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August 19, 2025Open Access

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

Key Points

ARIMA and ARMA-GARCH models show strong predictive capabilities for GDP and stock market volatility, respectively.
The ARIMA(1,0,0)(0,1,0) model achieved a mean absolute error of 8.5% for predicting regional GDP.
The study utilized quarterly GDP data from 2007-2019 and daily stock prices from 2002-2021, enhancing model reliability.
Findings suggest important implications for economic policymakers and financial risk managers in managing volatility.

Abstract

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.

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

Key Points

Abstract

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