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

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.

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.