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

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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
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