A Test of the Cross-Sectional Robustness of the Arbitrage Pricing Model Using Foreign Exchange Rates*

This paper tests the cross-sectional robustness of the arbitrage pricing theory (APT) model using foreign exchange rate data to determine if the model is robust with respect to the various random samples and various factor analytic techniques. Factor scores are developed using various samples and factor analytic techniques to explain the returns for other samples and groupings. The APT model is found to be robust across samples and techniques.     

Incentive Problems With Unidimensional Hidden Characteristics: A Unified Approach

This paper develops a technique for studying incentive problems with unidimensional hidden characteristics in a way that is independent of whether the type set is finite, the type distribution has a continuous density, or the type distribution has both mass points and an atomless part. By this technique, the proposition that optimal incentive schemes induce no distortion “at the top” and downward distortions “below the top” is extended to arbitrary type distributions. However, mass points in the interior of the type set require pooling with adjacent higher types and, unless there are other complications, a discontinuous jump in the transition from adjacent lower types.     

经理声誉考虑、任务关联性和长期报酬激励的效果研究

为研究如何激励经理努力提高企业未来业绩,假设经理承担着两项任务:声誉激励其提高企业当前业绩,报酬激励其提高未来业绩;在多任务委托代理模型框架下,本文研究了声誉和长期报酬对经理提高未来业绩的激励效果.本文把企业业绩分为当期业绩和长期(即未来)业绩,并假设委托人目标是长期企业业绩最大化,而不是当期业绩最大化.同时本文对经理努力成本函数的假设有重大改进:给出了经理长期和短期努力的具体的成本函数形式,考虑了两种任务即努力间的关联性情况,假设长期努力和当期努力有不同的成本系数.目前相关模型的成本函数最多只能满足以上三个条件中的前两个.因此,运用本文多任务委托代理模型分析得出的结论更明确、直观,并且更符合实际.本文发现,经理不同努力的成本、两项任务之间的关联性对经理的努力行为选择有重要的影响,要激励经理提高企业长期业绩,应该减少经理长期努力的成本或者弱化当期激励.     

A Unified Approach to Hedging Interest Rate Risk with Financial Futures*

There is abundant literature on the use of financial futures to reduce interest rate risk. While many applications have been developed and evaluated in the literature, little has been done to provide a simple, mathematical model unifying the disparate types of hedges. The purpose of this paper is to provide such a unifying framework. Under idealized conditions, an equation is developed giving a perfect hedge solution for arbitrary choice of planning horizon, existing or planned cash market position, and asset/liability mix. The paper is pedagogic in nature.     

需求-汇率风险聚集下的汇率风险对冲与批发价激励

以一个由上游制造商和下游零售商组成的二级供应链为建模背景,在汇率风险和需求风险聚集(pooling)在下游零售商的情况下,分别建立了有无批发价激励情形下的两个动态博弈模型,获得了相应的均衡。通过分析相应的均衡,结果表明,(1)在两种情形下,零售商的汇率风险对冲行为具有稳定供应链生产行为的作用,但批发价激励能够提高零售商的汇率风险对冲比例和供应链节点企业间的交易价格;(2)与无批发价激励的情形相比,有批发价激励时的制造商利润较高,从而制造商有使用批发价激励零售商对冲汇率风险的动机;(3)在风险聚集下,"通过较低的批发价来激励零售商对冲汇率风险"这一策略能够实现供应链盈利水平与风险承担之间的权衡:需求和汇率风险增加均会使得供应链节点企业和供应链整体的盈利水平降低,同时也将降低风险聚集处的节点企业和供应链整体的利润方差。     

A Wavelet‐Based Approach to Preserve Privacy for Classification Mining*

Despite the commercial success of data mining, a major drawback has been acknowledged across academic, industry, and government sectors, namely, the issue of violating the privacy of individuals. We propose a data transformation method based on wavelets to disguise private data while preserving the original classification patterns. Wavelet transformations have been used extensively in signal processing for data reduction, multiresolution analysis, and removing noise from data. In our implementation, two commonly used wavelet transforms, the Haar and the Daub‐4 transforms, are tested for pattern and privacy preservation in classification mining tasks. Empirical results confirm that the Haar and the Daub‐4 transforms preserve the classification patterns and preserve the privacy for real valued data.     

Optimal Pricing of Two Successive‐Generation Products with Trade‐in Options under Uncertainty

To entice consumers to purchase both current and next generation products, many manufacturers and retailers offer trade‐in programs that allow buyers of the first generation product to trade‐in the product and purchase the new generation product at a lower price. By considering the interactions between “forward‐looking” consumers and a firm when a trade‐in program is offered, we analyze a two‐period dynamic game to determine the optimal prices of two successive‐generation products in equilibrium, and examine the conditions under which trade‐in programs are beneficial to the firm. Our model incorporates market heterogeneity (valuation of the first generation product varies among the consumer population), product uncertainty (the incremental value of the new product is uncertain before its introduction), and consumers' forward‐looking behavior (consumers take future product valuation and prices into consideration when making purchasing decisions). With the trade‐in option, we show that consumers are willing to pay a price that is higher than their valuations of the current product. Furthermore, trade‐in programs are more beneficial to the firm when: (i) the durability of the current product is high; (ii) the market heterogeneity is low; or (iii) the uncertainty level (or the expected incremental value) of the new product is high. Finally, when the incremental value of the new product is more uncertain, consumers are more willing to purchase the current product because of the “option” value of the trade‐in programs and thus trade‐in programs can be more beneficial to the firm in this case.     

A Game-Theoretic Approach to Quantity Discount Problems*

The traditional quantity discount problem is analyzed from the perspective of game theory, including both noncooperative and cooperative models. For the noncooperative case, the Stackelberg equilibrium is derived. For the cooperative case, the Pareto Optimality criteria are used to find a group of optimal strategies. Both scenarios are illustrated through an example which quantifies the benefits resulting from cooperation between the buyer and the seller for game-theoretic solutions using geometric programming.     

Discount Pricing Policies and the Coordination of Decentralized Distribution Systems*

A challenge of supply chain management is to align the objectives, and hence coordinate the activities, of independent supply chain members. In this study, we approach this problem in a simple way by extending traditional quantity discounts that are based solely on buyers' individual order size to discount policies that are based on both buyers' individual order size and their annual volume. We show that discount policies are able to achieve nearly optimal system profit and, hence, provide effective coordination, for a decentralized two‐echelon distribution system, whereby a supplier sells a product to a group of heterogeneous and independent retailers each facing a downward‐sloping demand curve of its retail price. When buyers are heterogeneous, a critical issue of coordination is to motivate different customers to increase their demand and lot size according to their potential so as to improve profits. We show that market heterogeneity presents an effective discriminating factor for the supplier to segment customers in the design of a coordination mechanism.     

Distribution of Spot and Forward Exchange Rates: Empirical Evidence and Investor Valuation of Skewness and Kurtosis*

This paper examines the statistical distribution of exchange rates for eight major currencies for the post-1973 floating rate period. The results show that spot rates, forward rates, and ex-post risk premia all exhibit significant, persistent, but varying deviations from normality, and that the risk premia in forward rates reflect investor preferences for skewness and investor aversion towards standard deviation and kurtosis. These results imply that foreign currency forecasting and hedging practices, mean-variance portfolio analysis, pricing of foreign currency options, and other research involving exchange rates should account for these significant deviations from normality.     

A Nonatomic‐Game Approach to Dynamic Pricing under Competition

We study a revenue management problem involving competing firms. We assume the presence of a continuum of infinitesimal firms where no individual firm has any discernable influence over the evolution of the overall market condition. Under this nonatomic‐game approach, the unanimous adoption of an equilibrium pricing policy by all firms will yield a market‐condition process that in turn will elicit the said policy as one of the best individual responses. For both deterministic‐ and stochastic‐demand cases, we show the existence of equilibrium pricing policies that exhibit well‐behaving monotone trends. Our computational study reveals many useful insights, including the fact that only a reasonable number of firms are needed for our approach to produce near‐rational pricing policies.     

A Newsvendor Problem with Revenue Sharing and Channel Coordination*

Increasingly manufacturers and retailers are implementing revenue‐sharing policies to coordinate distribution channels more effectively. This article considers a standard newsvendor problem in a single manufacturer–retailer channel and compares the expected profits that each party receives in a traditional ordering environment with those that can be achieved under a revenue‐sharing policy designed to completely eliminate double marginalization. It is shown that the retailer always benefits from the transition to revenue sharing by capturing a portion of the incremental channel profit generated by the complete elimination of double marginalization. A necessary demand‐distribution‐dependent condition is derived under which the transition to revenue sharing benefits the manufacturer as well. The findings of this research are illustrated in a numerical example for the uniform demand distribution.     

人民币与新台币汇兑定价机制研究

随着海峡两岸"三通"的正式启动以及两岸金融监管谅解备忘录(MOU)的生效,两岸的经贸交流日趋紧密,因此人民币与新台币之间尽快实现双向直接兑换就显得更加迫切。本文采用直接定价和间接定价两种方法对人民币与新台币的汇率做出定价,直接定价法是根据购买力平价理论和货币模型,通过多元线性回归得到两地汇率与我国大陆和台湾地区产出水平、货币供应量和利率的关系。而间接定价法是根据利率平价理论,通过对人民币兑美元的NDF汇率、远期汇率和即期汇率以及新台币兑美元的NDF汇率和即期汇率分别做单位根检验,协整检验和格兰杰因果关系检验后,发现人民币与新台币各自的汇率之间存在显著的相关关系,因此我们利用NDF汇率替代利率平价公式中的远期汇率,计算出人民币与新台币的即期汇率,通过比较发现:间接定价法更适合于人民币与新台币汇率的决定。     

A Combined Monte Carlo and Possibilistic Approach to Uncertainty Propagation in Event Tree Analysis

In risk analysis, the treatment of the epistemic uncertainty associated to the probability of occurrence of an event is fundamental. Traditionally, probabilistic distributions have been used to characterize the epistemic uncertainty due to imprecise knowledge of the parameters in risk models. On the other hand, it has been argued that in certain instances such uncertainty may be best accounted for by fuzzy or possibilistic distributions. This seems the case in particular for parameters for which the information available is scarce and of qualitative nature. In practice, it is to be expected that a risk model contains some parameters affected by uncertainties that may be best represented by probability distributions and some other parameters that may be more properly described in terms of fuzzy or possibilistic distributions. In this article, a hybrid method that jointly propagates probabilistic and possibilistic uncertainties is considered and compared with pure probabilistic and pure fuzzy methods for uncertainty propagation. The analyses are carried out on a case study concerning the uncertainties in the probabilities of occurrence of accident sequences in an event tree analysis of a nuclear power plant.     

A Computerized Approach to Decision Process Tracing for Decision Support System Design*

We propose the use of computerized process tracing (CPT) tools as an appropriate approach for monitoring the information acquisition and evaluation phase of specific decision processes. CPT tools are unobtrusive and seem particularly relevant for evaluating certain decision tasks that may be supported by decision support systems (DSS). CPT tools can be an important component of DSS development. An information systems research taxonomy developed by previous researchers [29] [36] is used to position research work involving the methodology of CPT. Using a critique suggested by Libby [28], CPT tools are evaluated and compared to alternative process tracing tools. A brief empirical example using CPT is provided, and future uses relative to DSS are suggested. The appendix includes an example of a specific CPT tool.     

A Matrix-Based Approach to Uncertainty and Sensitivity Analysis for Fault Trees1

System unavailabilities for large complex systems such as nuclear power plants are often evaluated through use of fault tree analysis. The system unavailability is obtained from a Boolean representation of a system fault tree. Even after truncation of higher order terms these expressions can be quite large, involving thousands of terms. A general matrix notation is proposed for the representation of Boolean expressions which facilitates uncertainty and sensitivity analysis calculations.     

The Scientific Visualization of Organizations: A Rationale for a New Approach to Organizational Modeling*

This article examines the rationale for the application of a new research tool, scientific visualization, to the study of organizations. Based upon a general theory of visualization, the ability to simultaneously display both variables and organizational entities should allow for easier and more widespread analysis of organizational research issues. Applications and future directions for both research and praxis are discussed.     

Anesthesia Patient Risk: A Quantitative Approach to Organizational Factors and Risk Management Options

The risk of death or brain damage to anesthesia patients is relatively low, particularly for healthy patients in modern hospitals. When an accident does occur, its cause is usually an error made by the anesthesiologist, either in triggering the accident sequence, or failing to take timely corrective measures. This paper presents a pilot study which explores the feasibility of extending probabilistic risk analysis (PRA) of anesthesia accidents to assess the effects of human and management components on the patient risk. We develop first a classic PRA model for the patient risk per operation. We then link the probabilities of the different accident types to their root causes using a probabilistic analysis of the performance shaping factors. These factors are described here as the "state of the anesthesiologist" characterized both in terms of alertness and competence. We then analyze the effects of different management factors that affect the state of the anesthesiologist and we compute the risk reduction benefits of several risk management policies. Our data sources include the published version of the Australian Incident Monitoring Study as well as expert opinions. We conclude that patient risk could be reduced substantially by closer supervision of residents, the use of anesthesia simulators both in training and for periodic recertification, and regular medical examinations for all anesthesiologists.     

Two-Level Hierarchical Transportation Networks: A New Set of Problems and Practical Applications*

This paper defines and solves a set of new problems for two-level hierarchical transportation networks. Given a set of points, the objective is to find the path of a primary link such that a weighted sum of the lengths of the perpendiculars from each point to the primary link and the distance between the feet of the two extreme perpendiculars is minimized. The perpendiculars are the connecting or secondary links and the primary link could be a straight line or a curve. The paper provides an algorithm and computational results for the case in which the primary link is a straight line. Implementation approaches are also discussed.     

The Application of Neural Networks and a Qualitative Response Model to the Auditor's Going Concern Uncertainty Decision*

An auditor gives a going concern uncertainty opinion when the client company is at risk of failure or exhibits other signs of distress that threaten its ability to continue as a going concern. The decision to issue a going concern opinion is an unstructured task that requires the use of the auditor's judgment. In cases where judgment is required, the auditor may benefit from the use of statistical analysis or other forms of decision models to support the final decision. This study uses the generalized reduced gradient (GRG2) optimizer for neural network learning, a backpropagation neural network, and a logit model to predict which firms would receive audit reports reflecting a going concern uncertainty modification. The GRG2 optimizer has previously been used as a more efficient optimizer for solving business problems. The neural network model formulated using GRG2 has the highest prediction accuracy of 95 percent. It performs best when tested with a small number of variables on a group of data sets, each containing 70 observations. While the logit procedure fails to converge when using our eight variable model, the GRG2 based neural network analysis provides consistent results using either eight or four variable models. The GRG2 based neural network is proposed as a robust alternative model for auditors to support their assessment of going concern uncertainty affecting the client company.