Carbon Price Uncertainty-Macroeconomy Mixed-Frequency Spillovers: Evidence from the Frequency-Domain

This paper investigates the dynamic risk spillovers and frequency-domain connectedness between climate-related financial risk, traditional market volatility, and key macroeconomic variables in the overall European Union region. Employing a novel Mixed-Frequency Vector Autoregression with Frequency Domain Decomposition (MF-VAR-FDD) model, we analyze a unique dataset comprising weekly volatility indices for carbon (Carbon VIX), gold (Gold VIX), oil (Oil VIX), and equities (Euro VIX), alongside monthly data for industrial production, the ECB's shadow short rate, and inflation. This methodology allows for a nuanced decomposition of risk transmission across short-term (high-frequency) and long-term (low-frequency) horizons, providing critical insights that are obscured in common-frequency analyses. Our empirical results reveal a distinct asymmetry in the risk network: financial and commodity volatility indicators consistently act as net transmitters of risk, whereas macroeconomic fundamentals are systemic net receivers. The total spillover index is highly time-variant, exhibiting significant spikes that coincide with major economic and geopolitical events. The frequency decomposition further demonstrates that high-frequency (0-3 months) spillovers are predominantly driven by interactions within financial markets, with the Euro VIX playing a central role. Conversely, low-frequency (beyond 3 months) spillovers are more structural, with commodity price volatility (Oil VIX) and monetary policy expectations (ECB SSR) emerging as the largest long-term risk transmitter and receiver, respectively. More importantly, we find the prominent and pervasive role of the Carbon VIX as a source of systemic risk. Across the full sample, the Carbon VIX emerges as the most powerful net risk transmitter, indicating that volatility originating from the carbon market significantly propagates throughout the financial and macroeconomic system. Our findings, robust to alternative model specifications, underscore the imperative for policymakers and investors to integrate carbon market dynamics into their risk management frameworks and highlight the inadequacy of traditional models that ignore mixed-frequency information.