关键词: 状态空间模型, ERM算法, 收益率网络, 波动率网络

Abstract: For the key variables of financial market, yield and volatility, based on the high-dimensional state space model, this paper combines EM and sparse algorithms to establish yield network and volatility network among stocks respectively. The yield network depicts the relationship between stock returns, while the volatility network depicts the relationship between stock risks. Compared with existing models, the proposed model can effectively handle high-dimensional time series data. By analyzing the real data in Shenzhen, Shanghai, Hong Kong and New York stock markets, this paper establishes such networks for these four markets. The most prominent result is that the yield network has a higher degree center potential than the volatility network. This paper attributes the above phenomenon to the effect of policy and other factors on yield simple and direct, while the impact on volatility is indirect and complicated. The above results can also serve as the basis for building the multivariate volatility model.

Key words: state space model, ERM algorithm, yield network, volatility network