【作者】 王艳；

【导师】 马军海；

【作者基本信息】 天津大学 ， 管理科学与工程， 2010， 硕士

【摘要】 长期以来,黄金作为价值尺度、流通手段、储藏手段、支付手段和世界货币等职能而备受关注,如何预测黄金价格成为理论和实证研究的重要课题。对于黄金价格预测的研究,目前较多的使用多元线性回归模型,由于多元线性回归模型假设变量对黄金价格的影响在整个时间上不变,这显然不符合实际的情况。针对该问题,本文选用变系数回归模型对黄金价格进行预测,能够动态的反应各个变量对黄金价格的影响力,极大的提高了预测精度。影响黄金价格的因素众多而且纷杂,本文主要从美元指数、大宗商品价格、欧美股票市场、亚太股票市场和世界经济形势等方面对黄金价格的影响因素进行了相关性分析,并找出了其主要影响因素。最终选取美元指数、石油价格、白银价格、道琼斯指数、OECD领先指数和CRB指数为黄金价格的主要影响因素,把它们作为变系数模型的变量。另外,本文采用加权最小二乘法对参数进行估计,更正了传统的最小二乘法假设样本数据对预测点的权重都相等这一缺陷,使得离预测点越近的样本的权重值越大。在权重函数的选择上,本文利用交叉互证法,以1990年1月至2009年12月的黄金价格数据为样本,选取不同的光滑参数,求出其对应的残差平方和值,计算最小残差平方和对应的光滑参数值,实现整个样本空间的最优。然后利用变系数回归模型和多元线性回归模型分别模拟预测了2000年1月至2009年12月的黄金价格,通过分析发现,变系数回归模型的残差平方和小于多元线性回归模型,并且后者的误差率常常明显高于前者。因此本文利用变系数回归模型预测了2010年1月至12月的黄金月价格,所得结论具有较重要的理论和实际应用价值。更多还原

【Abstract】 For a long period, gold has attracted much attention as a measure of value, circulation means, storage means, payment, world monetary functions and so on, how to predict the gold price becomes an important academic and practical research topic. For the research of gold price forecast, the multiple linear regression model has been greatly studied at present, as it assumes that the impacts of variables on the price of gold remain invariability throughout time, which obviously does not accord with the actual situation. To avoid this problem, varying-coefficient regression model is applied to predict the gold price in this paper, the model has dynamic response to the various variables influence the price of gold, so it has greatly improved the prediction accuracy.Many factors affect the price of gold and they are confused, the articles mainly explored correlation analysis in U.S. dollar index, commodity prices, European stock markets, the Asia-Pacific stock markets and world economic situation and other aspects of the factors affecting the gold price, and identify the main factors. Finally U.S. dollar index, oil prices, silver prices, DOW index, OECD leading index and the CRB index are chosen as main factors affecting the price of gold, and regard them as variable coefficient model parameters.In addition, the weighted least squares is adopted as an estimation of the parameters, corrects the traditional least squares method defect which assumes the sample data weights equal points to the prediction, making sample weights larger closer with prediction points. In the choice of weighting function, the paper uses cross validation, chooses the gold price data from January 1990 to December 2009 as the sample, selects different smoothing parameters, and calculates the corresponding residual sum of squares values and the smoothing parameter value corresponding minimum residual sum of squares, to achieve the optimization for the entire sample space. Then predicts gold prices from January 2000 to December 2009 used the variable coefficient regression model and multiple linear regression model to simulate, the analysis found that the residual sum of squares of varying-coefficient regression model is less than multiple linear regression model, and the latter’s error rate is often higher than the former. So this article predicts the 12 months gold prices from January 2010 December 2010 applies varying-coefficient regression model. The results have much theoretical and practical value.更多还原