个体对股票投资的风险态度研究

传统理论认为个体是风险厌恶的,展望理论提出个体是损失厌恶的,对于损失的感受程度比赢得要强烈得多。本文通过实验表明,大部分个体对于股票投资是风险追求,而不是风险厌恶的,而且在两个参考点之外的区间个体更偏好风险,而在两个参考点之内的区间对风险的偏好程度相对要小。     

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.     

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.     

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.     

Two-stage financial risk tolerance assessment using data envelopment analysis

Typical questionnaires administered by financial advisors to assess financial risk tolerance mostly contain stereotypes of people, have seemingly unscientific scoring approaches and often treat risk as a one-dimensional concept. In this work, a mathematical tool was developed to assess relative risk tolerance using Data Envelopment Analysis (DEA). At its core, it is a novel questionnaire that characterizes risk by its four distinct elements: propensity, attitude, capacity, and knowledge. Over 180 individuals were surveyed and their responses were analyzed using the Slacks-based measure type of DEA efficiency model. Results show that the multidimensionality of risk must be considered for complete assessment of risk tolerance. This approach also provides insight into the relationship between risk, its elements and other variables. Specifically, the perception of risk varies by gender as men are generally less risk averse than women. In fact, risk attitude and knowledge scores are consistently lower for women, while there is no statistical difference in their risk capacity and propensity compared to men. The tool can also serve as a “risk calculator” for an appropriate and defensible method to meet legal compliance requirements, known as the “Know Your Client” rule, that exist for Canadian financial institutions and their advisors.     

Risk averse decision making under catastrophic risk

A nonstandard probabilistic setting for modeling of the risk of catastrophic events is presented. It allows random variables to take on infinitely large negative values with non-zero probability, which correspond to catastrophic consequences unmeasurable in monetary terms, e.g. loss of human lives. Thanks to this extension, the safety-first principle is proved to be consistent with traditional axioms on a preference relation, such as monotonicity, continuity, and risk aversion. Also, a robust preference relation is introduced, and an example of a monotone robust preference relation, sensitive to catastrophic events in the sense of Chichilnisky (2002), is provided. The suggested setting is demonstrated in evaluating nuclear power plant projects when the probability of a catastrophe is itself a random variable.     

Model selection based on Lorenz and concentration curves,Gini indices and convex order

In order to determine an appropriate amount of premium, statistical goodness-of-fit criteria must be supplemented with actuarial ones when assessing performance of a given candidate pure premium. In this paper, concentration curves and Lorenz curves are shown to provide actuaries with effective tools to evaluate whether a premium is appropriate or to compare two competing alternatives. The idea is to compare the premium income for sub-portfolios gathering low risks (identified as low by means of the premiums under consideration) to the true one, or equivalently, to the actual losses. Numerical illustrations performed on hypothetical data and real ones demonstrate the usefulness of the proposed approach.