Relationships between digital technology indices and clean energy stocks using quantile and wavelet approaches

Abstract

This study examines the nonlinear relationships between emerging digital technologies and clean energy (CE) stock returns using daily data from July 2, 2021, to June 1, 2026. Quantile regression (QR), cross-wavelet transform (XWT), and wavelet transform coherence (WTC) are employed to analyze the relationships between three CE stock indices (NASDAQ Clean Edge Green Energy Index, S&P Global Clean Energy Index, iShares Global Clean Energy ETF and five indices representing major digital technology segments. The QR results reveal substantial heterogeneity across market conditions. Robotics and artificial intelligence exhibit the strongest and most persistent positive relationships with CE stock returns. The remaining technologies display more heterogeneous effects across market states. Wavelet analysis shows that relationships evolve across time and investment horizons, with the most persistent dependence observed for robotics and artificial intelligence. The findings indicate that digital transformation should not be interpreted as a homogeneous determinant of CE financial markets. Financial markets distinguish among digital technology segments, highlighting the importance of nonlinear approaches for understanding digital innovation in sustainable energy finance.