Research on Systemic Financial Risk Measurement and Early Warning Model Based On TVP-SV-VAR Model

The global financial crisis has highlighted the significant impact that systemic financial risks can have on economic stability. As globalization and financial liberalization progress the speed and extent of financial risks are expanding. Despite China’s vast economic potential risks such as the real estate bubble and stock market fluctuations cannot be overlooked making it especially important to build an effective financial risk early warning system. This study uses the time-varying parameter state-space model (TVP-SV-VAR) and the Markov chain Monte Carlo method (MCMC) to conduct an empirical analysis of economic and financial data from the United States between 1995 and 2023 aiming to explore the time-varying effects of asset return volatility on financial risk. The study also incorporates the consumer confidence index to examine how expected factors influence financial risk and applies the support vector machine (SVM) algorithm to construct a financial risk assessment model. The findings show that there is a significant nonlinear and time-varying relationship between asset return volatility and financial risk and that monetary policy and consumer confidence have a substantial effect on this relationship. The impact of asset return volatility on financial risk varies under different conditions. This research offers new insights into improving China’s financial risk early warning system by enhancing monetary policy regulation optimizing financial supervision and boosting public confidence to effectively reduce systemic financial risks and support the healthy and stable development of the economy.