广东工业大学学报 ›› 2018, Vol. 35 ›› Issue (05): 26-30.doi: 10.12052/gdutxb.180066

• 综合研究 • 上一篇    下一篇

金融大数据中条件非相关波动模型的单纯形搜索算法

白颉1, 姚家进2, 张茂军2, 李桥兴3   

  1. 1. 太原学院 数学系, 山西 太原 030032;
    2. 桂林电子科技大学 数学与计算科学学院, 广西 桂林 541004;
    3. 贵州大学 管理学院, 贵州 贵阳 550025
  • 收稿日期:2018-03-30 出版日期:2018-07-10 发布日期:2018-07-18
  • 作者简介:白颉(1978-),女,讲师,硕士,主要研究方向为金融优化.
  • 基金资助:
    国家自然科学基金资助地区项目（71461005）；贵州大学文科重大科研项目（GDZT201604）

A Simple Search Algorithm on Conditionally Uncorrelated Volatility Models in Financial Big Data

Bai Jie1, Yao Jia-jing2, Zhang Mao-jun2, Li Qiao-xing3   

  1. 1. Department of Mathematics，Education Institute of Taiyuan University, Taiyuan 030032, China;
    2. School of Mathematics and Computing Science, Guilin University of Electronic Technology, Guilin 541000, China;
    3. School of Management, Guizhou University, Guiyang 550025, China
  • Received:2018-03-30 Online:2018-07-10 Published:2018-07-18

HTML

PDF (PC)

2681

摘要： 针对金融大数据中多维金融资产相关性计算的降维问题，提出了求解条件非相关波动模型的单纯形搜索优化算法.该算法极大地提高了估计参数的速度和精度.为了验证算法的有效性，检验了股票市场、债券市场、基金市场、外汇市场与期货市场的条件非相关性问题.本文的研究方法为金融大数据相关分析提供了新方法.

关键词: 条件不相关波动模型, 金融大数据, 单纯形搜索算法

Abstract: The issue of reduction dimension about the correlation of multivariable financial assets in financial big data is analyzed. A simple search algorithm is developed to compute conditionally uncorrelated volatility models, which greatly improves the speed and precision of the estimation parameters. In order to verify the validity of the algorithm, the conditional uncorrelation between stock market, bond market, fund market, foreign exchange market and futures market is tested. The results show that the algorithm provided is very effective to solve the CUC model, and the correlations between the stock market and the other markets is negative or positive. The research provides a new method for financial big data correlation analysis, which has important theoretical significance and application value.

Key words: conditionally uncorrelated volatility models, financial big data, simple search algorithm

中图分类号: 

  • TP333
[1] ENGLE R F. Autoregressive conditional heteroskedasticity with estimates of the variance of UK inflation[J]. Econometrica, 1982, 50(4):987-1008
[2] BOLLERSLEV T. Generalized autoregressive conditional heteroskedasticity[J]. Journal of Econometrics, 1986, 31(3):307-327
[3] BOLLERSLEV T. Modeling the coherence in short-run nominal exchange rates:a multivariate generalized ARCH model[J]. The Review of Economics and Statistics, 1990, 72(3):498-505
[4] ENGLE R F. Dynamic Conditional correlation:a simple class of multivariate generalized autoregressive conditional heteroskedasticity models[J]. Journal of Business and Economic Statistics, 2002, 20(3):339-350
[5] CREAL D, LUCAS A. A dynamic multivariate heavy-tailed model for time-varying volatilities and correlations[J]. Journal of Business & Economic Statistics, 2011, 29(4):552-563
[6] ZHANG X,CREAL D,KOOPMAN S J, et al. Modeling dynamic volatilities and correlations under skewness and fat tails:2011 Tinbergen Institute Discussion Paper:11-078/2/DSF22[R/OL].(2011-05-11)[2017-12-10].http://dx.doi.org/10.2139/ssrn.1920839
[7] FAN J, WANG M, YAO Q. Modelling multivariate volatilities via conditionally uncorrelated components[J]. Journal of the Royal Statistical Society, 2008, 70(4):679-702
[8] 王明进,陈奇志. 基于独立成分分解的多元波动率模型[J]. 管理科学学报, 2006, 9(5):56-64 WANG M J, CHEN Q Z. Multivariate volatilities modeling based on independent components[J]. Journal of Management Sciences in China, 2006, 9(5):56-64
[9] 孟庆浩,张卫国. 基于ICA的多元金融市场波动溢出及实证研究[J]. 系统工程, 2015, 33(10):115-121 MENG Q H, ZHANG W G. Volatility spillover effect and empirical study on multi-financial markets based on independent component analysis[J]. Systems Engineering, 2015, 33(10):115-121
[10] 赵丽丽,张波. 基于改进ICA模型的高维波动率估计[J]. 数理统计与管理, 2017, 36(1):38-50 ZHAO L L, ZHANG B. Estimation of high dimension volatility based on improved ICA model[J]. Journal of Applied Statistics and Management, 2017, 36(1):38-50
[11] 李桥兴,强保华,杨春燕. 大数据基元的HBase数据库存储模型与实现[J]. 广东工业大学学报, 2014, 31(3):8-13 LI Q X, QIANG B H, YANG C Y. The storage model of big data basic-elements in HBase database and its realization[J]. Journal of Guangdong University of Technology, 2014, 31(3):8-13
No related articles found!
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
版权所有 © 2017 《广东工业大学学报》编辑部
地址：广州市东风东路729号广东工业大学 邮编：510090
电话：020-37626653,020-37626139,020-37626396
邮箱：xbzrb@gdut.edu.cn
本系统由北京玛格泰克科技发展有限公司设计开发