Extreme Risk Spillover Effects among Financial Submarkets: Evidence from QVAR and TVP-VAR Models
Junxiao Zhao
School of Mathematics and Statistics, North China University of Water Resources and Electric Power, Zhengzhou 450046, China.
Haisong Cao *
School of Mathematics and Statistics, North China University of Water Resources and Electric Power, Zhengzhou 450046, China.
Jiaman Wang
School of Mathematics and Statistics, North China University of Water Resources and Electric Power, Zhengzhou 450046, China.
Xuezi Yang
School of Mathematics and Statistics, North China University of Water Resources and Electric Power, Zhengzhou 450046, China.
*Author to whom correspondence should be addressed.
Abstract
Aims/Objectives: To investigate risk contagion among financial submarkets, which is a core issue for maintaining financial stability, measure extreme risk spillover effects across different market regimes, and reveal the transmission paths and time-varying characteristics of extreme risks, so as to provide empirical evidence for preventing systemic financial risks.
Study Des: An empirical study that combines risk measurement models with quantile and time-varying parameter vector
autoregression models. It compares risk spillovers between normal and extreme downside environments and characterizes the
dynamic evolution of risk spillovers.
Place and Duration of Study: Four core financial submarkets in China (banking, insurance, bonds and stock) are selected,
with daily closing price data covering the period from January 4, 2007 to June 30, 2025 as the research sample.
Methodology: A time-phased dynamic weighting method is first applied to construct a proxy index for the insurance industry
from 2007 to 2012, whose validity is verified through anchor-point backward recursion and rationality checks. Then a TGARCH
model based on the skew-t distribution is employed to estimate VaR and ES measures to capture the leptokurtic, heavy-tailed, and asymmetric features of return series. Furthermore, a QVAR model is constructed to analyze risk spillover effects under extreme downside conditions. Finally, a TVP-VAR model is adopted to calculate the dynamic.total and directional spillover indices, thereby identifying the time-varying characteristics of risk transmission.
Results: The extreme risk measurement results show that the risk ranking of the four markets is: CSI Insurance > CSI Bank > CSI 300 > CSI All-Bond, and risk measures rose significantly during major events such as the 2008 global financial crisis, the 2015 stock market turbulence and the 2020 COVID-19 pandemic. The QVAR model finds that risk co-movement and transmission intensity under the extreme downside regime far exceed those in normal periods, with the total spillover effect reaching 4.58% under the extreme downside regime, more than 10 times the 0.43% observed under normal market conditions. The banking and insurance sectors are core net risk transmitters, while the bond market and CSI 300 mainly act as net risk receivers. The TVP-VAR model indicates that risk spillovers exhibit significant time-varying characteristics: the total spillover index peaked at 40% during the financial crisis and reached 36.67% in the early stage of the pandemic, and the banking sector served as a persistent and dominant risk exporter with an average spillover intensity of 29.2%.
Conclusion: This paper reveals the quantile heterogeneity and time-varying evolution patterns of extreme risk spillovers
among China’s financial submarkets, and clarifies the risk roles and transmission paths of different markets. The findings
provide empirical evidence and policy implications for identifying extreme risk contagion paths among financial submarkets
and preventing systemic financial risks.
Keywords: Financial submarkets, extreme risk spillover, QVAR, TVP-VAR, quantile heterogeneity, risk contagion