AI Computing Demand and Energy Market Co-movement: Evidence from EGARCH-X and DCC-GARCH Models

Abstract

This paper examines whether the expansion of AI-related computing demand has created a new financial linkage between major technology equities and energy infrastructure assets. Using 817 daily observations from March 2023 to July 2026, we estimate an EGARCH(1,1)-X model augmented with the MOVE Index as an exogenous variance regressor (Stage 1), and a bivariate DCC-GARCH model (Engle, 2002) on GARCH-filtered standardized residuals (Stage 2) for seven asset pairs spanning AI equities (NVDA, MSFT, SOXX) and energy infrastructure (URA, XLU). The MOVE Index coefficient δ is statistically significant for NVDA (δ = 0.023, p < 0.01), SOXX (δ = 0.030, p < 0.001), and XLU (δ = 0.018, p < 0.001), confirming macrofinancial stress transmission to AI-equity volatility. DCC-GARCH estimates reveal a significant increase in NVDA–URA co-movement (Δρ = +0.175, p < 0.001) and SOXX–URA co-movement (Δρ = +0.108, p < 0.01) following July 2024, while the control pair URA–XLU shows no structural break (Andrews sup-Wald p = 0.597). These findings suggest an AI-specific, rather than broad-market, origin for the documented co-movement increase. Policy implications for energy infrastructure investment and systemic risk monitoring are discussed.

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