The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Robert E Hall - One of the best experts on this subject based on the ideXlab platform.
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the role of the growth of Risk Averse wealth in the decline of the safe real interest rate
National Bureau of Economic Research, 2016Co-Authors: Robert E HallAbstract:Over the past few decades, worldwide real interest rates have trended downward. The real interest rate describes the terms of trade between Risk-tolerant and Risk-Averse investors. Debt pays off equally across contingencies at a given future date, so debt is valuable to Risk-Averse investors to smooth consumption across those contingencies. In an equilibrium with trade between investors who differ in attitudes toward Risk, the Risk-tolerant investors borrow from the Risk-Averse ones, shifting the Risk to those whose preferences favor taking on Risk. Heterogeneity in Risk aversion takes two forms in the model of the paper: variation in coefficients of relative Risk aversion and variation in beliefs about the probabilities of seriously adverse outcomes. If the composition of wealth shifts into the hands of investors with higher coefficients of relative Risk aversion and investors who believe in higher probabilities of bad events, the real interest rate falls. The paper calculates likely magnitudes of the decline and presents evidence in favor of a shift in the composition of wealth toward the holdings of the more Risk-Averse. In particular, the United States absorbs large amounts of Risk by borrowing from more Risk-Averse countries, notably China, which thereby shed corresponding amounts of Risk.
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the role of the growth of Risk Averse wealth in the decline of the safe real interest rate
Social Science Research Network, 2016Co-Authors: Robert E HallAbstract:Over the past few decades, worldwide real interest rates have trended downward. The real interest rate describes the terms of trade between Risk-tolerant and Risk-Averse investors. Debt pays off equally across contingencies at a given future date, so debt is valuable to Risk-Averse investors to smooth consumption across those contingencies. In an equilibrium with trade between investors who differ in attitudes toward Risk, the Risk-tolerant investors will borrow from the Risk-Averse ones, shifting the Risk to those whose preferences favor taking on Risk. In the case where investors have preferences that are additively separable in future states and in time, attitudes toward Risk are heterogeneous among investors if they differ in the curvature of their utility kernels and differ in their beliefs about the probabilities of outcomes, especially adverse outcomes. If the composition of investors shifts toward those with higher curvature (higher coefficients of relative Risk aversion) and toward investors who believe in higher probabilities of bad events, the real interest rate falls. The paper calculates likely magnitudes of the decline and presents evidence in favor of a shift in the composition of investors toward the more Risk-Averse. The downward trend in real interest rates is a significant problem for monetary policy but is helpful to heavily indebted countries.Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.
Alexander Shapiro - One of the best experts on this subject based on the ideXlab platform.
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decomposability and time consistency of Risk Averse multistage programs
arXiv: Optimization and Control, 2018Co-Authors: Alexander Shapiro, Kerem UgurluAbstract:Two approaches to time consistency of Risk Averse multistage stochastic problems were discussed in the recent literature. In one approach certain properties of the cor-responding Risk measure are postulated which imply its decomposability. The other approach deals directly with conditional optimality of solutions of the considered problem. The aim of this paper is to discuss a relation between these two approaches.
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Risk neutral and Risk Averse approaches to multistage renewable investment planning under uncertainty
European Journal of Operational Research, 2016Co-Authors: Sergio Bruno, Alexander Shapiro, Shabbir Ahmed, Alexandre StreetAbstract:Abstract Strategies for investing in renewable energy projects present high Risks associated with generation and price volatility and dynamics. Existing approaches for determining optimal strategies are based on real options theory, that often simplify the uncertainty process, or on stochastic programming approaches, that simplify the dynamic aspects. In this paper, we bridge the gap between these approaches by developing a multistage stochastic programming approach that includes real options such as postponing, hedging with fixed (forward) contracts and combination with other sources. The proposed model is solved by a procedure based on the Stochastic Dual Dynamic Programming (SDDP) method. The framework is extended to the Risk Averse setting. A specific case study in investment in hydro and wind projects in the Brazilian market is used to illustrate that the investment strategies generated by the proposed approach are efficient.
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Risk neutral and Risk Averse stochastic dual dynamic programming method
European Journal of Operational Research, 2013Co-Authors: Alexander Shapiro, Wajdi Tekaya, Joari Paulo Da Costa, Murilo Pereira SoaresAbstract:In this paper we discuss Risk neutral and Risk Averse approaches to multistage (linear) stochastic programming problems based on the Stochastic Dual Dynamic Programming (SDDP) method. We give a general description of the algorithm and present computational studies related to planning of the Brazilian interconnected power system.
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minimax and Risk Averse multistage stochastic programming
European Journal of Operational Research, 2012Co-Authors: Alexander ShapiroAbstract:In this paper we study relations between the minimax, Risk Averse and nested formulations of multistage stochastic programming problems. In particular, we discuss conditions for time consistency of such formulations of stochastic problems. We also describe a connection between law invariant coherent Risk measures and the corresponding sets of probability measures in their dual representation. Finally, we discuss a minimax approach with moment constraints to the classical inventory model.
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on a time consistency concept in Risk Averse multistage stochastic programming
Operations Research Letters, 2009Co-Authors: Alexander ShapiroAbstract:We discuss time consistency of multistage Risk Averse stochastic programming problems. The concept of time consistency is approached from an optimization point of view. That is, at each state of the system optimality of a decision policy should not involve states which cannot happen in the future.
Houmin Yan - One of the best experts on this subject based on the ideXlab platform.
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channel bargaining with Risk Averse retailer
International Journal of Production Economics, 2012Co-Authors: Fangmei Liu, Houmin YanAbstract:Abstract We first consider a supply chain with one manufacturer and one retailer where there is only one product with stochastic demand. The retailer is Risk Averse with Conditional Value-at-Risk (CVaR) as her Risk measure and the manufacturer is a Risk-neutral agent. We model the problem as a Nash-bargaining problem where the manufacturer and the retailer negotiate about the wholesale price and order quantity. We show that there exists a Nash-bargaining equilibrium for the wholesale price and order quantity with equal and unequal bargaining power. We also find that even for the equal bargaining power between the two agents, the retailer's bargaining power for the supply chain profit increases as she becomes more Risk Averse. We then extend the model to the case where demand is endogenous and can be manipulated by setting the retail price. We show that there exists a Nash-bargaining equilibrium for the wholesale price, retail price and the order quantity under equal bargaining power.
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channel coordination with a Risk neutral supplier and a downside Risk Averse retailer
Production and Operations Management, 2009Co-Authors: Xianghua Gan, Suresh P Sethi, Houmin YanAbstract:We investigate how a supply chain involving a Risk-neutral supplier and a downside-Risk-Averse retailer can be coordinated with a supply contract. We show that the standard buy-back or revenue-sharing contracts may not coordinate such a channel. Using a definition of coordination of supply chains proposed earlier by the authors, we design a Risk-sharing contract that offers the desired downside protection to the retailer, provides respective reservation profits to the agents, and accomplishes channel coordination.
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coordination of supply chains with Risk Averse agents
Production and Operations Management, 2009Co-Authors: Xianghua Gan, Suresh Sethi, Houmin YanAbstract:The extant supply chain management literature has not addressed the issue of coordination in supply chains involving Risk-Averse agents. We take up this issue and begin with defining a coordinating contract as one that results in a Pareto-optimal solution acceptable to each agent. Our definition generalizes the standard one in the Risk-neutral case. We then develop coordinating contracts in three specific cases (1) the supplier is Risk neutral and the retailer maximizes his expected profit subject to a downside Risk constraint, (2) the supplier and the retailer each maximizes his own mean-variance trade-off, and (3) the supplier and the retailer each maximizes his own expected utility. Moreover, in case (3) we show that our contract yields the Nash Bargaining solution. In each case, we show how we can find the set of Pareto-optimal solutions, and then design a contract to achieve the solutions. We also exhibit a case in which we obtain Pareto-optimal sharing rules explicitly, and outline a procedure to obtain Pareto-optimal solutions.
Tsanming Choi - One of the best experts on this subject based on the ideXlab platform.
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optimal pricing in mass customization supply chains with Risk Averse agents and retail competition
Omega-international Journal of Management Science, 2019Co-Authors: Tsanming Choi, Bin Shen, Qi SunAbstract:Abstract We analytically investigate the optimal pricing decisions in a mass customization (MC) supply chain with one Risk Averse manufacturer and two Risk Averse competing retailers. The manufacturer is the Stackelberg leader, which offers a wholesale pricing contract to the retailers. After receiving the wholesale price, each retailer decides the retail selling price for the MC product simultaneously. We first prove the existence of a unique pricing equilibrium and then derive the optimal prices. After that, we focus our attention on exploring how the degree of Risk aversion of each supply chain agent affects the optimal prices as well as consumer welfare, supply chain profitability, and credit deposit under a competitive setting. We find that a more Risk Averse manufacturer will offer a lower wholesale price, which leads to lower retail selling prices offered to the market. For the retailers, if a retailer is more Risk Averse, it will make the manufacturer offer a higher wholesale price and it will set a lower retail selling price; however, whether the competing retailer will increase or decrease the retail selling price depends on the level of competition. We examine the impacts brought by the market demand uncertainties as well as the respective demand correlation. We conclude by revealing that in the sufficiently competitive market environment, consumers enjoying MC services are benefited more but the supply chain profitability may decrease more (depending on how Risk Averse the agents are) when (i) the manufacturer and retailers are more Risk Averse, (ii) demand uncertainties and the correlation between market demands are higher. We also find that the retailers need to pay more credit deposit if the manufacturer is more Risk Averse or the demand correlation is higher. Finally, we consider the MC product improvement scheme in the extended model and reveal that it is a Pareto improving optimal measure if the supply chain agents are not too Risk Averse and the increment in production cost is sufficiently small.
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impacts of retailer s Risk Averse behaviors on quick response fashion supply chain systems
Annals of Operations Research, 2018Co-Authors: Tsanming ChoiAbstract:Supply chain systems for fashion apparel products face a high level of Risk as the market demand is very volatile and unpredictable. In order to cope with demand volatility, the quick response system which aims to shorten replenishment lead time has been well-established. With a shortened lead time, retailers can postpone the ordering decision and improve their demand forecast by gathering updated market information. However, there is a limit for quick response in which the market demand forecast is never fully accurate and uncertainty can never be fully eliminated. Thus, to keep the level of Risk under control, retailers tend to possess a Risk-Averse behaviour in making their respective inventory decisions. In this paper, we explore the make-to-order quick response fashion supply chain with a Risk Averse retailer. We employ the mean-Risk framework to incorporate the retailer’s Risk Averse behavior into the optimization model. With the focal point on uncovering the impacts brought by the retailer’s Risk Averse behavior to the quick response fashion supply chain system, we analytically derive important theoretical insights regarding the retailer’s optimal decisions, the implied inventory service levels, the values of quick response, and the contractual arrangements to attain Pareto improvement when the retailer is Risk Averse.
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game theoretic analysis of a multi period fashion supply chain with a Risk Averse retailer
International Journal of Inventory Research, 2013Co-Authors: Tsanming ChoiAbstract:This paper studies a single-manufacturer single-retailer multi-period fashion supply chain which sells a category of short-life highly fashionable products. The retailer is Risk Averse and the supply chain is led by the upstream manufacturer. This paper studies two commonly seen contracts in the fashion industry, namely the pure wholesale pricing contract (PWPC) and the markdown money contract (MDMC). The efficient region for the Risk Averse retailer’s optimal ordering quantity for each contract is found and this region will: 1) become smaller if the wholesale price increases (under both PWPC and MDMC); 2) get larger when the markdown money increases (under MDMC). We introduce the concept of ‘Risk Averse quantity reduction level’ (RAQRL). Our analytical findings further indicate that: 1) under scenario one in which the manufacturer’s goal is to maximise the supply chain’s expected profit, the appropriately set MDMC can coordinate the supply chain whereas PWPC cannot; 2) under scenario two in which the manufacturer’s goal is to maximise the supply chain’s expected profit while ensuring the variance of profit is within a limit, both PWPC and MDMC can coordinate the supply chain.
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the coordination of fashion supply chains with a Risk Averse supplier under the markdown money policy
Systems Man and Cybernetics, 2013Co-Authors: Bin Shen, Tsanming Choi, Yulan WangAbstract:Motivated by the popular markdown money policy (MMP) in the textiles and clothing (TC) industry, in this paper, we explore how this policy performs in a two-stage TC/fashion supply chain with an upstream Risk-Averse manufacturer (supplier) and a downstream Risk-neutral retailer. Specifically, we investigate both the optimal decisions of the Risk-Averse supplier with respect to the MMP contract parameters and the optimal ordering decision of the Risk-neutral retailer so that the whole supply chain can be coordinated (i.e., optimized). We then conduct a numerical study with the real data from two companies to explore the performance of the optimal MMP proposed in our paper. Important insights and specific implications to the industry practitioners are discussed.
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service commitment strategy and pricing decisions in retail supply chains with Risk Averse players
Service Science archive, 2012Co-Authors: Tiaojun Xiao, Tsanming Choi, Danqin Yang, T C E ChengAbstract:We study the service commitment strategy and pricing decisions in a single-supplier single-retailer supply chain where all the players (and consumers) are Risk Averse. Motivated by various industrial practices, we explore the case where the retailer determines whether to provide a service guarantee (SG) or to provide no service guarantee (NSG). The main incentive for using SG is to reduce the service-level Risk to consumers. We derive the range of the supplier's degree of Risk aversion and the range of the consumer's sensitivity (or attitude) to service reliability over which the retailer chooses SG. We find that (i) the retailer's motivation to use SG increases with the consumer's product quality perception, (ii) the retailer's motivation to use SG decreases with the retailer's degree of Risk aversion but increases with both the consumer's degree of Risk aversion and the retailer's service investment efficiency, and (iii) the unit wholesale price under NSG is lower than that under SG if and only if the consumer's service-level sensitivity is sufficiently small. In addition, we illustrate that the endogenization of unit wholesale price raises the retailer's motivation to use SG if the consumer is sufficiently Risk Averse; otherwise, it may decrease this motivation. In the make-to-stock mode, we also find that a higher unit-holding cost weakens the retailer's motivation to use an availability guarantee.
John Canny - One of the best experts on this subject based on the ideXlab platform.
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Risk Averse robust adversarial reinforcement learning
International Conference on Robotics and Automation, 2019Co-Authors: Xinlei Pan, Daniel Seita, Yang Gao, John CannyAbstract:Deep reinforcement learning has recently made significant progress in solving computer games and robotic control tasks. A known problem, though, is that policies overfit to the training environment and may not avoid rare, catastrophic events such as automotive accidents. A classical technique for improving the robustness of reinforcement learning algorithms is to train on a set of randomized environments, but this approach only guards against common situations. Recently, robust adversarial reinforcement learning (RARL) was developed, which allows efficient applications of random and systematic perturbations by a trained adversary. A limitation of RARL is that only the expected control objective is optimized; there is no explicit modeling or optimization of Risk. Thus the agents do not consider the probability of catastrophic events (i.e., those inducing abnormally large negative reward), except through their effect on the expected objective. In this paper we introduce Risk-Averse robust adversarial reinforcement learning (RARARL), using a Risk-Averse protagonist and a Risk-seeking adversary. We test our approach on a self-driving vehicle controller. We use an ensemble of policy networks to model Risk as the variance of value functions. We show through experiments that a Risk-Averse agent is better equipped to handle a Risk-seeking adversary, and experiences substantially fewer crashes compared to agents trained without an adversary. Supplementary materials are available at https://sites.google.com/view/rararl.
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Risk Averse robust adversarial reinforcement learning
arXiv: Learning, 2019Co-Authors: Xinlei Pan, Daniel Seita, Yang Gao, John CannyAbstract:Deep reinforcement learning has recently made significant progress in solving computer games and robotic control tasks. A known problem, though, is that policies overfit to the training environment and may not avoid rare, catastrophic events such as automotive accidents. A classical technique for improving the robustness of reinforcement learning algorithms is to train on a set of randomized environments, but this approach only guards against common situations. Recently, robust adversarial reinforcement learning (RARL) was developed, which allows efficient applications of random and systematic perturbations by a trained adversary. A limitation of RARL is that only the expected control objective is optimized; there is no explicit modeling or optimization of Risk. Thus the agents do not consider the probability of catastrophic events (i.e., those inducing abnormally large negative reward), except through their effect on the expected objective. In this paper we introduce Risk-Averse robust adversarial reinforcement learning (RARARL), using a Risk-Averse protagonist and a Risk-seeking adversary. We test our approach on a self-driving vehicle controller. We use an ensemble of policy networks to model Risk as the variance of value functions. We show through experiments that a Risk-Averse agent is better equipped to handle a Risk-seeking adversary, and experiences substantially fewer crashes compared to agents trained without an adversary.
