对期望理论的两个重要推进——损失厌恶系数λ及参考点研究

行为金融学是近20年来最为重要的新兴金融理论,期望理论更是其中的亮点,其运用领域之广超乎人们的想象,所以对它的完善改进具有非常重要的意义。本文对期望理论中的损失厌恶系数和参考点问题进行了深入研究,并且得出了创新性的结果:损失厌恶系数不是常数而是动态变量;得出了内生且纳入更多投资者行为特征的参考点。这些结果是对期望理论的重要改善和推进,对其在大量金融领域和涉及到财富偏好的问题的解决中都大有裨益。     

SECOND BEST TAXES AND QUOTAS IN NITROGEN REGULATION

This paper investigates second best policies to regulate nitrogen leaching. When the policy maker controls the total nitrogen use, an overall tax is superior to an action equivalent overall quota. When the goal is to regulate the expected level of nitrogen leaching, expected profit of a risk neutral farmer is greater under an overall tax than under an overall quota under a reasonable condition. The welfare ranking of action equivalent taxes and quotas for a risk averse farmer is generally ambiguous. In the numerical analyses, overall taxes turned out to be superior to overall quotas with both targets, despite a very high value of absolute risk aversion, 0.01. These results suggest that an overall tax is probably a superior policy to an overall quota to regulate expected total nitrogen use or expected nitrogen leaching for risk neutral and most of risk averse farmers.     

资产组合的CVaR风险的敏感度分析

基于CVaR风险计量技术，分别给出了正态和t分布情形下资产组合的CVaR值，对一般情形下风险资产组合的CVaR风险关于头寸的敏感度进行了分析，研究了其经济意义。     

Distributionally robust modeling of optimal control

The aim of this paper is to formulate several questions related to distributionally robust Stochastic Optimal Control modeling. As an example the distributionally robust counterpart of the classical inventory model is discussed in details. Finite and infinite horizon stationary settings are considered.     

性别差异对研发投资决策影响的实验研究

基于Z-Tree实验平台利用研发投资自然博弈实验研究发现风险规避导致女性的研发投入强度低于男性,但女性在研发投资过程中愿意投入的研发努力程度高于男性。随着时间推移,男性的研发努力程度明显下降,但女性的研发努力程度反而上升。男性和女性在决策中均表现出心理账户效应,女性更加愿意通过努力来提高业绩。男性在投资中更多体现期权思想,而女性更多表现出长期倾向下的持续努力。文章在揭示性别因素影响研发投资机理基础上,为董事会性别多样性、分级董事会以及递延薪酬等治理机制发挥作用机理提供了新解释。研究结论可以为企业职位的性别配置、分级董事会以及激励契约设计提供借鉴。关键词:性别差异;研发投资;实验研究;心理账户;风险规避;长期倾向     

Designing a contaminated soil sampling strategy for human health risk assessment

Human health risk assessment is a site-based approach used to identify the potential health hazards which are induced by an old site contamination. For a proper evaluation of the daily doses of contaminants to which people will be exposed given the future occupation of the site, both a characterization and a quantification of soil pollution are needed. Such information can be provided by soil sampling. Thus the choice of the location, the number, depth and type of soil samples is very important and ought to follow a well-defined strategy. A review of contaminated site sampling practices in Europe and North America could not identify any completely formalized sampling strategy for human health risk assessment. On the contrary there are several approaches which can be roughly classified into two categories: a systematic sampling scheme over the whole site, on the one hand, and a sampling design driven by an initial knowledge of the contamination sources and fitted to the suspected pollution pattern, on the other. The first approach provides a complete coverage of the site but it may be rather expensive and entail useless sampling. The performance of the second depends on the quality of prior information. Actually both methods can be combined as explained hereafter. In view of the specificity of each site, the requirements of health risk assessment and the time and cost constraints, it seems difficult to work out a typical soil sampling strategy suitable for all sites. However, some recommendations can be made according to the site dimensions, the nature, degree and heterogeneity of contamination, and the (future) use of the site. The scientist should thus rely on a thorough examination of all available information (site history, geology and hydrogeology, soil properties, contaminants behaviour , etc.) to delimit contaminated areas as homogeneous as possible and then distribute the sampling points (e.g.using a sampling grid). They should also take the potential exposure paths into account in order to define the areas and soil strata to be sampled as a priority. Statistical and geostatistical tools can be helpful for formulating a sampling strategy as well as for interpreting the collected data. Received: 7 December 2001 Accepted: 24 February 2002     

A quantile-based Time at Risk: A new approach for assessing risk in financial markets

In this paper, we provide a new measure for evaluation of risk in financial markets. This measure is based on the return interval of critical events in financial markets or other investment situations. Our main goal was to devise a model like Value at Risk (VaR). As VaR, for a given financial asset, probability level and time horizon, gives a critical value such that the likelihood of loss on the asset over the time horizon exceeds this value is equal to the given probability level, our concept of Time at Risk (TaR), using a probability distribution function of return intervals, provides a critical time such that the probability that the return interval of a critical event exceeds this time equals the given probability level. As an empirical application, we applied our model to data from the Tehran Stock Exchange Price Index (TEPIX) as a financial asset (market portfolio) and reported the results.     

Dynamic models of segregation†

Some segregation results from the practices of organizations, some from specialized communication systems, some from correlation with a variable that is non‐random; and some results from the interplay of individual choices. This is an abstract study of the interactive dynamics of discriminatory individual choices. One model is a simulation in which individual members of two recognizable groups distribute themselves in neighborhoods defined by reference to their own locations. A second model is analytic and deals with compartmented space. A final section applies the analytics to ‘neighborhood tipping.’ The systemic effects are found to be overwhelming: there is no simple correspondence of individual incentive to collective results. Exaggerated separation and patterning result from the dynamics of movement. Inferences about individual motives can usually not be drawn from aggregate patterns. Some unexpected phenomena, like density and vacancy, are generated. A general theory of ‘tipping’ begins to emerge.     

Supplier quality improvement: The value of information under uncertainty

We consider supplier development decisions for prime manufacturers with extensive supply bases producing complex, highly engineered products. We propose a novel modelling approach to support supply chain managers decide the optimal level of investment to improve quality performance under uncertainty. We develop a Poisson–Gamma model within a Bayesian framework, representing both the epistemic and aleatory uncertainties in non-conformance rates. Estimates are obtained to value a supplier quality improvement activity and assess if it is worth gaining more information to reduce epistemic uncertainty. The theoretical properties of our model provide new insights about the relationship between the degree of epistemic uncertainty, the effectiveness of development programmes, and the levels of investment. We find that the optimal level of investment does not have a monotonic relationship with the rate of effectiveness. If investment is deferred until epistemic uncertainty is removed then the expected optimal investment monotonically decreases as prior variance increases but only if the prior mean is above a critical threshold. We develop methods to facilitate practical application of the model to industrial decisions by a) enabling use of the model with typical data available to major companies and b) developing computationally efficient approximations that can be implemented easily. Application to a real industry context illustrates the use of the model to support practical planning decisions to learn more about supplier quality and to invest in improving supplier capability.