2016 INFORMS Annual Meeting Program

TA42

INFORMS Nashville – 2016

2 - On The Optimal Timing Of Meld Score Updates In The Liver Transplantation System Sepehr Nemati, Ivey School of Business, 1255 Western Road,

3 - Limit Theorems For Hawkes Processes With A Large Initial Intensity Xuefeng Gao, The Chinese University of Hong Kong, xfgao@se.cuhk.edu.hk

Ivey Business School, London, ON, N6G 0N1, Canada, sproon@ivey.uwo.ca, Zeynep Icten, Lisa N Maillart, Andrew J Schaefer, Mark Roberts

Hawkes process is a class of simple point processes that is self-exciting and has clustering effect. The intensity of this point process depends on its entire past history. It has wide applications in finance, neuroscience, social networks, criminology, seismology, and many other fields. In this paper, we study the linear Hawkes process with an exponential kernel in the asymptotic regime where the initial intensity of the Hawkes process is large. We derive limit theorems for this asymptotic regime as well as the regime when both the initial intensity and the time are large. The limit theorems could be useful for approximating the transient behavior of Hawkes processes. 4 - Testing The Capital Asset Pricing Model Under Economic Regime Shifts Yonggan Zhao, Professor, Dalhousie University, 6100 University Avenue, Suite 2010, Halifax, NS, B3H 4R2, Canada, yonggan.zhao@dal.ca We present a dynamic version of the Capital Asset Pricing Model (CAPM) with economic regime shifts. Assuming the equilibrium security returns are characterized by economic indicators, we test the hypotheses that risk premiums on financial securities are asymmetric across economic regimes with positive risk premium in the expansion regimes and negative risk premium in the contraction regimes. Using a sector rotation investment strategy, the superiority of the dynamic CAPM to the traditional CAPM in predicting stock returns is shown. TA42 207D-MCC Choice Models and Assortment Optimization Sponsored: Revenue Management & Pricing Sponsored Session Chair: Sumit Kunnumkal, Indian School of Business, Hyderabad, India, sumit_kunnumkal@isb.edu 1 - Assortment, Pricing And Market Expansion Ruxian Wang, Johns Hopkins Carey Business School, Baltimore, MD, 21202, United States, ruxian.wang@jhu.edu We incorporate market expansion into consumer choice models and investigate the revenue management problems. We characterize the structure of the optimal policies for the problems under the consumer choice models with various market expansion effects, and develop efficient algorithms. 2 - Assortment Planning Decision In Two-sided Market Ying Cao, University of Texas at Dallas, Ying.Cao@utdallas.edu, Dorothee Honhon, Sridhar Seshadri We consider a firm which makes product assortment decisions when facing a two- sided market, which means it receives revenues from two distinct groups. We obtain structural properties of the optimal assortment and theoretical bounds on the performance of heuristic policies, showing the value of considering both sides of the market. 3 - A Near-optimal Exploration-exploitation Approach For Assortment Selection Vashist Avadhanula, Columbia University, New York, NY, 10027, United States, va2297@columbia.edu, Shipra Agrawal, Vineet Goyal, Assaf Zeevi We consider a dynamic assortment optimization problem where customers choose according to an unknown MNL choice model. In each period, we offer an assortment of at most K products out of N and observe the customer’s choice to learn the model parameters. We present an exploration-exploitation policy that achieves a near-optimal worst case regret of $\tilde {O}(\sqrt{NT})$. Our policy is based on the principle of optimism under uncertainty and does not require any separability assumption on the parameters. We also present a nearly matching lower bound of $\Omega(\sqrt{NT/K})$ for this problem. 4 - New Bounds For Assortment Optimization Under The Nested Logit Model Sumit Kunnumkal, Indian School of Business, sumit_kunnumkal@isb.edu We consider the assortment optimization problem under the nested logit choice model. We establish new bounds on the quality of revenue ordered assortments.

Patients on the waiting list for liver transplants have opportunities to game the system by concealing changes in their health status. We formulate a model that determines, as a function of the last reported health status, current health status, days until the next required update and the quality of the current liver offer, whether the patient should do nothing, report her current health status, or accept the current liver offer (if any) to maximize expected lifetime. We analyze the degree to which a patient can benefit from the flexibility inherent to the current reporting requirements. 3 - Operational And Financial Decisions Within Proportional Investment Cooperatives Xiaoyan Qian, PhD Student, University of Auckland, 12 Grafton Road, Auckland, 1010, New Zealand, x.qian@auckland.ac.nz, Tava Olsen In a proportional investment co-op, operational and financial decisions are inseparable because members’ capital investment is required to be in proportion to their economic transactions with the co-op. In agriculture where yield and market are uncertain, we propose a Markov decision process wherein the decisions of processing quantity interact with the financial decisions of retained earnings and short term loans. The results include: (1) characterization of the value function and the optimal policy; (2) explicit expressions for the deterministic-yield dynamic program; and (3) identification of financial risks. Keywords: cooperative; finance and operations; coordination. 4 - Easy Affine Markov Decision Processes: Applications And Algorithms Jie Ning, Case Western Reserve University, jie.ning@case.edu, Matthew J Sobel Affine Markov decision processes (MDPs) with continuous state and action vectors and decomposable constraints on actions have unique features that free them from the curse of dimensionality. Exploiting the properties of affine MDPs (a companion paper presented in another session), we present algorithms that efficiently compute an optimal policy and the value function. We show that affine MDPs are applicable in a variety of decision-making contexts such as fishery management and advertising, and that the optimal policies generate qualitative insights. Chair: Xuefeng Gao, The Chinese University of Hong Kong, William MWM Engineering Building, Shatin, NA, Hong Kong, xfgao@se.cuhk.edu.hk 1 - A Primal-dual Iterative Method For Stochastic Dynamic Programming And Its Applications Nan Chen, Chinese University of Hong Kong, nchen@se.cuhk.edu.hk Due to the curse of dimensionality, people often rely on computationally tractable, but suboptimal, heustric policies to solve stochastic dynamic programs (SDP). Our work develops a recursive approach from the technique of information relaxation to obtain a sequence of confidence intervals for SDP optimal value. The width of the confidence interval can be used to measure the quality of currently used heuristics. We also show the resulting intervals converge in a finite number of iterations to the true value. Thereby our approach presents a systematic way to improve the quality of control polices. Two applications in optimal trading execution and network revenue management are discussed. 2 - Operational Risk Management: Coordinating Capital Investment And Firm Growth Lingjiong Zhu, Florida State University, zhu@math.fsu.edu We consider a jump-diffusion model to analyze the impact on a firm’s value of small shocks caused by market risk events and large shocks caused by operational risk events. We consider the investments in the infrastructure of a firm that aims at mitigating the impact of operational risk events through changes in the stochastic nature of the large shocks. We study the investment strategies in two settings: the maximization the firm’s value over a fixed investment horizon and the minimization of the ruin probability over an infinite horizon. This is based on the joint work with Yuqian Xu and Mike Pinedo. TA41 207C-MCC Quantitative Methods in Finance VI Sponsored: Financial Services Sponsored Session

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