基于神经网络的企业信用等级评估

企业信用等级评估是金融领域重要的问题，论文采用人工神经网络模型研究企业信用等级的评估问题，按照企业样本在信用等级的分布状况来抽样，然后，根据企业样本性质的不同，将其分为制造业和非制造业两大类，利用偏相关分析方法建立了企业信用评级的指标体系，此外，还介绍了几种企业信用评级常用的评估模型，并将神经网络评估模型的性能和其他的信用评估模型作了比较，实验结果表明神经网络模型具有更好的预测准确性。     

基于险度函数的研制风险度量研究

现代弹箭系统是一种集多门学科于一身的复杂系统.在研制过程中,由于受到不确定性因素的影响,会给研制工作带来一定的风险.本文分析了各种风险分析与估计方法,给出了风险的数学表述方法和风险分布的概率估计.结合武器系统研制的实际情况,假定行为人风险偏好特性为定常风险中立,给出了在该风险偏好特性下的险度函数.通过险度函数的研制风险分析为风险决策提供了量化的决策依据.以反直升机智能雷战斗部为例,验证了该方法的有效性,并且具有科学和实用价值.     

The Risk Evaluation of the Implement for Enterprise E—Business

In this paper, risk evaluation index system of electronic business is put forward for core manufactures. At the same time, risk evaluation method is established on the basis of fuzzy mathematics and grey theory, and its effectiveness and feasibility are tested in the practical example.     

A Case－Based System for Construction Project Risk Management

1　IntroductionIn the field of construction,project risk management is a complex and important task.Therefore,it is very important for construction project managers to consider all possiblerisk factors affecting a given construction project. Furthermore,they should examine howthese factors interact in the decision- making process and how to balance subjective beliefsand information in identifying risk and uncertainties,and information,and takingcorrective actions to control the identified risk…     

基于竞争型二元风险模型的盈余过程研究

针对保险市场格局改变引入竞争型二元风险模型，利用经典风险模型理论．对其盈余过程的破产概率和生存概率等作出具体的研究．     

模糊数学模型在我国商业银行经营风险评价中的应用

文中通过对目前影响商业银行风险状况的因素的分析,利用模糊数学的基本理论和方法,构造出一个数学模型。该模型适用于对商业银行的经营风险进行综合评价。     

FSA在海事监管中的应用(英文)

介绍综合安全评估(FSA)方法产生的背景,分析FSA评价方法的技术特征,阐述该方法在国际及中国海事机构的运用情况,提出在海事管理工作中加以应用的建议及相关具体措施,探讨该方法在提升海事风险控制实际运用过程中尚存在的问题和未来发展前景.     

邓小平改革开放的风险观初探

对邓小平改革开放的风险观产生的时代性、风险观的主要内容、风险观在改革开放中的地位三个方面进行探索性认识.     

一类高新技术公司风险预测指标分析及评估方法

以管理顾问及咨询公司为背景,来探讨高新技术公司的风险.在分析可能产生的风险基础上,建立风险预测指标体系,并给出风险评估的方法.     

Forecasting value‐at‐risk with a parsimonious portfolio spillover GARCH (PS‐GARCH) model

Accurate modelling of volatility (or risk) is important in finance, particularly as it relates to the modelling and forecasting of value‐at‐risk (VaR) thresholds. As financial applications typically deal with a portfolio of assets and risk, there are several multivariate GARCH models which specify the risk of one asset as depending on its own past as well as the past behaviour of other assets. Multivariate effects, whereby the risk of a given asset depends on the previous risk of any other asset, are termed spillover effects. In this paper we analyse the importance of considering spillover effects when forecasting financial volatility. The forecasting performance of the VARMA‐GARCH model of Ling and McAleer (2003), which includes spillover effects from all assets, the CCC model of Bollerslev (1990), which includes no spillovers, and a new Portfolio Spillover GARCH (PS‐GARCH) model, which accommodates aggregate spillovers parsimoniously and hence avoids the so‐called curse of dimensionality, are compared using a VaR example for a portfolio containing four international stock market indices. The empirical results suggest that spillover effects are statistically significant. However, the VaR threshold forecasts are generally found to be insensitive to the inclusion of spillover effects in any of the multivariate models considered. Copyright © 2008 John Wiley & Sons, Ltd.