LAUSR

ABSTRACT In this paper, we investigate the comovement between the Chinese business cycle and financial variables from 1994 to 2017 using a dynamic conditional correlation-mixed data sample (DCC-MIDAS) model. We analyze the relation and contagion between the business cycle and financial volatility and then construct a DCC-MIDAS model to capture the dynamic relation between the business cycle and financial volatility. Then, we carry out an empirical analysis, finding comovement in the relation and contagion between the Chinese business cycle and financial volatility. Short-term shocks can influence both long-term relations and variations in the correlation coefficients with a lag. An accumulation of short-term shocks can be transformed into a long-term tendency, which explains the dynamically related long-term effect. Constructing this model with high-frequency data captures more information than using low-frequency data, which reveals more profound patterns in the comovement between the business cycle and financial volatility.