基于Copula函数的尾部相关性度量

本文讨论了Copula函数的尾部相关性,当边缘分布是标准Frechet分布时,计算了Copula函数的尾部相关系数及缓慢变化函数,并且得出线性混合Copula函数的尾部相关性是几乎独立的或者较强的渐近相关.     

基于Copula函数的秩相关和尾相关研究

变量间的相关结构是考察变量联合分布特征的首要任务。最经典的是线性相关系数,然而由于线性相关系数是建立在正态分布假设基础上的,存在着很多的局限性,所以,引入了用Copula函数表示的秩相关和尾相关测度。用Copula函数表示的秩相关和尾相关测度比较稳健,不易受极端值影响,且不需要正态分布假设。最后通过模拟得到了上市公司t-Cop-ula的秩相关和尾相关测度。     

消费者价格指数和生产者价格指数相关性研究——基于Copula函数

利用Copula函数,基于2004.1-2012.3的消费者价格指数(CPI)和生产者价格指数(PPI)数据,文章实证分析CPI和PPI的相互关系;通过平方欧式距离检验,认为t-Copula函数是所有Copula函数中对CPI和PPI的拟合程度最优,计算结果表明CPI、PPI存在对称的尾部相关性,两者表现出同时上涨或同时下跌的状态;全国消费者价格指数NCPI、城市消费者价格指数UCPI和农村消费者价格指数RCPI与PPI的尾部相关系数分别为0.4192、0.4560和0.2136,农村相对城市自给自足成分较高和消费结构的差异,PPI对RCPI的影响程度要弱于对UCPI的影响程度。     

连接函数(Copula)在行业投资中的应用

文章提出了在中国股市测定选择和模拟Copula函数的方法,同时也给出了对股市的对数收益率如何使用不同的二元Copula生成有效的蒙特卡洛方法。     

信用风险度量及其组合管理中的相关性研究——Copula函数的引进

综述了从传统过渡到现代的信用风险度量方法,并比较了它们的特点.提出组合管理思想是信用风险管理的方向,组合的相关性问题是研究重点.而Copula函数具有描述非线性相关的优势,使用Copula函数可以更加精确地度量信用风险.     

基于径向基函数的汽车销量预测研究

径向基函数具有良好的逼近任意非线性函数和表达系统内在的难以解析的规律性的能力,并且具有极快的学习收敛速度。基于径向基函数网络的在预测非线性数据上的优点,我们可以将其用于汽车销量的预测。     

基于特征价格与SVM的二手房价格评估

二手房价格的准确衡量一直是个永恒的话题。为了避免传统评估方法的不足,更加合理地预测二手房价格,引入特征价格理论和支持向量机理论(SVM),以杭州市西湖区内的二手房价格数据,构建价格评估模型,实证研究表明具有良好的评估效果。     

基于特征价格的哈尔滨市住宅价格空间分异研究

哈尔滨市住宅价格具有一定的空间分异特征。住宅价格较高的地区主要集中在两大块,一为道里区,二为南岗区。在开发区泛CBD范围内,临近长江路和红旗大街的住宅价格又出现了一个小高潮,以上三点连线为较高住宅价格线,向两侧房价大体呈放射下降趋势。在对住宅价格的影响中,建筑结构、物业管理水平和到CBD距离为一类影响因素。     

Copula函数在风险管理中的应用研究--以上证A股与B股的相关结构分析为例

Copula函数为研究资产间的相关性提供了一种新方法,已被越来越多地应用于风险管理。基于对Copula函数的基本特点及其在风险管理中的作用分析,以及运用Copula函数对上海证券市场A股与B股指数的相关结构进行的分析,发现了与国外市场不同的研究结果:不论市场处于上升期或下跌期,上证A股与B股指数间均存在较强的尾部相关性。     

Oil price forecasting under asymmetric loss

Based on the approach developed by Elliott et al. (2005), we found that the loss function of a sample of oil price forecasters is asymmetric in the forecast error. Our findings indicate that the loss oil price forecasters incurred when their forecasts exceeded the price of oil tended to be larger than the loss they incurred when their forecast fell short of the price of oil. Accounting for the asymmetry of the loss function does not necessarily make forecasts look rational.     

Forecasting house prices using dynamic model averaging approach: Evidence from China

Forecasting house price has been of great interests for macroeconomists, policy makers and investors in recent years. To improve the forecasting accuracy, this paper introduces a dynamic model averaging (DMA) method to forecast the growth rate of house prices in 30 major Chinese cities. The advantage of DMA is that this method allows both the sets of predictors (forecasting models) as well as their coefficients to change over time. Both recursive and rolling forecasting modes are applied to compare the performance of DMA with other traditional forecasting models. Furthermore, a model confidence set (MCS) test is used to statistically evaluate the forecasting efficiency of different models. The empirical results reveal that DMA generally outperforms other models, such as Bayesian model averaging (BMA), information-theoretic model averaging (ITMA) and equal-weighted averaging (EW), in both recursive and rolling forecasting modes. In addition, in recent years it is found that the Google search index, instead of fundamental macroeconomic or monetary indicators, has developed greater predictive power for house price in China.     

中国碳排放试点市场碳交易价格分析及预测

对试点市场碳价格分析结果表明:①试点市场碳价格平均水平相差较大,各市场数据均出现尖峰厚尾、波动率集聚、多重分形特征;②试点市场月均价分析过程中,发现新的碳排放市场的建立会对各个碳市场交易价格有提升作用,免费碳排放配额比例的适当调整有利于碳排放配额交易价格下降,碳排放市场核算与核查体系的逐步完善会使碳排放配额交易价格趋于平稳。本文采用马尔科夫转换多重分形模型对碳价格进行预测,得出了准确度较高的结果。     

On the internal consistency of short-term,medium-term and long-term oil price forecasts

We derive internal consistency restrictions on short-, medium- and long-term oil price forecasts. We then analyse whether oil price forecasts extracted from the Survey of Professional Forecasters (SPF) conducted by the European Central Bank (ECB) satisfy these internal consistency restrictions. We find that neither short-term forecasts are consistent with medium-term forecasts nor that medium-term forecasts are consistent with long-term forecasts. Using a more complex expectation formation structure featuring a distributed lag structure, however, we find stronger evidence of internal consistency of medium-term forecasts with long-term forecasts.     

新股上市首日开盘价预测方法的改进与启示

本文分析了现有新股开盘价预测方法存在的缺陷，简要介绍了启发式决策及其应用价值。启发式是凭借经验的解题方法，是一种思考上的捷径，也称之为经验法则或拇指法则。如果在引入变量时考虑投资者决策的启发式心理因素，以还原投资者形成价格判断的真实决策过程，便可以提高新股开盘价预测的精度。运用实际样本对新股开盘价预测方法进行改进，改进的指导思想是考虑投资者的启发式判断心理。主要改进之处有两点，一是对自变量进入预测模型的方式进行改进。二是找出合适的样本容量的大小。改进之后的预测方法使得预测误差减少了2／3。文章最后论述了新改进的预测方法对实证研究的启示意义。     

Forecasting US real house price returns over 1831–2013: evidence from copula models

Given the existence of nonnormality and nonlinearity in the data generating process of real house price returns over the period of 1831–2013, this article compares the ability of various univariate copula models, relative to standard benchmarks (naive and autoregressive models) in forecasting real US house price over the annual out-of-sample period of 1874–2013, based on an in-sample of 1831–1873. Overall, our results provide overwhelming evidence in favour of the copula models (Normal, Student’s t, Clayton, Frank, Gumbel, Joe and Ali-Mikhail-Huq) relative to linear benchmarks, and especially for the Student’s t-copula, which outperforms all other models both in terms of in-sample and out-of-sample predictability results. Our results highlight the importance of accounting for nonnormality and nonlinearity in the data generating process of real house price returns for the US economy for nearly two centuries of data.     

Theory Analysis of Model for Price Fluctuations in a Single Commodity Market

The price model for a single commodity market is a very important economic model that describes the basic rules for price fluctuations in a single commodity market. In this paper, we investigated the general case for the model, and proved that every positive solution is bounded and we obtained a necessary and sufficient condition for oscillation of every positive solution concerning positive state solution.     

小波神经网络预测方法在石油期货价格预测中的应用研究

金融时间序列数据的预测是预测领域的热点问题。本文结合小渡变换与神经网络的有关理论,给出了基于小渡神经网络的石油期货价格预测具体学习算法并进行了拟合及检测,结果表明该方法具有比常用的BP算法及径向基函数网络算法（HCM算法）更好的拟合能力、推广能力,可为石油期货买卖决策提供一定的依据,并可推广于其它金融时间序列的预测。