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

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 (R2) 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.