Predictive ability and economic gains from volatility forecast combinations

The availability of numerous modeling approaches for volatility forecasting leads to model uncertainty for both researchers and practitioners. A large number of studies provide evidence in favor of combination methods for forecasting a variety of financial variables, but most of them are implemented on returns forecasting and evaluate their performance based solely on statistical evaluation criteria. In this paper, we combine various volatility forecasts based on different combination schemes and evaluate their performance in forecasting the volatility of the S&P 500 index. We use an exhaustive variety of combination methods to forecast volatility, ranging from simple techniques to time-varying techniques based on the past performance of the single models and regression techniques. We then evaluate the forecasting performance of single and combination volatility forecasts based on both statistical and economic loss functions. The empirical analysis in this paper yields an important conclusion. Although combination forecasts based on more complex methods perform better than the simple combinations and single models, there is no dominant combination technique that outperforms the rest in both statistical and economic terms.     

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.     

Can online search data improve the forecast accuracy of pork price in China?

Online search data provide us with a new perspective for quantifying public concern about animal diseases, which can be regarded as a major external shock to price fluctuations. We propose a modeling framework for pork price forecasting that incorporates online search data with support vector regression model. This novel framework involves three main steps: that is, formulation of the animal diseases composite indexes (ADCIs) based on online search data; forecast with the original ADCIs; and forecast improvement with the decomposed ADCIs. Considering that there are some noises within the online search data, four decomposition techniques are introduced: that is, wavelet decomposition, empirical mode decomposition, ensemble empirical mode decomposition, and singular spectrum analysis. The experimental study confirms the superiority of the proposed framework, which improves both the level and directional prediction accuracy. With the SSA method, the noise within the online search data can be removed and the performance of the optimal model is further enhanced. Owing to the long-term effect of diseases outbreak on price volatility, these improvements are more prominent in the mid- and long-term forecast horizons.     

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.     

金融系统压力:指数化测度及其溢出效应研究

2008年全球金融危机的爆发及其冲击促使监管者更多的关注全球金融市场的关联网络特征及金融风险在各金融市场间的传导机制.本文基于全球向量自回归模型(GVAR),从关联网络的视角构建了金融压力溢出效应模型,考察信贷市场、资本市场、外汇市场、债券市场和货币市场等五个金融子市场压力在不同国家和市场间的传导及其动态演变.研究结果表明,美国是全球金融市场中金融压力主要溢出者;发达经济体主要通过其资本市场和货币市场影响我国的信贷市场、资本市场和外汇市场,而新兴经济体金融压力则主要影响我国的债券市场和货币市场.资本市场和货币市场是不同国家和金融子市场间金融压力传染的重要路径.2014年以来国际金融市场压力对我国金融市场的影响远高于金融危机时期,且资本市场和外汇市场所受影响更为显著.     

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。     

新时代中国跻身创新型国家前列的前景、目标和建议——基于全球创新指数报告的研究

 比较了中国与处于创新型国家前列的发达国家的创新能力和水平，分析了新时代中国跻身创新型国家前列的前景和挑战。分析表明：（1）中国研发投入总量、专利和论文数量已经处于创新型国家前列；（2）企业基础研发投入占比、发明专利占比、研发人员密度、论文引用率等指标还与创新型国家差距较大，追赶上有较大难度；（3）知识产权保护和产学研合作水平亟待提高。因此，中国2030-2035年跻身创新型国家前列，关键在于优化创新投入结构，提高创新成果质量。     

基于指标函数的舰载机机队回收调度优化研究

由于复飞逃逸、飞行故障等因素随机发生, 传统静态调度方法难以有效解决动态随机调度问题。对此，提出一种基于飞机优先序指标函数的蒙特卡罗模拟-差分进化搜索的实时调度算法。在离线模拟回收环境下，由算法完成基于随机模型的指标函数系数优化，后由该指标函数实现对机队优先序的实时评估排序，进而完成回收在线动态调度。仿真结果表明所提算法能有效解决回收调度问题。在相同机队初始输入下, 目标值呈现较好统计特性, 其期望值能够快速收敛到一定范围, 且无复飞情况下着舰时间窗目标呈显著正态性; 不同回收机队输入对指标系数最优值的散布较小, 表明指标函数评估优先序具有较好的通用性和有效性; 模拟回收调度发现, 着舰成功率的提高将显著降低机队复飞次数, 提高回收效率。     

智能控制系统智能水平评价的软件系统

在“一种智能控制系统智能水平的评价方法”提出方法的基础上,提出一种新的评价算法,并设计开发出智能控制系统智能水平评价的软件系统。给出了系统的功能模块设计、数据库设计等,最后列举了两个模拟评价的例子。