基于时间序列的上海期铜价格短期预测模型研究

发布时间：2018-01-03 05:07

本文关键词：基于时间序列的上海期铜价格短期预测模型研究　出处：《中国社会科学院研究生院》2014年硕士论文　论文类型：学位论文

【摘要】：随着中国工业生产的有色金属铜和更广泛的需求，国内铜消费量一直居世界首位，铜期货已逐渐成为一个重要的投资和对冲工具。然而，，由于各种因素的干扰，频繁波动的铜市场，价格走势很难预测，增加期货交易的风险。关于管辖的运行规律和未来趋势铜价有效地把握能够发现和期货投资者要求提供补充资料，并避免在一定程度上国际贸易的风险。本文旨在探讨内部工作规则铜价的影响铜价运行和建立时间序列模型模型来预测铜的未来价格，为了能够给国内铜生产商的基本因素进行了简要分析，并投资者提供有价值的决策。本文回顾了时间序列分析的基本方法，时间序列模型引入了确定性和随机性时间序列模型。从自回归模型（AR模型），移动平均模型（MA模型），自回归移动平均模型（ARMA模型），自回归移动平均模型（ARIMA模型）的演变，并强调效果的ARCH序列分析，逻辑推理高频时的作用。最后，使用铜日常数据和月度数据，分别采用该方法，该方法以确定与随机模型相结合对中国铜的价格进行了模拟数据趋势GARCH模型的类型，实证结果表明，该预测模型具有更好的预测结果。
[Abstract]:With China ' s industrial production of nonferrous metals copper and more extensive demand , domestic copper consumption has been the first place in the world , copper futures has become an important investment and hedging instrument . However , due to various factors such as the interference of various factors , frequent fluctuation of the copper market , the price trend is very difficult to predict , and to avoid the risk of international trade to a certain extent . The purpose of this paper is to discuss the influence of the copper price on the internal working rules and to establish the time series model model to predict the future price of copper . In order to be able to make a brief analysis on the basic factors of domestic copper producers , and provide valuable decisions for investors . This paper reviews the basic methods of time series analysis , introduces the deterministic and stochastic time series models from the time series model . From the self - regression model ( AR model ) , the moving average model ( MA model ) , the self - regressive moving average model , the self - regressive moving average model , the function of the effect is emphasized . Finally , using the daily data and the monthly data of copper , this method is used to simulate the price of copper in China . The results show that the forecasting model has better prediction results .

【学位授予单位】：中国社会科学院研究生院
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：F724.5;F764.2;F224

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