Empirical analysis of the correlation between China’s Macroeconomic Market and Crude Oil Market based on mixed-frequency group factor model

Abstract

This paper examines the asymmetric correlation and dynamic interaction between China’s macroeconomic market and the global crude oil market, addressing a critical limitation in existing literature: the frequency mismatch between high-frequency (daily) crude oil data and low-frequency (monthly) macroeconomic data. To resolve this, we employ a mixed-frequency group factor model that decomposes volatility drivers into two mutually exclusive components: (1) common factors, which capture cross-market spillovers between the two markets; and (2) group-specific factors, including low-frequency (LF)-specific factors (for China’s macroeconomic indicators) and high-frequency (HF)-specific factors (for crude oil prices). Our empirical analysis uses a comprehensive dataset spanning January 2005 to March 2024, covering 11 daily crude oil price indicators and 60 monthly Chinese macroeconomic indicators. We validate results using the adjusted coefficient of determination ( R 2 ) and Bayesian Information Criterion (BIC) for model selection, and further test robustness across three samples: a full sample (2005.01–2024.03) and two crisis sub-samples (2007.01–2009.12 Financial Crisis, 2020.01–2023.12 COVID-19). Three core findings emerge: First, the two markets exhibit strong asymmetric influence; Second, the correlation is time-varying and crisis-sensitive; Third, factors show long-term persistence. These results confirm that crude oil acts as a key external constraint on China’s macroeconomic stability, while China’s macroeconomic conditions have limited impact on global oil pricing—consistent with its status as a “price taker” in the global crude oil market. The study provides empirical support for policymakers to design targeted risk-mitigation strategies and for market participants to optimize oil-related investment and risk management.