On asymptotic optimality of Merton's myopic portfolio strategies under time discretization

This paper studies the properties of discrete-time stochastic optimal control problems associated with portfolio selection. We investigate if optimal continuous-time strategies can be used effectively for a discrete-time market after a straightforward discretization. We found that Merton's strategy approximates the performance of the optimal strategy in a discrete-time model with sufficiently small time steps

Mean variance and goal achieving portfolio for discrete-time market with currently observable source of correlations

The paper studies optimal portfolio selection for discrete time market models in mean-variance and goal achieving setting. The optimal strategies are obtained for models with an observed process that causes serial correlations of price changes. The optimal strategies are found to be myopic for the goal-achieving problem and quasi-myopic for the mean variance portfolio

Measuring the Gains to Groundwater Management with Recursive Utility

Replaced with revised version of paper 07/20/05.Recursive Utility, Dynamic Games, Groundwater Policy, Resource /Energy Economics and Policy, D91, C73, Q25,

Retirement saving with contribution payments and labor income as a benchmark for investments.

In this paper we study the retirement saving problem from the point of view of a plan sponsor, who makes contribution payments for the future retirement of an employee. The plan sponsor considers the employee's labor income as investment-benchmark in order to ensure the continuation of consumption habits after retirement. We demonstrate that the demand for risky assets increases at low wealth levels due to the contribution payments. We quantify the demand for hedging against changes in wage growth and find that it is relatively small. We show that downside-risk measures increase risk-taking at both low and high levels of wealth

Effects of Inequality and Transmissivity in a Common Pool Aquifer - Theory, Experimental Evidence and Policy Implications

The early literature on common pool resources focused on the race for appropriation among users and its damaging effects on the resource stock and on the aggregate welfare of all users. The differential game framework was widely used to examine each user's actions under non-cooperative management and to assess the losses from deviating from an optimal resource management under cooperation. Interest has recently shifted toward the effects of characteristics such as heterogeneity among users and level of commonality in the resource on the use of common resources. This article is interested in combining both effects: I consider a dynamic model of a common pool aquifer with a finite transmissivity used by two farmers with dissimilar efficiencies. I unravel the players' behavior under different strategies and estimate their respective profits in order to evaluate the welfare effects of inequality and transmissivity. Solving for the aggregate profit of all players allowed me to revisit of a widespread result found in the common pool resource literature, which is that well enforced property rights are always associated with higher profitability; indeed, in the case of highly unequal players I reach a rather counterintuitive result as increasing transmissivity is proven to increase the overall profits. Such a result was never established in the literature at hand. However, on the distributional aspect, the model shows that the benefits of less efficient users always suffer from more transmissivity, even when the inequality is high enough to generate a raise in aggregate profits. For the validation of my theoretical results I carried out a series of experiments in the experimental laboratory at the Department of Agricultural and Resource Economics with volunteer subjects recruited from the University of Maryland. I used four experimental treatments. In the first two treatments the transmissivity is infinite; the players are highly differentiated in one treatment and identical in the other. The last two treatments are a replica of the earlier ones but with no transmissivity. The laboratory data were compared to the theoretical solution following four benchmark paths: the social optimum, the subgame perfect equilibrium, the semi-myopic, and the myopic. The results show that the decisions of a significant share of players follow the myopic path. All the theoretical findings were corroborated by the experimental results including the increasing effect of transmissivity in the presence of users highly unequal. In Chapter 5 on policy implications, I try to extend the analysis on the combined (or individual) effects of transmissivity and inequality on the aquifer use to the case when the possibility of communication between users, or the existence of a central agency, allows the emergence of alternative resource management modes. The first mode corresponds to the case of social optimum resource management; when users coordinate their actions to maximize the benefits to the community from the aquifer. The second mode of management corresponds to the case where, from a certain round, only one user, a priori the most effective, is allowed to use the resource, while the other user abandons extraction activities for the remaining duration of the game

Real-time Allocation Decisions in Multi-echelon Inventory Control

Inventory control is a crucial activity for many companies. Given the recent advances in information technology, there have never been greater opportunities for coordinated inventory control across supply chain facilities. But how do we design efficient control methods and policies that take advantage of the detailed information that is now becoming available? This doctoral thesis investigates these issues within the field of inventory control theory. The objective of the research is: To develop mathematical models and policies for efficient control and increased understanding of stochastic multi-echelon inventory systems, with a focus on allocation decisions and the use of real-time information. This thesis is based on five scientific papers which are preceded by a summarizing introduction. The papers address different types of inventory distribution systems, all consisting of a central stocking facility that supplies an arbitrary number of local stocking facilities (referred to as retailers). The retailers face stochastic end customer demand. The systems are characterized by the presence of real-time inventory information, including continuously updated information on the current inventory levels at different facilities and on the locations of outstanding orders. In Paper I and Paper II we derive and evaluate different decision rules for stock allocation (known as allocation policies) for a central warehouse which applies a time based shipment consolidation strategy. The allocation policy determines how the central warehouse should distribute its stock among different retailers in case of shortages. New allocation policies that utilize real-time information are compared to the commonly used First Come - First Served policy which requires less information. In Paper III we shift focus to the delivery policy at a central warehouse which supplies multiple retailers that order in batches. When the central warehouse cannot satisfy an entire retailer order immediately, the delivery policy determines if the order should be shipped in several parts or in its entirety when all items are available. We investigate the value of using a new delivery policy that uses real-time information on when replenishments will arrive at the central warehouse. The information is used to determine the best course of action for each order placed by the retailers. We also study how to allocate safety stocks to all facilities in the system given this new policy. In Paper IV and Paper V we consider a system where retailers may receive emergency shipments from a support warehouse in combination with regular replenishments from a central warehouse/outside supplier. We investigate how safety stocks should be allocated between the retailers and the support warehouse. Furthermore, we evaluate the benefits of tracking orders in real time and using this information in the decision whether or not to request an emergency shipment

Reducing costs of repairable spare parts supply systems via dynamic scheduling

We study a system consisting of one repair shop and one stockpoint, where spare parts of repairables are kept on stock to serve an installed base of systems. Part requests are met from stock if possible, and backordered otherwise. Our objective is to determine initial stock levels and a policy for scheduling repair jobs such that holding and backorder cost are minimized. We propose two dynamic scheduling rules, compare their performance with the static priority rule, and show that even when stock levels and static priorities have been optimized simultaneously, dynamic scheduling rules often reduce total cost by more than 10%