Time Series Analysis of Regional GDP and Stock Market Volatility Using ARIMA and ARMA-GARCH Models

This study applies ARIMA and ARMA-GARCH models to analyze quarterly GDP data (2007-2019) for Beijing, Tianjin, and Hebei, and daily Shanghai Composite Index prices (2002-2021). The ARIMA(1,0,0)(0,1,0) model demonstrates predictive accuracy for regional GDP (MAE = 8.5%), while the ARMA(0,0,3)-GARCH(1,1) model effectively captures the "peak and fat tail" characteristics of stock returns. Findings provide actionable insights for economic policymakers and financial risk managers, highlighting the models' effectiveness in addressing non-stationarity and volatility clustering in economic and financial time series.