Journal of Guangdong University of Technology ›› 2018, Vol. 35 ›› Issue (05): 26-30.doi: 10.12052/gdutxb.180066

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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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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

CLC Number: 

  • TP333
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