Forecasting Chinese Macroeconomy with Volatility Connectedness of Financial Institutions

ABSTRACT Systemic risk emphasizes the impact on the real economy and is popularly measured by a network interconnectedness approach. We test, for the first time, whether the volatility connectedness of financial institutions is a significant predictor of Chinese macroeconomy. The connectedness is derived from volatility spillover networks and is measured by total connectedness introduced in Diebold and Yilmaz (2014), which reflects the effects of risk transmission and systemic risk in the financial system. Both in-sample and out-of-sample analyses show that an increase in total connectedness among financial institutions stably and strongly forecasts a slowdown in China’s economic activity over the next three to twelve months, when controlling for many factors. Furthermore, including the total connectedness into the regression models improves the macroeconomy forecasts accuracy. Our results are robust to alternative measures of total connectedness.