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     

基于Bayes网络的项目风险评估方法研究

本文研究了项目风险评估的建模方法 ,通过与其它传统的项目风险评估方法比较 ,针对项目风险评估的信息不完备、具有一定不确定性的特点 ,选择Bayes网络建立项目风险评估模型。本文内容适合于在不确定环境下 ,信息不完备时的项目风险评估     

科技型中小企业发展的金融支持问题研究

科技型中小企业的发展离不开金融的支持。针对科技型中小企业的融资特点和我国科技型中小企业的融资现状,通过借鉴国际经验,从加强政府支持、完善风险投资机制、加大商业银行支持和建立多层次资本市场这四个方面论述了如何构建科技型中小企业的金融支持体系。     

基于组合赋权云模型的阻力伞保障风险评估

针对阻力伞保障风险指标体系不完善、风险管理不系统的问题,通过工作分解结构(work breakdown structure, WBS)-资源分解结构(resource breakdown structure, RBS)法兼顾工作流程和风险要素,建立清晰完善的阻力伞保障风险指标体系。采用组合赋权法融合不确定层次分析法(analytic hierarchy process, AHP)-集对分析的主观权重和CRITIC法(criteria importance though intercriteria correlation)的客观权重实现对风险指标的合理赋权,建立基于云模型的风险评估模型确定风险等级,引入雷达图直观地展示和等级并进行风险分析。研究结果对于实施风险控制、提升保障效率和确保飞行安全具有重要意义。     

VaR--一种风险度量的方法

在险价值是目前市场风险估值的主流理论，被用来估计市场风险暴露在给定置信度下的最坏的预期损失.本文介绍了VaR的概念和计算方法。考虑到时变风险，讨论了GARCH模型，最后给出了评价模型的后验测试方法。     

VaR模型在我国金融风险管理中的运用研究

近20多年来金融市场迅猛发展,金融机构所面临的风险日趋复杂,主要风险已从信用风险转向了市场风险,表现为利率风险、股价风险和汇率风险的综合。我国金融市场是一个发展中的新兴市场,建立适应于我国金融市场的风险预测模型对我国金融风险管理十分必要。文章从研究VaR模型的具体操作方法入手,分析VaR法对于我国金融市场风险管理的影响,探讨VaR法在我国风险度量和金融监管中存在的问题并提出改进策略。     

高速公路网管理单元实时安全可靠度研究

高速公路运营安全性是实现高速公路高效运营的基本前提,本文提出采用实时安全可靠度来表征高速公路网管理单元的运营安全性。首先,根据研究目的,提出了高速公路网管理单元划分方法。然后,本文将高速公路网管理单元实时安全可靠度分为静态安全可靠度SS[i,t]和动态安全可靠度SD[i,t],静态安全可靠度采用模糊综合评判方法确定,动态安全可靠度在分析单车行车风险的基础上,采用可靠性图能分析方法确定。最后,本文通过算例,将实时安全可靠度的计算方法加以说明。     

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.