Volatility spillover of energy stocks in different periods and clusters based on structural break recognition and network method

Abstract

This paper investigates the volatility spillover among multiple energy stocks in different periods and clusters (the period of similar fluctuation) by employing the Toeplitz inverse covariance-based clustering method (TICC) and network method. Specifically, we investigate the spillover effect among energy stocks in periods and compare that in the same clusters under different events to reveal the characteristics of spillover networks among energy stocks. Our empirical results are as follows. First, stock price fluctuations of ten years can be divided into 11 periods and 9 clusters, each of which is closely related to the contemporaneous major event. Second, the volatility of energy stocks clearly varies in different periods and clusters from several aspects, policymakers should pay attention to the impact extent of the event and choose energy stocks with strong spillover effects to control to stabilize the market. Finally, despite energy stocks have similar fluctuations in the same clusters, the spillover effects on other stocks are distinct. This study is helpful for analyzing the volatility spillover in different periods as well as providing recommendations for risk reduction of different periods.