Informs Annual Meeting 2017

TB04

INFORMS Houston – 2017

TB03A Grand Ballroom A Financial Engineering in Applied Probability I Sponsored: Applied Probability Sponsored Session Chair: Tomoyuki Ichiba, University of California-Santa Barbara, Santa Barbara, CA, 93106, United States, ichiba@pstat.ucsb.edu 1 - Systemic Risk in Financial Markets Andrey Sarantsev, University of California, Santa Barbara, CA, United States, sarantsev@pstat.ucsb.edu We study behavior of utility-maximizing private banks borrowing from the central bank and investing in risky assets. The central bank is risk-averse, and sets the interest rate to control the amount of risk in the system. 2 - Option Pricing with Delayed Information Tomoyuki Ichiba, University of California-Santa Barbara, South Hall 5607A, Santa Barbara, CA, 93106, United States, ichiba@pstat.ucsb.edu, Mostafa Mousavi We study the effects of delayed information on option pricing. First, we discuss absence of arbitrage and super replication problem with delayed information in discrete time, binomial-tree models. For contingent claims with convex payoff we present a closed-form formula. Then we also consider continuous time models as the limit of binomial tree models when the time steps and delay steps go to zero. Finally, we explore how delayed information exagerates the volatility smile. 3 - Pricing Various Swaps Indranil SenGupta, North Dakota State University, Department of Mathematics, Fargo, ND, 58108-6050, United States, indranil.sengupta@ndsu.edu, William Wilson, William Nganje One of the major challenges in arbitrage free pricing of swap is to obtain an accurate pricing expression which can be used with good computational accuracy. In this presentation we obtain various approximate expressions for the pricing of volatility, variance and covariance swaps. We show that with the approximate formulas obtained from the Barndorff-Nielsen and Shephard model the error estimation in fitting the delivery price is much less than the existing models with comparable parameters. 4 - Pricing Spread Options using a Variance Gamma Copula Paradigm Kyle Meerscheidt, University of Houston, 4800 Calhoun Rd, Houston, TX, 77004, United States, kmeerscheidt@gmail.com, Edward Kao A spread option is a contingent claim whose value depends on the difference between the value of one asset and a constant multiple of the value of a second asset. This paper examines the valuation of a spread call option where the prices of the two assets are driven by a bivariate dependent Lévy process, with a two- dimensional Lévy copula describing the dependence structure. This method can be extended to other bivariate Lévy processes, including those whose marginal (component) processes are not of the same family. Data-Driven Operations Management Sponsored: Manufacturing & Service Oper Mgmt Sponsored Session Chair: Adam J Mersereau, University of North Carolina, Chapel Hill, NC, 27599-3490, United States, ajm@unc.edu Co-Chair: Gah-Yi Ban, London Business School, London, NW1 4SA, United Kingdom, gban@london.edu 1 - Data-driven Percentile Optimization for Multi-class Queueing Systems with Model Ambiguity: Theory and Application Austin Steven Bren, Arizona State University, 5151 Guadalupe Road, Apt 2061, Phoenix, AZ, 85044, United States, hasbren@gmail.com, Soroush Saghafian Multi-class queueing systems often experience ambiguity in the form of unknown parameters. To incorporate robustness in the control policies we apply a novel data-driven technique that can express different levels of robustness and utilize incoming data parameter learning. Optimal policies are found to be related to a closed-form priority-based policy, with connections traditional priority policies. We also apply our approach to a hospital Emergency Department (ED) using real- world data, and demonstrate the benefits of using our framework for improving current patient priority policies. TB03C Grand Ballroom C

2 - Pricing for Reusable Resources Yunjie Sun, Columbia University, 500 West 120th Street, 535 S.W. Mudd Building, New York, NY, 10027, United States, ys2888@columbia.edu, Omar Besbes, Adam Elmachtoub We consider price optimization of reusable resources. We develop results on structural properties of the optimal dynamic pricing policy as well as provably good heuristics for this class of problems. We also discuss initial implementation efforts of a large scale pricing project at an industry partner, Dassault Falcon Jet, for rotable spare parts. 3 - Learning Single-peaked Ranking Preferences from Choice Data Mohammed Ali Aouad, Massachusetts Institute of Technology, 856 Massachussetts Avenue, Flat 9, Cambridge, MA, 02139, United States, aaouad@mit.edu, Vivek Farias, Retsef Levi We develop an efficient estimation procedure to learn single-peaked preferences from assortment choice data. The latter choice model assumes that individual ranking preferences form a « peak » according to a central permutation over items. This structure is popular in social choice theory, assortment optimization and matrix completion. While searching over the space of permutations is generally hard, we show that rank aggregation (corresponding to the column generation step in the estimation problem) can be solved in polynomial time under the single-peaked structure. We showcase the relevance of the proposed methodology in experiments on real choice data, in several product categories. 4 - Dynamic Procurement of New Products with Covariate Information: the Residual Tree Method Adam J. Mersereau, University of North Carolina, Kenan-Flagler We study a practice-motivated problem of dynamically procuring a new, short life-cycle product under demand uncertainty. The firm does not know the demand for the new product but has data on demand histories and covariate information for similar products sold in the past. We propose a new data-driven combined forecasting and optimization algorithm called the residual tree method, and we analyze its performance via epi-convergence theory and computations. Numerical validations on realistic data confirm the tractability of the approach and the value of covariate information. Business School, CB 3490, Chapel Hill, NC, 27599-3490, United States, ajm@unc.edu, Gah-Yi Ban, Jeremie Gallien 320A Sustainable Operations and Policy Sponsored: Manufacturing & Service Oper Mgmt, Sustainable Operations Sponsored Session Chair: Krishnan S. Anand, University of Utah, Salt Lake City, UT, 84112, United States, anand@eccles.utah.edu 1 - Food Wastage in Commercial Kitchens Varun Karamshetty, INSEAD, Boulevard de Constance, Fontainebleau, 77300, France, Varun.Karamshetty@insead.edu, Elena Belavina Kitchens are wasting as much on food waste as they make in profits. With an estimated $1 trillion in food being wasted every year, food waste is not only a huge economic loss, but also has a major impact on our environment accounting for nearly 15% of global GHG emissions. We analyze proprietary data to understand key drivers of food wastage in commercial kitchens. We identify the controllable factors, and quantify the impact of each of them. We then propose changes in their operations that minimize wastage while maintaining their service quality and bottom-line. 2 - Are Hazardous Substance Rankings Effective? Basak Kalkanci, Georgia Institute of Technology, 3151 Stillhouse Creek Dr SE, Apt 25517, Atlanta, GA, 30339, United States, basak.kalkanci@scheller.gatech.edu, Wayne Fu, Ravi Subramanian We empirically investigate the relationship between changes in the relative assessed hazard levels of chemicals and emissions reductions (including the use of source reduction and end-of-pipe treatment) by facilities that use these chemicals. We also examine the moderating effects of operational leanness - an attribute that prior studies have found to be associated with better environmental performance - in the setting wherein the relative assessed hazard levels of chemicals change over time. TB04

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