Predicting exchange rates in Asia: New insights on the accuracy of survey forecasts

By linking measures of forecast accuracy as well as testing procedures with regard to forecast rationality this paper investigates aggregated survey forecasts with forecast horizons of 3, 12, and 24 months for the exchange rates of the Chinese yuan, the Hong Kong dollar, the Japanese yen, and the Singapore dollar vis-à-vis the US dollar and, hence, for four different currency regimes. The rationality of the exchange rate predictions is initially assessed utilizing tests for unbiasedness and efficiency which indicate that the investigated forecasts are irrational in the sense that the predictions are biased. As one major contribution of this paper, it is subsequently shown that these results are not consistent with an alternative, less restrictive, measure of rationality. Investigating the order of integration of the time series as well as cointegrating relationships, this empirical evidence supports the conclusion that the majority of forecasts are in fact rational. Regarding forerunning properties of the predictions, the results are rather mediocre, with shorter term forecasts for the tightly managed USD/CNY FX regime being one exception. As one additional important and novel evaluation result, it can be concluded, that the currency regime matters for the quality of exchange rate forecasts.     

Combining multivariate volatility forecasts using weighted losses

The ability to improve out-of-sample forecasting performance by combining forecasts is well established in the literature. This paper advances this literature in the area of multivariate volatility forecasts by developing two combination weighting schemes that exploit volatility persistence to emphasise certain losses within the combination estimation period. A comprehensive empirical analysis of the out-of-sample forecast performance across varying dimensions, loss functions, sub-samples and forecast horizons show that new approaches significantly outperform their counterparts in terms of statistical accuracy. Within the financial applications considered, significant benefits from combination forecasts relative to the individual candidate models are observed. Although the more sophisticated combination approaches consistently rank higher relative to the equally weighted approach, their performance is statistically indistinguishable given the relatively low power of these loss functions. Finally, within the applications, further analysis highlights how combination forecasts dramatically reduce the variability in the parameter of interest, namely the portfolio weight or beta.     

Identifying US business cycle regimes using dynamic factors and neural network models

We use dynamic factors and neural network models to identify current and past states (instead of future) of the US business cycle. In the first step, we reduce noise in data by using a moving average filter. Dynamic factors are then extracted from a large-scale data set consisted of more than 100 variables. In the last step, these dynamic factors are fed into the neural network model for predicting business cycle regimes. We show that our proposed method follows US business cycle regimes quite accurately in-sample and out-of-sample without taking account of the historical data availability. Our results also indicate that noise reduction is an important step for business cycle prediction. Furthermore, using pseudo real time and vintage data, we show that our neural network model identifies turning points quite accurately and very quickly in real time.     

非自治随机Kuramoto-Sivashinsky方程的Wong-Zakai逼近

在Wong-Zakai逼近下证明了非自治Kuramoto-Sivashinsky方程吸引子的存在性.     

中国与“一带一路”沿线部分国家的经济关系动态分析

GTAP(Global Trade Analysis Project)模型综合考虑了资本总量、资本流动、人口因素、国民生产总值等多项指标,可以用于各相关国的经济发展和经济合作互动关系分析.选用GTAP模型,分析了中国与一带一路沿线部分国家的经济互动关系.在西亚地区选择了沙特阿拉伯、以色列、伊朗、阿曼、阿联酋、科威特、伊拉克共7国,分别在降低关税20%和降低关税50%两种假设条件下展开实证分析.实证结果显示:在关税分别降低20%和50%两种假设条件下,中国和西亚诸国的GDP都有一定程度增加,各国之间进出口规模都有一定程度的扩大,这表明"一带一路"倡议对于沿线各国的经济发展和合作互动具有明显的促进作用.     

基于广义梯形模糊数相似度的交叉效率集结

在不确定环境下,针对模糊数据的多样性和复杂性,本文结合广义梯形模糊数相似度理论将广义梯形模糊数的交叉效率矩阵转换为交叉互评相似度矩阵,并根据模糊信息检索系统中的布尔模型,以能最大程度还原信息本身为准则,构建满足"正相容性"的有序几何平均集结函数对交叉互评相似度矩阵进行集结,从而得到关于每个决策单元的同行评价综合相似度.根据决策单元的同行评价综合相似度,建立反映决策者偏好的模糊一致性偏好矩阵,并对决策单元进行集结权重的分配,根据分配结果计算决策单元的全局交叉效率值.本文的集结方法可以解决不确定环境下,交叉效率矩阵数据多样性的问题,有较高的适用性;并且其集结结果具有较高的一致性和稳定性.最后本文以梯形模糊数的交叉效率矩阵为算例,来说明该方法的合理性和适用性.     

道德风险约束下的最优融资结构——动态金融契约研究综述

债务违约、企业资金链断裂与破产,以及宏观层面的金融危机等经济金融现象促使经济学家反思金融契约的资源配置效率与风险问题.以契约理论的建模技术为逻辑主线,以信息非对称环境中最优契约设计与融资结构优化为关注对象,结合融资结构、资产定价与道德风险之间的内在联系,对动态金融契约理论的研究方法与研究成果进行了系统评述,并根据金融契约理论发展逻辑导出公司金融与资产定价的理论融合方向.基于理论发展路径,初步探讨了动态金融契约理论的未来拓展方向,并结合当前我国经济结构特点给出了该理论的应用前景.     

科技成果社会影响力评估的国际经验及启示

 对英国的卓越研究框架（REF）、荷兰的标准评估协议（SEP）和美国的STAR METRICS这3个典型的科技成果社会影响力评估方案进行比较，识别了其评估对象、评估程序和评估目标方面的差异，重点围绕这3个方案的评估内容指标和评估方法进行了深入分析。结合国际经验，认为中国应当将科技成果社会影响力评估置于公立科研机构绩效评价的核心地位；需要在贯彻国家战略导向和体现评估对象、评估目标和区域差异的基础上开发符合中国国情的科技成果社会影响力评估内容和指标体系；要构建大数据评估并使用数据驱动方法进行科技成果的社会影响力评估。     

基于图形的网络演化博弈的拓扑结构

对基于图形的网络演化博弈,首先求出典型结点策略演化方程,进而给出将结点方程组合成网络局势演化方程的方法。利用局势演化方程,将计算逻辑动态系统不动点与极限环的公式推广用于图形的网络演化博弈。然后,介绍某玩家单独更新的局势演化方程,并依此给出网络演化博弈纯纳什均衡点计算公式。     

考虑动态重构的装备体系可用性及弹性分析

要素协同作战框架下武器装备体系具有可动态重构的系统特性, 既增加了体系的复杂性和交互性, 又使其具有了更为明显的弹性特征。本文首先提出了装备体系弹性多层级评价方法, 确定了组成体系的关键装备要素。其次, 以典型的关键装备要素——武器系统为对象, 提出了基于动态双阈值多状态权重k-out-of-n系统模型的装备体系的性能可用性及弹性分析评价方法。最后, 以编队协同防空作战体系为对象进行案例应用研究, 得到了性能阈值恒定、随时间变化和动态变化这3种情况下的火力网可用度和弹性随时间变化的规律, 并验证了所提模型与评价方法的有效性与实用性。