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; 姚家进; 张茂军; 李桥兴

doi:10.12052/gdutxb.180066

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金融大数据中条件非相关波动模型的单纯形搜索算法

综合研究 | 更新时间：2026-05-27

- 金融大数据中条件非相关波动模型的单纯形搜索算法
- A Simple Search Algorithm on Conditionally Uncorrelated Volatility Models in Financial Big Data
- 广东工业大学学报 2018年35卷第5期页码：26-30
- 作者机构：
  
  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
- 作者简介：
  
  白颉(1978–)，女，讲师，硕士，主要研究方向为金融优化
- 基金信息：
  
  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)
- DOI：10.12052/gdutxb.180066
  中图分类号： TP333
- 网络首发：2018-07-10，
  
  纸质出版：2018
- 稿件说明：
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白颉, 姚家进, 张茂军, 李桥兴. 金融大数据中条件非相关波动模型的单纯形搜索算法[J]. 广东工业大学学报, 2018, 35(5): 26-30.

Bai Jie, Yao Jia-jing, Zhang Mao-jun, et al. A Simple Search Algorithm on Conditionally Uncorrelated Volatility Models in Financial Big Data[J]. 2018, 35(5): 26-30.
白颉, 姚家进, 张茂军, 李桥兴. 金融大数据中条件非相关波动模型的单纯形搜索算法[J]. 广东工业大学学报, 2018, 35(5): 26-30. DOI： 10.12052/gdutxb.180066.

Bai Jie, Yao Jia-jing, Zhang Mao-jun, et al. A Simple Search Algorithm on Conditionally Uncorrelated Volatility Models in Financial Big Data[J]. 2018, 35(5): 26-30. DOI： 10.12052/gdutxb.180066.

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.

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.

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Keywords

references

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