Abstract

In this paper, based on results of the volatility of stock returns after the Hilbert Huang Transform, to research the influential factors of volatility composition, the influential factor model of yield volatility is established. This model studies the volatility from three angles respectively: the hysteresis of impact, the influence degree and the affect correlation. For the hysteresis of impact, this paper uses the  model to determine lag phases of different IMF of volatility. For the influence degree, after using principal component analysis to eliminate the multicollinearity between different IMF, we calculate direct contribution, correlation coefficient and variable coefficient to quantify the influence degree of IMF on RV, BV and JV, the independence degree and the information abundance. For affect correlation, this paper adopts four different distance calculating methods and grey correlation method to depict the connection degree between RV and IMFin different dimensions. Finally, this paper uses the data of China's financial markets to carry on the empirical analysis, and explores various characteristics of realized volatility through comprehensive influence degree, in order to provide new perspectives and ideas for financial analysis and forecast. provide new perspectives and ideas for financial analysis and forecast.

 

This work is licensed under a Creative Commons Attribution 4.0 License.