Analyzing the dynamic interactions among environmental finance, energy, and commodity markets using a wavelet coherence approach
Understanding the evolving interdependencies among environmental finance, technology, energy, and commodity markets is essential for effective portfolio diversification, risk management, and sustainable investment decisions. However, these relationships are dynamic and vary across both time and investment horizons. This paper employs Wavelet Coherence methodology to dissect the dynamic, time- and frequency-dependent linkages between key environmental finance indices (Standard & Poor’s [S&P] Global Carbon Efficient Index [CRBN] and S&P Kensho Smart Grids Index [SMOG]) and a comprehensive suite of technology, energy, and commodity assets. Our analysis reveals the distinct economic identities of these indices. We find an exceptionally strong and persistent coherence between CRBN, artificial intelligence, and clean energy across a broad spectrum of frequencies, positioning CRBN as a central node in the modern technology and green investment ecosystem. Furthermore, CRBN exhibits strong long-term coherence with crude oil and copper, indicating its role as a strong proxy for the global business cycle. In contrast, the SMOG index, while also strongly linked to clean energy in the long run, displays a fundamentally different relationship with traditional energy. Its coherence with crude oil is not a long-term phenomenon but manifests as a significant medium-term, crisis-driven linkage, highlighting its sensitivity to cyclical market shocks rather than long-run fundamentals. These findings provide a granular map of the distinct risk exposures and economic drivers of key environmental assets, offering critical insights for horizon-specific asset allocation, thematic investing, and risk management.
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