基于物元与可拓集合理论的企业技术创新综合风险测度模型

技术创新是企业获得竞争优势的动力源泉,是企业谋求生存和发展的重要保障。然而,技术创新是一项高风险活动,企业要避免和减少损失,将威胁转化为机会,就必须对技术创新风险进行管理,而准确度量风险则是进行技术创新风险管理的基础。本文基于物元与可拓集合理论,建立了企业技术创新综合风险测度的多维可拓物元模型,并提出了风险的可拓测度方法。该方法能够综合考虑企业技术创新过程中的各种主观信息和客观信息,反映技术创新过程中的各种风险因素的状态,不仅可以对企业技术创新的综合风险进行测度,而且可以对企业技术创新的客观风险和决策者的主观风险做出准确评估。理论分析和实践结果均表明了所建立模型的可行性和有效性,这为企业技术创新风险的测度提供了一条新的途径。     

基于粗糙集与未确知模型的供应商风险评估方法研究

供应商是制造商材料的来源,供应商风险的发生可能导致原来井然有序的供应链无法正常运转,因此,供应商风险的预警与评估在供应链风险管理中占据着举足轻重的地位。本文提出一种基于粗糙集和未确知测度理论的供应商风险评估方法,该方法首先运用粗糙集理论对繁杂的供应商风险指标体系进行精简,并确定指标权重,然后运用未确知测度模型对供应商风险进行评估,从而使评估结果更加客观。最后在实证研究中以某汽车制造商企业供应商风险评估体系为例,阐明了该评估方法的科学有效性以及评估结果的合理性。     

基于深度学习的多维情景空间下群体性事件分析与预测研究

在社会转型的背景下，我国群体性事件的数量不断攀升，极大地影响了社会的和谐稳定。本文应用"情景-次级情景-对象-要素"模型对各类群体性事件案例进行分解，提取相关的影响因素，构建多维情景空间模型，并提出了用于群体性事件预测的卷积神经网络模型，阐述了其基本原理，然后结合实例说明了其应用。通过根据多维情景空间模型对群体性事件案例进行编码形成的数据样本集，来训练该预测模型，并用AUC值评估其有效性。最后分析了不同影响因素在群体性事件预测中的作用以及应急管理主体的应对方向。     

基于实物期权的基础设施投资担保负担测度及其风险管理研究

从项目筹资模式、基础设施投资担保现状及其运营模式视角出发,研究我国基础设施投资担保负担测度及其风险管理的问题。利用或有负债思想,分析了下限式担保、上下限式担保、比例上下限式担保三类担保的期权特性及其影响因素;运用实物期权方法,在不同担保方式下构建了担保负担评估模型,并对相应的项目投资担保负担进行了评估,结果显示,投资收益担保是担保负担最直接、最主要的来源,也是政府方的或有负债,基于此,在融资谈判中,政府方可以通过调整项目投资担保水平和项目投资担保方式来控制担保负担和锁定担保风险。     

商业连锁企业供应链风险管理

本文着重介绍商业连锁企业供应链风险的评估与控制。在对商业连锁企业供应链风险管理理论研究的基础上，对其进行识别并建立模型加以评估，针对风险性比较大的风险因素进行重点防范与控制。     

基于可信性理论的中国企业FDI风险评估

对中国企业对外直接投资(Foreign Direct Investment,FDI)风险进行科学评估,是企业成功“走出去”的必要前提.通过采用公理化的可信性测度和经典的层次分析法,本文构建一个基于可信性理论的风险评估模型.模型在评估多层次风险指标时,能够避免选取隶属函数存在主观性的问题.此外,可信性测度具有自对偶性,使得模型的评估结果更容易理解和接受.针对风险因素的复杂性,本文建立FDI风险评估的多层次指标体系,并运用所构建的模型对企业FDI风险进行评估.实例证明评估模型的可行性与有效性.     

金融资产风险度量及其在风险投资中的应用——基于稳定分布的新视角

引入稳定分布对沪深两市指数数据进行检验,结果表明两指数具有“尖峰厚尾”的分形特征,在此基础上建立了DaR类风险测度,实证研究表明两指数跌幅时间序列存在协同跌幅共线性效应.其次,给出了蒙特卡洛稳定分布和正态分布模拟下的两类风险测度估计值,建立了离差率模型,结果表明稳定分布下的风险度量适合投资者进行风险管理.最后,研究了不同跟踪时间窗口下的风险测度指标MDD.投资者和风险管理人员不仅要关注VaR类风险,更要警惕DaR类风险指标.     

中国燃油期货市场的VaR与ES风险度量

近年来,我国燃油期货市场取得快速发展,但有关该市场波动特征和风险状况的研究却非常缺乏。以上海期货交易所燃油期货价格指数为例,分别在多头和空头两种头寸状况以及5种不同分位数水平下,运用条件覆盖检验、非条件覆盖检验等后验分析方法,实证对比了不同风险测度模型对VaR和ES两种不同风险指标估计的精度差异。研究结果表明:在我国燃油期货市场的风险测度估计中考虑国际燃油价格波动因素有助于获得更为精准的风险测度精度;在综合考虑了模型对价格变化动力学的刻画效果以及对极端风险的测度精度等因素后,FIGARCH_CST-SST模型是一个相对合理的风险测度模型选择。     

基于行为金融的证券组合风险管理研究

考虑人的心理行为因素,引入行为金融理论于风险管理主体的决策体系,指出了管理者的有限理性心理结构;基于BSV(Barberis-Shleifer-Vishny)思想建立了一种风险度量模型,据此调整了传统的行为证券组合理论;算法释例结果表明,该模型接近证券组合风险决策的实际情景。     

基于目标的风险度量方法

VaR和CVaR在国内外风险管理实践中得到了普遍应用,但监管者以概率置信水平作为其监管目标的方法对于实际投资者的风险度量而言并不是很直观,投资者更加关心的是资产目标价值能否实现的风险.将资产的目标价值以直观的方式加入到风险的定义中,提出了广义一致风险测度公理假设,并证明了广义一致风险测度也具有很好的性质.此外,看跌期权作为测度风险的有效方法,具有直观的经济含义,可以证明它满足广义一致风险测度公理假设.最后建立了看跌期权费风险测度和E-VaR/E-CVaR之间的数量关系.     

Human Factors Analysis for Maritime Accidents Based on a Dynamic Fuzzy Bayesian Network

Human factors are widely regarded to be highly contributing factors to maritime accident prevention system failures. The conventional methods for human factor assessment, especially quantitative techniques, such as fault trees and bow-ties, are static and cannot deal with models with uncertainty, which limits their application to human factors risk analysis. To alleviate these drawbacks, in the present study, a new human factor analysis framework called multidimensional analysis model of accident causes (MAMAC) is introduced. MAMAC combines the human factors analysis and classification system and business process management. In addition, intuitionistic fuzzy set theory and Bayesian Network are integrated into MAMAC to form a comprehensive dynamic human factors analysis model characterized by flexibility and uncertainty handling. The proposed model is tested on maritime accident scenarios from a sand carrier accident database in China to investigate the human factors involved, and the top 10 most highly contributing primary events associated with the human factors leading to sand carrier accidents are identified. According to the results of this study, direct human factors, classified as unsafe acts, are not a focus for maritime investigators and scholars. Meanwhile, unsafe preconditions and unsafe supervision are listed as the top two considerations for human factors analysis, especially for supervision failures of shipping companies and ship owners. Moreover, potential safety countermeasures for the most highly contributing human factors are proposed in this article. Finally, an application of the proposed model verifies its advantages in calculating the failure probability of accidents induced by human factors.     

An overview of maritime waterway quantitative risk assessment models

The safe navigation of ships, especially in narrow shipping waterways, is of the utmost concern to researchers as well as maritime authorities. Many researchers and practitioners have conducted studies on risk assessment for maritime transportation and have proposed risk reduction/control measures accordingly. This article provides a detailed review and assessment of various quantitative risk assessment models for maritime waterways. Eighty-seven academic papers and/or project reports are summarized and discussed. The review then proceeds to analyze the frequency and consequence estimation models separately. It should be pointed out that we further summarize the advantages and disadvantages of frequency estimation models and provide recommendations for their application. From the overview, we find that the quantification of the impact of human error is of great importance and should be considered in future studies. Possible solutions are also proposed in the discussions.     

基于行业相关性的银行业信用风险宏观压力测试研究

遵循宏观审慎管理的原则和理念, 提出了基于行业相关性的银行业信用风险宏观压力测试方法。通过考虑行业相关性和风险因子t分布特性, 对多元风险因子模型进行了拓展;将宏观压力测试情景与多元风险因子模型对接起来, 将压力情景下得到的行业景气指数取值转换为相应压力情景下行业风险因子的条件分布;在考察宏观经济周期的基础上, 采用指数平滑法、回归模型方法和历史情景分析方法处理宏观经济整个周期的历史数据, 从而确定宏观压力测试的情景设置, 这种情景设置能消除信用风险计量的顺周期性。这一过程将银行业经济资本管理与系统性风险防范有机地联系起来。这一信用风险宏观压力测试方法能反映不同行业信贷资产间的违约相关性, 能识别某一行业衰退对其他行业信贷资产产生的负面影响, 从而反映系统性风险的来源及其作用机理。     

Speaking the Truth in Maritime Risk Assessment

Several major risk studies have been performed in recent years in the maritime transportation domain. These studies have had significant impact on management practices in the industry. The first, the Prince William Sound risk assessment, was reviewed by the National Research Council and found to be promising but incomplete, as the uncertainty in its results was not assessed. The difficulty in assessing this uncertainty is the different techniques that need to be used to model risk in this dynamic and data-scarce application area. In previous articles, we have developed the two pieces of methodology necessary to assess uncertainty in maritime risk assessment, a Bayesian simulation of the occurrence of situations with accident potential and a Bayesian multivariate regression analysis of the relationship between factors describing these situations and expert judgments of accident risk. In this article, we combine the methods to perform a full-scale assessment of risk and uncertainty for two case studies. The first is an assessment of the effects of proposed ferry service expansions in San Francisco Bay. The second is an assessment of risk for the Washington State Ferries, the largest ferry system in the United States.     

Fitting Hierarchical Holographic Modeling into the Theory of Scenario Structuring and a Resulting Refinement to the Quantitative Definition of Risk

A point of view is suggested from which the Hierarchical Holographic Modeling (HHM) method can be seen as one more method within the Theory of Scenario Structuring (TSS), which is that part of Quantitative Risk Assessment having to do with the task of identifying the set of risk scenarios. Seen in this way, HHM brings strongly to our attention the fact that different methods within TSS can result in different sets of risk scenarios for the same underlying problem. Although this is not a problem practically, it is a bit awkward conceptually from the standpoint of the "set of triplets" definition of risk, in which the scenario set is part of the definition. Accordingly, the present article suggests a refinement to the set of triplets definition, which removes the specific set of scenarios, found by any of the TSS methods, from the definition of risk and casts it, instead, as an approximation to the "true" set of scenarios that is native to the problem at hand and not affected by the TSS method used.     

Application of Bayesian Networks in Quantitative Risk Assessment of Subsea Blowout Preventer Operations

This article proposes a methodology for the application of Bayesian networks in conducting quantitative risk assessment of operations in offshore oil and gas industry. The method involves translating a flow chart of operations into the Bayesian network directly. The proposed methodology consists of five steps. First, the flow chart is translated into a Bayesian network. Second, the influencing factors of the network nodes are classified. Third, the Bayesian network for each factor is established. Fourth, the entire Bayesian network model is established. Lastly, the Bayesian network model is analyzed. Subsequently, five categories of influencing factors, namely, human, hardware, software, mechanical, and hydraulic, are modeled and then added to the main Bayesian network. The methodology is demonstrated through the evaluation of a case study that shows the probability of failure on demand in closing subsea ram blowout preventer operations. The results show that mechanical and hydraulic factors have the most important effects on operation safety. Software and hardware factors have almost no influence, whereas human factors are in between. The results of the sensitivity analysis agree with the findings of the quantitative analysis. The three‐axiom‐based analysis partially validates the correctness and rationality of the proposed Bayesian network model.     

Assessment of Catastrophic Risk Using Bayesian Network Constructed from Domain Knowledge and Spatial Data

Prediction of natural disasters and their consequences is difficult due to the uncertainties and complexity of multiple related factors. This article explores the use of domain knowledge and spatial data to construct a Bayesian network (BN) that facilitates the integration of multiple factors and quantification of uncertainties within a consistent system for assessment of catastrophic risk. A BN is chosen due to its advantages such as merging multiple source data and domain knowledge in a consistent system, learning from the data set, inference with missing data, and support of decision making. A key advantage of our methodology is the combination of domain knowledge and learning from the data to construct a robust network. To improve the assessment, we employ spatial data analysis and data mining to extend the training data set, select risk factors, and fine‐tune the network. Another major advantage of our methodology is the integration of an optimal discretizer, informative feature selector, learners, search strategies for local topologies, and Bayesian model averaging. These techniques all contribute to a robust prediction of risk probability of natural disasters. In the flood disaster's study, our methodology achieved a better probability of detection of high risk, a better precision, and a better ROC area compared with other methods, using both cross‐validation and prediction of catastrophic risk based on historic data. Our results suggest that BN is a good alternative for risk assessment and as a decision tool in the management of catastrophic risk.     

An integrated modelling approach for raw material management in a steel mill

An integrated modelling approach combining optimisation models with simulation is proposed for coordinated raw material management at steel works throughout the whole supply chain management process. The integrated model is composed of three components: ship scheduling, yard operation simulation and material blending models. The ship scheduling model determines which brands, how much and when they should be arrived, and the problem is modelled as mixed integer linear programming. The simulation model is used to simulate the whole processes from ships’ arrivals to the retrieval of the materials through the berthing and unloading the raw materials. Finally, the raw material blending model is developed for determining the brand and quantity of raw materials to be used. The proposed integrated modelling approach for raw material management has been successfully implemented and applied at steelwork to provide shipping schedules and predict future inventory levels at stock yards. By coordinating all the activities throughout the entire raw material supply chain management process, this article proposes an integrated approach to the problem and suggests a guideline by the appropriate simplification. The quantitative nature of the optimisation model and simulation facilitates an assessment of the risk factors in the supply chain, leading to an evaluation of a wide variety of scenarios and the development of multiple contingency plans. Further research is expected to supplement the ship scheduling models with heuristics for the idiosyncratic constraints of maritime transportation.     

The Role of Quantitative Risk Assessments for Characterizing Risk and Uncertainty and Delineating Appropriate Risk Management Options, with Special Emphasis on Terrorism Risk

The purpose of this article is to discuss the role of quantitative risk assessments for characterizing risk and uncertainty and delineating appropriate risk management options. Our main concern is situations (risk problems) with large potential consequences, large uncertainties, and/or ambiguities (related to the relevance, meaning, and implications of the decision basis; or related to the values to be protected and the priorities to be made), in particular terrorism risk. We look into the scientific basis of the quantitative risk assessments and the boundaries of the assessments in such a context. Based on a risk perspective that defines risk as uncertainty about and severity of the consequences (or outcomes) of an activity with respect to something that humans value we advocate a broad risk assessment approach characterizing uncertainties beyond probabilities and expected values. Key features of this approach are qualitative uncertainty assessment and scenario building instruments.