基于局部均值分解的高频金融数据波动率估计

为解决高频数据在风险评估中存在的非线性问题，提出了利用局部均值分解方法实现高频数据波动率估计。首先，采用高频模拟数据验证估计方法的可行性; 其次，将沪深300 指数不同频率收盘价作为研究对象，利用局部均值分解方法估计波动率，计算相对误差统计量。实验结果表明，利用局部均值分解方法可以有效实现高频数据波动率估计和解决高频数据中的非线性问题，随着抽样频率的增加，估计精度逐渐提高。该方法为高频数据波动率非参数估计提供了新的研究思路。

Volatility Estimation of High Frequency Financial Data Based on Local Mean Decomposition#br#

In order to solve the problem of nonlinearity in high frequency data，the LMD ( Local Mean Decomposition) method is proposed to estimate the volatility of high frequency data． Firstly，high-frequency simulation data are used to verify the feasibility of the estimation method． Secondly，taking the closing price of Shanghai and Shenzhen 300 index at different frequencies as the research object，the volatility is estimated by LMD method，and the relative error statistics are calculated． The results show that the LMD method can effectively estimate the volatility of high-frequency data and solve the nonlinear problems in high frequency data．With the increase of sample frequency，the estimation accuracy gradually improves． The method of LMD provides a new idea for non-parametric estimation of volatility in high frequency data．