Desheng Dash Wu

Optimal Option Ordering and Pricing Decisions With Capital Constraint and Default Risk

This paper examines a single-period supply chain system consisting of a manufacturer selling an option contract to a capital-constrained retailer who faces stochastic demand. Option contract can transfer partial risk from the retailer to the manufacturer. This paper extends the existing literature of game-theoretic and credit financing models by explicitly incorporating retailers default risk into the pure option ordering and pricing decision problems. Stackelberg equilibriums under different scenarios, i.e., capital sufficient, capital constraint without credit, bank credit (BC), and trade credit (TC), are derived. The analytic model demonstrates that, under TC, the retailer can behave with and without bankruptcy risk, which depends on the retailers initial capital. Both the mathematical model and computational experiments reveal that the decisions and profits under BC are independent of retailers default risk, whereas default risk has a significant impact on the decisions and profits under TC. It shows that TC contract can partially coordinate the supply chain.

Supply Chain Loss Averse Newsboy Model With Capital Constraint

The financing of supply chains involves decisions by supply chain members as well as by lending institutions. The optimality of lending decisions in this environment depends on the loss aversion on the part of supply chain members as well as the availability of capital. The purpose of this paper is to understand the impact of capital constraint and loss aversion on operational decisions in supply chains. Traditional models have the bank external to the supply chain, with the banks interest rate exogenous. This research concerns a capital-constrained supply chain with the manufacturer selling to a loss averse newsvendor-like retailer, and a bank financing both the manufacturer and the retailer. The existence of supply chain finance equilibrium is proven by the use of Stackelberg game analysis. The best pricing and ordering decisions of both manufacturer and retailer are determined, and results demonstrate how these key decisions are influenced by their initial capital and the banks financial decisions. For instance, the optimal order quantity increases or decreases with initial capital, and it is interesting that bankruptcy protection encourages a cash-constrained retailer to adopt an aggressive ordering strategy. Moreover, it is shown that the retailers loss aversion has a significant impact on the capital constraint problem. With an increase in loss aversion, the required initial working capital decreases. Loss aversion can even change the retailers situation from one of capital constraint to one of capital sufficiency. An extension with double orders is given for comparison. Numerical examples are given to demonstrate the impact of initial capital and loss aversion on the optimal decisions and some other managerial insights are discussed.

Data Mining Models and Enterprise Risk Management

The advent of big data has led to an environment where billions of records are possible. Data mining is demonstrated on a financial risk set of data using R (Rattle) computations for the basic classification algorithms in data mining. We have not demonstrated that scope by any means, but have demonstrated small-scale application of the basic algorithms. The intent is to make data mining less of a black-box exercise, thus hopefully enabling users to be more intelligent in their application of data mining.

Value at Risk Models

Value at risk (VaR) is one of the most widely used models in risk management. It is based on probability and statistics. VaR can be characterized as a maximum expected loss, given some time horizon and within a given confidence interval. Its utility is in providing a measure of risk that illustrates the risk inherent in a portfolio with multiple risk factors, such as portfolios held by large banks, which are diversified across many risk factors and product types. VaR is used to estimate the boundaries of risk for a portfolio over a given time period, for an assumed probability distribution of market performance. The purpose is to diagnose risk exposure.

Environmental Damage and Risk Assessment

The problem of environmental damage and risk assessment has grown to be recognized as critically important, reflecting the emphasis of governments and political bodies on the urgency of need to control environmental degradation. This chapter reviews a number of approaches that have been applied to support decision making relative to project impact on the environment. The traditional approach has been to apply cost-benefit analysis, which has long been recognized to have issues. Most of the variant techniques discussed in this chapter are modifications of CBA in various ways. Contingent valuation focuses on integrating citizen input, accomplished through surveys. Other techniques focus on more accurate inputs of value tradeoffs. Conjoint analysis is a means to more accurately obtain such tradeoffs, but at a high cost of subject input. Habitat equivalency analysis modifies the analysis by viewing environmental damage in terms of natural resource service loss. Compensatory restoration assessed reflects actions to compensate for interim losses. Focus is thus on cost of actual restoration. Rather than abstract estimates of the monetary value of injured resources, the focus is on actual cost of restoration to baseline.

Sustainability and Enterprise Risk Management

The challenge of environmental sustainability is important not only as a moral imperative, but also a managerial responsibility to operate profitably. Environmental sustainability has become a critical factor in business, as the threats to environmental degradation from carbon emissions, chemical pollution, and other sources has repeatedly created liability for firms that don’t consider the environment, as well as regulatory attention. Legislators and journalists provide intensive oversight to operations of any organization. There are many cases of multi-billion dollar corporations brought to or near to bankruptcy by responsibilities for things like asbestos, chemical spills, and oil spills.

Chance Constrained Models

Chance constrained programming was developed as a means of describing constraints in mathematical programming models in the form of probability levels of attainment. Consideration of chance constraints allows decision makers to consider mathematical programming objectives in terms of the probability of their attainment. If α is a predetermined confidence level desired by a decision maker, the implication is that a constraint will be violated at most (1–α) of all possible cases.

Balanced Scorecards to Measure Enterprise Risk Performance

Balanced scorecards are one of a number of quantitative tools available to support risk planning. A number of applications in production planning and control performance measurement are reviewed. Various forms of scorecards, e.g., company-configured scorecards and/or strategic scorecards, have been suggested to build into the business decision support system or expert system in order to monitor the performance of the enterprise in the strategic decision analysis. This chapter demonstrates the value of small business scorecards with a case from a bank operation.

Earthquake Disaster Response in China

The magnitude of the 2008 Sichuan earthquake and its ensuing disaster bring home the power of nature, and the challenge all mankind faces in preparing for and mitigating such events. There are all kinds of disasters that threaten us, some natural (floods, hurricanes, tsunamis in addition to earthquakes); others artifacts of human inability to appropriately manage its affairs (war, terrorism)…

Simulation Risk Modeling

The following sections are included:SimulationThe Simulation ProcessFinancial Simulation ExamplesCash management simulationVaR modelsConclusions