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.

KEYWORDS:

• Volatility connectedness
• financial institutions
• systemic risk
• predictive regression
• macroeconomy

JEL:

• C53
• E44
• G10
• G20

Disclosure Statement

No potential conflict of interest was reported by the authors.

Notes

1. The framework facilitates study of both crisis and non-crisis episodes, including trends as well as bursts in spillovers (Diebold and Yilmaz 2009), which has been widely used to describe the interconnectedness between financial institutions and systemic risk measurements (Barunik and Kehlík 2018; Demirer et al. 2018; Diebold and Yilmaz 2014; Wang et al. 2018). Considering the nonlinearity in the behavior of financial institutions is indeed an interesting direction for future expansion. Similar to study of Gregoriou, Racicot, and Théoret (2021), the nonlinearity may be considered by nonlinear impulse response.

2. Descriptive statistics of the return volatilities of other financial institutions are similar to those of the above four institutions, thus they have not been presented because of space constraints.

3. The variables are selected with constraints of data frequency and availability. The Macroeconomic Prosperity Index is provided by National Bureau of Statistics of China.

4. According to the following formula in Newey and West (1987): number of lags $q=floor(4\times (\frac{T-n}{100}{)}^{(\frac{2}{9})})$, where n equals 12, corresponding to the number of month lags (denoted n in Equation(9)), and T is 131, corresponding to the 131 months between January 2008 to January 2019, and floor represents the floor function.

5. Considering that the indicator reflecting macroeconomy in our paper is an aggregate indicator obtained by principal component analysis method. Accordingly, this cannot objectively distinguish China’s economic cycle (recession and expansion). Therefore, we rely on the commonly and widely used indicator-industrial production growth (IP)-to identify the China’s cycle states (Wang, Zhang, and Liu 2010; Zhou and Zhang 2019).

Additional information

Funding

This work was supported by the National Natural Science Foundation of China (Nos.71771042 and 72171039), the Fundamental Research Funds for the Central Universities (N180614004 and 22lzujbkydx031), the Project of Humanities and Social Science of Ministry of Education of China (Grant No.18YJCZH224) and the Philosophy and Social Science Planning Project of Gansu Province (2022YB008).