University, ORFE, Princeton, NJ, United States of America, dito@princeton.edu 1 - Assessing Financial Model Risk Pauline Barrieu, Professor, London School of Economics, Statistics Department, Houghton Street, London, WC2A2AE, United Kingdom, P.M.Barrieu@lse.ac.uk, Giacomo Scandolo Model risk has a huge impact on any risk measurement procedure and its quantification is therefore a crucial step. In this paper, we introduce three quantitative measures of model risk when choosing a particular reference model within a given class: the absolute measure of model risk, the relative measure of model risk and the local measure of model risk. Each of the measures has a specific purpose and so allows for flexibility. 2 - Multivariate Shortfall Risk and Monetary Risk Allocation Samuel Drapeau, Shanghai Jiao Tong University, 211 West Huaihai Road, Shanghai, 200030, China, samuel.drapeau@gmail.com, Stephane Crepey, Yannick Armenti, Antonis Papapantoleon We present a measure designed to address the global and intrinsic risk of interconnected system (banks, CCP...). The goal is twofold: first, provide the total amount of liquidity to be reserved to overcome financial stress situations. Second, address its allocation to each member in function of the systemic risk they put on the system. Finally, we present how these high dimensional computations can be solved in an efficient manner using Fourier methods. 3 - Variable Annuities with Guaranteed Withdrawal Benefits Patrick Cheridito, Princeton University, ORFE, Princeton, NJ, United States of America, dito@princeton.edu Variable annuities with withdrawal benefits have become popular over the last couple of years. Their cost to the issuer not only depends on market conditions but also on policyholder behavior. In this talk we discuss a contract whose withdrawal guarantees are based on the running maximum of the account value. The optimal withdrawal strategy is derived, and the cost of the contract to the issuer is determined.

MC06 06-Room 306, Marriott INFORMS Section on Finance Student Paper Competition Sponsor: Financial Services Sponsored Session Chair: Jim Bander, Toyota Financial, Chandler, AZ, United States of America, jim.bander@gmail.com 1 - Revisiting Eisenberg - Noe: A Dual Perspective

Deung-geon Ahn, KAIST, #2111, E2-2, 291 Daehak-ro, Yuseong- gu, Daejeon, Korea, Republic of, deunggeon.ahn@kaist.ac.kr, Kyoung-kuk Kim In this paper, we consider the Eisenberg-Noe framework for systemic risk with random shocks. Using duality, we characterize the amount of shock amplification due to the network structure and find the region for the shock vector that makes a specific bank default. These results enable us to improve some of the existing results of the network effect on systemic risk. More importantly, we propose efficient simulation schemes for the systemic risk measurement based on the characterization. 2 - A Partitioning Algorithm for MDPs and its Application to Limit Order Books with Stochastic Market Depth Ningyuan Chen, Columbia University, S. W. Mudd 321, 500 W 120th Street, New York, NY, 10027, United States of America, nc2462@columbia.edu Linear-quadratic control plays a central role in control theory, but its analytical solution, the so-called linear-quadratic regulator, fails in the presence of constraints. We consider a class of Markov decision processes (MDPs), with linear inequality constraints, non-convex quadratic cost, and linear state dynamics, governed by a Markov chain. By the proposed partitioning algorithm, we find the explicit solution to this class of MDPs: The value function and the optimal policy have analytical quadratic and linear forms, respectively, subject to a linear partition of the state space. The algorithm is applied to two applications. In the main application, we present a model for limit order books with stochastic market depth to study the optimal order execution problem. As a feature of our model, stochastic market depth is consistent with empirical studies and necessary to accommodate various order activities, such as limit order submission at and outside the best quotes and order cancellation, which may account for a large proportion of limit order activities. As a result, the optimal order execution policy is also stochastic and adapted to the random changes of market depth. 3 - An Optimization View of Financial Systemic Risk Modeling: The Network Effect and the Market Liquidity Effect Xin Liu, Doctoral Student, The Chinese University of Hong Kong, 609, William Mong Engineering Building, Hong Kong, Hong Kong - PRC, liuxin@se.cuhk.edu.hk

Abstract not available at this time.

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