金融市场的协动预测模型:DWT-SVM方法  

DWT-SVM for Co-Movement Prediction with Financial Markets

作　　者：韩璐[1] 苏治[2,3] 刘志东 HAN Lu;SU Zhi;LIU Zhidong(School of Management Science and Engineering,Central University of Finance and Economics,Beijing 100081;School of Statistics and Mathematics,Central University of Finance and Economics,Beijing 100081;School of Finance,Central University of Finance and Economics,Beijing 100081)

机构地区：[1]中央财经大学管理科学与工程学院,北京100081 [2]中央财经大学统计与数学学院,北京100081 [3]中央财经大学金融学院,北京100081

出　　处：《系统科学与数学》2020年第12期2342-2356,共15页Journal of Systems Science and Mathematical Sciences

基　　金：国家自然科学基金项目(71971226,71673315);国家哲学社会科学基金重大项目(15ZDC024)资助课题。

摘　　要：金融市场由于其复杂的行为机制导致了其在走势预测上的困难.目前学术界对金融市场的走势预测,已经从单一市场预测过渡到协动预测的讨论上.文章通过对布伦特原油指数、Arca天然气指数、道琼斯工业指数和深圳成指四种价格指数的分形建模发现这四种指数都具有短暂高阶稳定的特征;进而,通过连续小波变换的多尺度分析,研究了这四种指数的协动关系,在协动关系的基础上,分离出稳定域,标记了协动相位.最后,采用离散小波变换和支持向量机对协动相位构建了预测模型.通过实验可以发现基于小波分析的支持向量机模型对金融市场的走势预测有较好的效果.In this paper,we analyze the inherent evolutionary dynamics of financial and energy markets,study their interrelationships and carry out predictive analysis tasks in an integrated nonparametric framework.We consider the daily closing prices of BRENT Index,Arca Natural Gas price,DJIA Index,and SZSE Index during January 2012 to January 2017 for this purpose.Firstly,we investigate the empirical characteristics of the underlying temporal dynamics of the financial time series through technique of nonlinear dynamics to extract the key insights.Results suggest the existence of strong trend component and long-range dependence as the underlying pattern.Then we apply the continuous wavelet transformation based multi-scale exploration to investigate the co-movements of the considered assets.Long and medium range co-movements among the heterogeneous assets are discovered.The findings of dynamic time varying association reveal these financial assets have long relation lagging effects.Finally,we employ a hybrid model incorporating discrete wavelet transformation with support vector machine algorithms for forecasting the future trends.Statistical analysis of predictive performance justifies the usage of DWT-SVM,which can effectively be used for trading purposes.

关 键 词：金融市场协动性 小波变换 递归量化分析 支持向量机 

分 类 号：F831.5[经济管理—金融学] F224