多面体不确定线性系统的鲁棒D-稳定研究

针对一类多面体不确定线性系统给出一种新的鲁棒D-稳定的充分条件,该条件可以由一族线性矩阵不等式(LM Is)的可行解得到,而这族LM Is可以由多面体的顶点描述。通过引入参数相关Lyapunov函数使得该条件的保守性降低,与其他条件相比这个条件具有更广泛的适用性,推广并改进了现有成果。     

对测不准关系的认同与争议

介绍了测不准关系的一些不同的表述和证明方法,对其中关于这一原理的认同和有争议的问题进行了比较与分析.结合殿村和蔡林格(Zeilinger)近期完成的电子和中子衍射实验的结果,评述了有关量子理论的新进展.这些实验充分地证明了单个粒子具有波动性的事实.测不准关系原本是微观粒子波动性质的必然结果,所以它应当适用于单个微观粒子.     

基于线性矩阵不等式的贷款组合鲁棒优化模型

运用线性矩阵不等式方法,研究了商业银行贷款组合选择的鲁棒优化问题.在Markowitz均值-方差理论基础上,建立了贷款组合鲁棒优化模型,并用多个期望收益向量和协方差矩阵描述未来贷款收益的不确定性,给出了线性矩阵不等式的求解方法.用数值仿真验证了模型的有效性.由于模型考虑了未来贷款收益的不确定性,得到了可靠性较高的结果.研究结果表明,该模型具有鲁棒性,可以有效降低贷款风险,并可为商业银行提供贷款决策依据.     

Forecasting and nowcasting emerging market GDP growth rates: The role of latent global economic policy uncertainty and macroeconomic data surprise factors

In this paper, we assess the predictive content of latent economic policy uncertainty and data surprise factors for forecasting and nowcasting gross domestic product (GDP) using factor-type econometric models. Our analysis focuses on five emerging market economies: Brazil, Indonesia, Mexico, South Africa, and Turkey; and we carry out a forecasting horse race in which predictions from various different models are compared. These models may (or may not) contain latent uncertainty and surprise factors constructed using both local and global economic datasets. The set of models that we examine in our experiments includes both simple benchmark linear econometric models as well as dynamic factor models that are estimated using a variety of frequentist and Bayesian data shrinkage methods based on the least absolute shrinkage operator (LASSO). We find that the inclusion of our new uncertainty and surprise factors leads to superior predictions of GDP growth, particularly when these latent factors are constructed using Bayesian variants of the LASSO. Overall, our findings point to the importance of spillover effects from global uncertainty and data surprises, when predicting GDP growth in emerging market economies.     

Does a lot help a lot? Forecasting stock returns with pooling strategies in a data‐rich environment

A variety of recent studies provide a skeptical view on the predictability of stock returns. Empirical evidence shows that most prediction models suffer from a loss of information, model uncertainty, and structural instability by relying on low‐dimensional information sets. In this study, we evaluate the predictive ability of various lately refined forecasting strategies, which handle these issues by incorporating information from many potential predictor variables simultaneously. We investigate whether forecasting strategies that (i) combine information and (ii) combine individual forecasts are useful to predict US stock returns, that is, the market excess return, size, value, and the momentum premium. Our results show that methods combining information have remarkable in‐sample predictive ability. However, the out‐of‐sample performance suffers from highly volatile forecast errors. Forecast combinations face a better bias–efficiency trade‐off, yielding a consistently superior forecast performance for the market excess return and the size premium even after the 1970s.     

Forecast Combination and Bayesian Model Averaging: A Prior Sensitivity Analysis

In this study we evaluate the forecast performance of model‐averaged forecasts based on the predictive likelihood carrying out a prior sensitivity analysis regarding Zellner's g prior. The main results are fourfold. First, the predictive likelihood does always better than the traditionally employed ‘marginal’ likelihood in settings where the true model is not part of the model space. Secondly, forecast accuracy as measured by the root mean square error (RMSE) is maximized for the median probability model. On the other hand, model averaging excels in predicting direction of changes. Lastly, g should be set according to Laud and Ibrahim (1995: Predictive model selection. Journal of the Royal Statistical Society B 57 : 247–262) with a hold‐out sample size of 25% to minimize the RMSE (median model) and 75% to optimize direction of change forecasts (model averaging). We finally apply the aforementioned recommendations to forecast the monthly industrial production output of six countries, beating for almost all countries the AR(1) benchmark model. Copyright © 2011 John Wiley & Sons, Ltd.     

不确定条件下纵向研发合作的投资决策研究

针对一个存在两个上游企业和两个下游企业的双层市场结构,对上下游企业实施纵向研发合作策略下的产量和利润进行了研究。并考虑了研发固有的不确定性和溢出效应的影响,从企业是否进行研发以及进行多大程度的研发两个方面,研究上下游企业实施纵向研发合作的投资决策。并在此基础上,探讨了企业研发成本分担比例和违约赔偿。     

双重不确定冷贮备系统的可靠性分析

运用不确定理论研究了无充足样本的冷贮备冗余系统。假设系统所有元件的寿命服从具有不确定参数且相互独立的不确定分布,基于可靠度函数和平均寿命建立了3类冗余系统可靠性的数学模型:转换开关完全可靠型、转换开关离散型、转换开关连续型的冷贮备系统。此外,针对元件寿命为独立同分布的特殊情况进行了分析,给出数值算例,进一步印证模型的合理性。     

不确定与损毁情景下可靠性设施选址鲁棒优化模型与算法研究

设施选址是长期战略性决策问题,选址决策面临各种不确定因素,设计一个可靠的选址网络具有重要的战略意义.本文同时考虑需求的不确定性及设施可能损毁的情景,扩展无容量限制的固定费用可靠性选址模型,建立不确定与损毁情景下服务能力有限的可靠性设施选址鲁棒优化模型.基于Bertsimas和Sim鲁棒优化方法提出一个新的混合整数规划模型,通过引入辅助变量和对偶变换实现非线性鲁棒优化模型转化为鲁棒对应模型,提出蝙蝠算法(BA)对模型予以求解,并通过算例仿真验证模型和算法的可行性,为设施选址决策提供模型和方法设计.     

Knight不确定环境下供应商的乐观度对定价决策的影响

针对供应商的可变成本和产品市场价的双重Knight不确定性,本文运用α-maxmin多先验期望效用和纳什讨价还价模型研究了供应商的定价问题,涉及固定价格、成本加成和参考市场价三种合同,剖析了供应商的乐观度和含糊度对这些定价决策的影响。结论表明,固定价格随着供应商对成本的乐观度的提高而降低,随着市场价的乐观度的提高而提高.成本(市场价)的乐观度决定着成本(市场价)的含糊度与定价结果的关系.参考市场价合同下,供应商的定价权威不影响目标价格与成本的乐观度的关系,但影响目标价格与市场价的乐观度之间的关系。上述研究为分析不确定环境下供应商的定价行为提供理论支持。