We consider a cash management problem where a company with a given financial endowment and given future cash flows minimizes the Conditional Value at Risk of final wealth using a lower bound for the expected terminal ...
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We consider a cash management problem where a company with a given financial endowment and given future cash flows minimizes the Conditional Value at Risk of final wealth using a lower bound for the expected terminal wealth. We formulate the optimization problem as a multi-stage stochastic linear program (SLP). The company can choose between a riskless asset (cash), several default- and option-free bonds, and an equity investment, and rebalances the portfolio at every stage. The uncertainty faced by the company is reflected in the development of interest rates and equity returns. Our model has two new features compared to the existing literature, which uses no-arbitrage interest rate models for the scenario generation. First, we explicitly estimate a function for the market price of risk and change the underlying probability measure. Second, we simulate scenarios for equity returns with moment-matching by an extension of the interest rate scenario tree.
Multistage stochastic linear programming has many practical applications for problems whose current decisions have to be made under future uncertainty. There are a variety of methods for solving the deterministic equi...
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Multistage stochastic linear programming has many practical applications for problems whose current decisions have to be made under future uncertainty. There are a variety of methods for solving the deterministic equivalent forms of these dynamic problems, including the simplex and interior-point methods and nested Benders decomposition, which decomposes the original problem into a set of smaller linear programming problems and has recently been shown to be superior to the alternatives for large problems. The Benders subproblems can be visualised as being attached to the nodes of a tree which is formed from the realisations of the random data process determining the uncertainty in the problem. This paper describes a parallel implementation of the nested Benders algorithm which employs a farming technique to parallelize nodal subproblem solutions. Differing structures of the test problems cause differing levels of speed-up on a variety of multicomputing platforms: problems with few variables and constraints per node do not gain from this parallelisation. We therefore employ stage aggregation to such problems to improve their parallel solution efficiency by increasing the size of the nodes and therefore the time spent calculating relative to the time spent communicating between processors. A parallel version of a sequential importance sampling solution algorithm based on local expected value of perfect information (EVPI) is developed which is applicable to extremely large multistage stochastic linear programmes which either have too many data paths to solve directly or a continuous distribution of possible realisations. It utilises the parallel nested Benders algorithm and a parallel version of an algorithm designed to calculate the local EVPI values for the nodes of the tree and achieves near linear speed-up.
This article optimizes a finite population, dynamic, stochastic inventory model where future demand is endogenous to inventory policy. Specifically, satisfied customers are not only likely to remain with the firm, but...
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This article optimizes a finite population, dynamic, stochastic inventory model where future demand is endogenous to inventory policy. Specifically, satisfied customers are not only likely to remain with the firm, but may also refer new customers. In contrast, backorders and lost sales may cause disgruntled customers to defect and potentially cause them to dissuade new customers from doing business with the firm. Thus, inventory policy and customer demand are endogenous. Further, the model allows for the possibility that too many customer defections may lead to product market failure. The incorporation of these innovations into our model yields inventory policies that differ substantially from those reported in the literature, with the greatest differences occurring when the firm has low to medium market share.
This paper develops a dynamic theoretical model to assess the impact of asset insurance on poverty and the cost of social protection in developing countries. We analyze the model under two technological assumptions: a...
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This paper develops a dynamic theoretical model to assess the impact of asset insurance on poverty and the cost of social protection in developing countries. We analyze the model under two technological assumptions: a standard, globally concave production technology, and a fixed cost technology that creates a non-convex production set and admits the possibility of multiple equilibria and a poverty trap. Under both assumptions, the introduction of an asset insurance market reduces poverty and the costs of social protection. Under the non-convex production set, there is a strong public finance case for insurance premium subsidies that target poor and vulnerable households and bring them into the insurance market. While the challenges of making microinsurance markets work are multiple, this analysis suggests the potential gains to solving these challenges are substantial.
We propose a novel network interdiction model that reconciles many operational realities identified by military literature. Specifically, we conduct network interdiction within a dynamic network under partial informat...
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We propose a novel network interdiction model that reconciles many operational realities identified by military literature. Specifically, we conduct network interdiction within a dynamic network under partial information, using incomplete feedback and allowing two-sided adaptive play. Combining these aspects in an evolving game, we use optimization, simulation, and stochastic models to achieve a hybrid model. Modeling some currently underrepresented martial problems in this way makes it possible to highlight otherwise obscure relationships between policy and outcome, and to discover emergent effects, such as deterrence. As an example of this class of problems, we consider the struggle between a smuggler and interdictor. The smuggler seeks to maximize the amount of forces and materiel infiltrated from an origin to destination. The interdictor seeks to minimize this smuggler flow. Using two simple examples of an illicit- trafficking network, we demonstrate how to use these quantitative models within such an interdictor-smuggler context to (1) evaluate the value of seizures as a proxy for smuggled materiel, (2) assess the value of exploration, and (3) provide decision makers with practical ways to better allocate resources and increase effectiveness.
Personal finance is a challenging topic which can benefit from a scientific approach to individual financial planning. This paper presents an individual asset liability management (iALM) model for life cycle planning ...
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Personal finance is a challenging topic which can benefit from a scientific approach to individual financial planning. This paper presents an individual asset liability management (iALM) model for life cycle planning which uses the methodology of dynamicstochastic optimisation and incorporates ideas from both classical and behavioural finance. Its implementation is in the form of a decision support tool for use by financial advisers or wealth managers. The investment universe is given by a set of indices for major asset classes and their returns are simulated forward over the lifetime of a household. On the liability side the foreseen cash flows of incomes and outgoings are simulated and punctuated by life events such as illness and death. The household's utility function is constructed for each time period over a range of monetary values in terms of household financial goals and preferences. Taxes and pension savings are treated using the tax shielded saving accounts specific to a national jurisdiction in terms of constraints in the optimisation sub-models. The paper goes on to present an analysis of iALM model recommendations for a representative UK household, together with an evaluation of the sensitivity of the financial plan generated to changes in market environments such as the 2007-9 crisis. The promise of this new technology is to bring modern decision support tools to individual investors in order to facilitate custom designed consumption, savings and investment policies.
Asset and Liability Management(ALM) plays an important role as a risk management tool in Chinese commercial banks after China joined WTO. It is an integrated technique on the management of a bank's balance *** man...
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Asset and Liability Management(ALM) plays an important role as a risk management tool in Chinese commercial banks after China joined WTO. It is an integrated technique on the management of a bank's balance *** managing its assets and liabilities,a bank is confronted with many government restrictions and market *** paper describes a model using dynamic stochastic programming *** tree of random asset returns,cash flows and discounted rate is generated as the input of the stochastic *** programming is solved from three different perspectives and assessment indices indicating the value of information and the value of stochastic model are *** illustrative research of China Minsheng Bank is *** indicate that ALM generates superior decisions.
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