契约型战略联盟的灵活性期权价值研究

当面对较高程度的不确定性时,企业可以选择契约型战略联盟作为战略投资的治理模式,根据相关不确定因素的发展变化而选择不同的合作伙伴,这样,不但确保了战略投资灵活期权价值的实现,而且此治理模式本身也具有较灵活的期权价值.契约型联盟可以分为紧密型战略联盟和松散型战略联盟.本文应用期权动态规划定价模型,对紧密型联盟治理模式和松散型联盟契约治理模式的灵活性期权价值做了模型分析,并进行了相关参数的敏感性比较.分析结果表明,在一定范围内.当战略投资相关因素的不确定性程度较高时,应该选择较灵活的治理模式,如松散型战略联盟.     

日本制造企业新产品开发过程中模糊前端创新的效果分析

本文对日本制造企业新产品开发(NPD)过程中模糊前端(FFE)创新成功的关键因素进行了大样本分析,开发并构建了FFE创新要素与NPD项目绩效之间的因果关系模型.根据513家日本制造企业NPD项目的调查数据,运用AMOS验证因果关系模型,我们得出两个重要结论:在FFE阶段,技术和市场的不确定性减少越多,NPD项目的效率越高;开发前项目初始规划强度越大,开发过程中市场和技术的不确定性就越少.而且相对服务企业而言,制造企业的管理层更容易做出对降低市场不确定性有重大影响的初始规划.     

Dynamic pricing and revenue management process in Internet retailing under uncertainty: An integrated real options approach

Competition and demand volatility often cause modern enterprises to be confronted by uncertain environments. When a firm manages revenue in such competitive and risky environments, the optimization of pricing and capacity allocation, subject to a fixed time and capacity, becomes a complicated problem. Many previous papers concerning revenue management (RM) and pricing require that the firm possesses the ability to know the demand curve (or demand distribution) and set prices on it to maximize profits. However, this assumption may not be the case in some industries. Therefore, this paper focuses on the dynamic lead indicators rather than assumptive lag indicators to establish a concise and flexible decision model for practical use. This paper provides an integrated real options (IRO) approach with analytic hierarchy process (AHP) for the auction RM problem under competitive/dynamic pricing and revenue uncertainty in Internet retailing. A numerical example is also presented to illustrate that the IRO approach can generate better decisions than the naı¨ve (or risk unawareness) approach in revenue quality of safety and profitability. The new perspective and approach proposed by this paper can be extended to other RM fields whenever both profitability and risk are critical to decision making.     

经济不确定性的量化测度研究：前沿进展与理论综述

测量经济不确定性是一件重要但又较困难的事情,目前国内外专门研究经济不确定性测量方法的文献并不多。本文梳理了近年来国内外量化测度经济不确定性的文献,从中将零碎的有关经济不确定性的测量方法提炼出来,归纳为三大类：通过寻找合适的经济不确定性替代指标、通过调查数据计算被调查者对当前经济状况和预期的主观感知截面数据偏差（或预测偏差）计算不确定性、通过构建状态空间模型求解作为隐含状态量的经济不确定性。本文详细介绍了每类方法的原理和典型方法,对各类方法做了评述：分析了各类方法的适用场景和优缺点,探讨了应用每类方法的关键点和难点,并介绍了每类方法的代表性应用实例。本文清晰、全面地展示了目前量化测度经济不确定性的前沿进展和理论发展,为研究经济不确定性夯实基础工作,补缺了近年来国内在量化测度经济不确定性方法综述这一领域的空白。     

多样化水平与中国企业出口波动:基于产品和市场组合的研究

借鉴Markowitz资产组合理论的思想,本文利用中国制造业企业2000-2012年的出口交易数据,检验了企业在产品和市场组合上的多样化水平对其出口波动的影响,并对三者之间的作用机制进行了探讨。研究发现,中国企业多样化水平在经历了加入WTO之后的急速上升之后处于相对稳定的水平。但是,企业间的多样化水平在贸易方式、区域和产业等层面呈现明显的分化特征;其次,出口多样化指数对企业出口波动的影响呈现 “倒U型”的动态特征。从平抑出口波动的效果上来看,市场多样化要比产品多样化更为有效;另外,本文还进一步通过分解出口方差的方法,构建中介效应模型以揭示了市场多样化策略影响企业出口波动的机制。     

Percentiles of the Product of Uncertainty Factors for Establishing Probabilistic Reference Doses

Exposure guidelines for potentially toxic substances are often based on a reference dose (RfD) that is determined by dividing a no-observed-adverse-effect-level (NOAEL), lowest-observed-adverse-effect-level (LOAEL), or benchmark dose (BD) corresponding to a low level of risk, by a product of uncertainty factors. The uncertainty factors for animal to human extrapolation, variable sensitivities among humans, extrapolation from measured subchronic effects to unknown results for chronic exposures, and extrapolation from a LOAEL to a NOAEL can be thought of as random variables that vary from chemical to chemical. Selected databases are examined that provide distributions across chemicals of inter- and intraspecies effects, ratios of LOAELs to NOAELs, and differences in acute and chronic effects, to illustrate the determination of percentiles for uncertainty factors. The distributions of uncertainty factors tend to be approximately lognormally distributed. The logarithm of the product of independent uncertainty factors is approximately distributed as the sum of normally distributed variables, making it possible to estimate percentiles for the product. Hence, the size of the products of uncertainty factors can be selected to provide adequate safety for a large percentage (e.g., approximately 95%) of RfDs. For the databases used to describe the distributions of uncertainty factors, using values of 10 appear to be reasonable and conservative. For the databases examined the following simple "Rule of 3s" is suggested that exceeds the estimated 95th percentile of the product of uncertainty factors: If only a single uncertainty factor is required use 33, for any two uncertainty factors use 3 x 33 approximately 100, for any three uncertainty factors use a combined factor of 3 x 100 = 300, and if all four uncertainty factors are needed use a total factor of 3 x 300 = 900. If near the 99th percentile is desired use another factor of 3. An additional factor may be needed for inadequate data or a modifying factor for other uncertainties (e.g., different routes of exposure) not covered above.     

Efficient Sensitivity/Uncertainty Analysis Using the Combined Stochastic Response Surface Method and Automated Differentiation: Application to Environmental and Biological Systems

Estimation of uncertainties associated with model predictions is an important component of the application of environmental and biological models. "Traditional" methods for propagating uncertainty, such as standard Monte Carlo and Latin Hypercube Sampling, however, often require performing a prohibitive number of model simulations, especially for complex, computationally intensive models. Here, a computationally efficient method for uncertainty propagation, the Stochastic Response Surface Method (SRSM) is coupled with another method, the Automatic Differentiation of FORTRAN (ADIFOR). The SRSM is based on series expansions of model inputs and outputs in terms of a set of "well-behaved" standard random variables. The ADIFOR method is used to transform the model code into one that calculates the derivatives of the model outputs with respect to inputs or transformed inputs. The calculated model outputs and the derivatives at a set of sample points are used to approximate the unknown coefficients in the series expansions of outputs. A framework for the coupling of the SRSM and ADIFOR is developed and presented here. Two case studies are presented, involving (1) a physiologically based pharmacokinetic model for perchloroethylene for humans, and (2) an atmospheric photochemical model, the Reactive Plume Model. The results obtained agree closely with those of traditional Monte Carlo and Latin hypercube sampling methods, while reducing the required number of model simulations by about two orders of magnitude.     

考虑不确定性影响的银行服务质量评价

本文研究了不确定性对服务质量评价的影响,建立了服务质量评价的均值—方差模型,并进行了实证分析和检验。这一模型被应用来对我国主要商业银行的服务质量和顾客满意度进行排序分析,其结果显示银行服务质量的提高应该从服务的平均水平和一致性两个方面来努力。     

Risk—Its Priority and Probability: The Analytic Hierarchy Process

Risk estimation involves priorities and probabilities which are themselves a form of priority of natural alternatives. This paper provides illustrations of how one can deal with risk and uncertainty using the Analytic Hierarchy Process, a new approach to measurement by ratio scales. The paper also includes a discussion of how to deal with risk strategies involving interdependence. Particular emphasis is made on the siting of nuclear power plants.     

Uses of Probabilistic Exposure Models in Ecological Risk Assessments of Contaminated Sites

Food web models have two uses in assessments of environmental contaminants. First, they are used to determine whether remediation is needed by estimating exposure of end-point species and subsequent effects. Second, they are used to establish cleanup goals by estimating concentrations of contaminants in ambient media that will not cause significant effects. This paper demonstrates how achievement of these goals can be enhanced by the use of stochastic food web models. The models simulate the dynamics of PCBs and mercury in the food webs of mink and great blue herons. All parameters of the models are treated as having knowledge uncertainty, due to imperfect knowledge of the actual parameter values for the site, chemicals, and species of interest. This uncertainty is an indicator of the potential value of additional measurements. In addition, those parameters that are responsible for variance among individual organisms are assigned stochastic uncertainty. This uncertainty indicates the range of body burdens that are expected when the end-point species are monitored. These two types of uncertainty are separately accounted for in Monte Carlo simulations of the models. Preliminary monitoring results indicate that the models give reasonably good estimates of heron egg and nestling body burdens and of variance among individuals.