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| A Study of Financial Risk Contagion from the Volatility Spillover Network Perspective |
| GONG Xiaoli, XIONG Xiong
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| College of Management and Economics, Tianjin University; China Center for Social Computing and Analytics |
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Abstract Following the subprime mortgage crisis, China's financial market experienced a money shortage in 2013, a stock market crash in 2015, and a bond default crisis in 2018. Due to this turmoil in the domestic and international economic environment, the risk spillover effects of China's financial system have received much attention. As the cross-market and cross-region contagion of financial risks has grown, systemic risk warning and prevention have become the focus of research. Therefore, studying the risk contagion of financial volatility in this context not only provides guidance for regulators on maintaining financial stability and preventing financial crises stemming from systemic risk accumulation, but also has significant implications for investors' portfolio risk management.
To describe the financial systemic risk transmission path and characteristics in China, this paper analyzes the internal risk contagion mechanism of the financial system from the perspective of a volatility spillover network. The risk spillover relationship between markets is used as the connected edges of the network, and the variance contribution (as calculated by the variance decomposition) is used as the adjacency matrix. Next, the complex network of risk spillovers in China's financial system is constructed, and the risk contagion features of China's financial system can be analyzed using the topological structure of the network. We first use the general dynamic factor model to distinguish between the common volatility components of financial return volatility and the idiosyncratic volatility components. Next, based on the volatility spillover effects between the currency market, capital market, commodity trading market, foreign exchange market, real estate market, and gold market, the dynamics of the volatility spillover of the variance decomposition index based on TVP-VAR model are analyzed, and the linkage and risk transfer mechanisms are discussed. After analyzing the directional volatility spillover effects, the variance decomposition network method is used to construct the weighted and directed volatility information spillover network, and the risk contagion characteristics of the financial system network are described from the perspective of the network. Subsequently, the dynamic conditional correlation generalized autoregressive conditional heteroskedasticity (DCC-GARCH) tcopula model is utilized to describe the relationship between the nonlinear volatility index variables, and the optimal portfolio weight and hedging strategy of the volatility index portfolio are calculated. The framework is used for empirical research on different sub-markets within the Chinese financial system.
The research sample comprises the explanatory variable indicators of the 12 secondary financial sub-markets of the Chinese financial system from January 2007 to October 2018, based on data obtained from the Wind database. The empirical study reveals that the absolute values of the net spillover effects of the real estate market and the foreign exchange market are larger than those of other markets. However, due to the comprehensive influence of various factors such as the macroeconomic situation and regulatory policies, the risk spillover effects of the currency market show uncertainties. Although the stock market displays a high degree of risk spillover and risk tolerance, the net spillover effect is not strongly evident, indicating that the stock market plays the main role in transmitting risks between other financial markets.
On the basis of volatility spillovers, we further consider the asset allocation weight of portfolios using stock market volatility indexes and other market volatility indexes. The research conclusions are helpful for understanding the risk contagion mechanism of the financial system, making it possible to strengthen macro-prudential supervision and avoid investment risks.
This study makes three major contributions. First, we use GDFM to extract common volatility shock components and the idiosyncratic volatility components of sub-markets in the financial system. Second, the time-varying vector autoregressive variance decomposition spillover index is used to identify the dynamic linkage of volatility shocks between financial systems. Third, based on the asset allocation of portfolios using the stock market volatility index and other market volatility indexes, the DCC-GARCH tcopula model is used to characterize the non-linear heterogeneous volatility series between financial markets and calculate the optimal portfolio strategy for the volatility index based on the volatility spillover effects.
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Received: 28 December 2018
Published: 10 June 2020
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