Abstract: In recent years, systemic financial risks frequently break out, which causes great attention from scholars at home and abroad. At present, most scholars are concerned with the spillover effects between two sectors from a static perspective. To uncover the spillover effect among sectors in China, the industry indexes of Shenwan Index are selected, which includes 28 sectors as the research object. Firstly, from static and dynamic perspectives, the characteristics of systemic financial risk spillover effect of economic sectors in different time scales and time periods are studied by using the maximum overlap discrete wavelet transform method and the spillover index method. Then, the networks of risk spillover effect in different time scales and time periods are constructed to analyze its topology evolution laws. Experimental results are as follows: the level of risk spillover of economic sectors is relatively high, and the connectedness of the overall market is strong; the main risk spillover sectors include the national defense industry, agriculture, forestry, animal husbandry, fishery, and non-ferrous metals at the original scale; the main risk acceptance sectors involve capital markets, chemical, electromechanical, service industries, and biopharmaceutical industry; the characteristics of risk spillover effect of economic sectors vary significantly under different time scales, and the risk spillover effect tends to be weak with the increase in time scales; the risk spillover effect of endogenous crisis is higher than that of exogenous crisis; the risk spillover effect favors for the sectors in the same community. Our conclusions provide theoretical supports for decision-makers. Regulators should pay more attention to the main risk spillover sectors under different time scales and protect the main risk acceptance sectors to make sure they run smoothly. For investors, it is necessary to recognize the risk spillover characteristics of industries in different time scales, evaluate the impact of the main risk spillover industries, and make rational investments.

Key words: wavelet transform； industry indexes； volatility spillover； topology analysis； complex network