A Novel Approach to Analyzing Nonlinear Effects of Decomposed Oil Shocks on Global Stock Market Indices: Evidence From Nardl and Wavelet Coherence

Recent literature on oil shocks and stock market (SM) indices has increasingly emphasized the importance of nonlinearities in these relationships. However, limited research has specifically examined the disaggregated effects of oil shocks as classified by Ready (2018) — namely, global oil demand, supply, and risk shocks (RSs) — on international SM indices. This study addresses this gap by exploring the nonlinear and time-frequency interactions between these oil shocks and nine major stock indices from March 1996 to January 2022. Employing the Nonlinear Autoregressive Distributed Lag (NARDL) model and Wavelet Coherence Transform (WTC), the results reveal that positive and negative oil demand shocks (DSs) are associated with increased stock index performance. In contrast, oil supply and RSs generally exert adverse effects. Positive DSs exhibit more substantial economic influence, indicating that rising global oil demand enhances SM returns. Furthermore, wavelet coherence analysis uncovers a robust lead-lag structure in the time-frequency domain, where oil demand and supply shocks (SSs) act as dominant forces behind SM fluctuations. In contrast, oil price risk shows a more ambiguous pattern. This study offers a nuanced approach to understanding asymmetric and region-specific effects of global oil shocks.