Learning how the financial risk has been transmitted between industries in China before and after some big events is a crucial topic in risk manage- ment. Due to the large number of industries in China, the market can be regarded as a high-dimension network. To conduct the empirical research on risk transmission mechanism in China based on daily data of the SWS (Shen Wan Security) secondary industry index, I utilize the LASSO algorithm to compress, select and estimate variables and build a high dimensional VAR model and calculate the pairwise risk connectedness between different in- dustries. With the help of network analysis, I visualize the outcome of the VAR-Lasso model. Then, both full sample estimation and rolling window estimation are applied to provide static analysis and dynamic analysis on the risk transmission network. Based on the analysis, clustering character- istics can be easily found in risk transmission of the market, especially be- tween industries in the same industrial chain or between industries that are closely connected. Particularly, Oil exploitation, Insurance, Banking, and Railway Transportation are functioning as an efficient intermediary nodes in the whole transmission process. As for dynamic analysis, the overall risk connectedness reaches the summit during the great stock crisis in 2015 and the shock of COVID-19. Comparisons are made on the risk transmission network before and after those big events.




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