Forecasting EU Carbon Price Volatility: Distinguishing True Jumps From False Detections in HAR‐Type Models

Yanhua Wu, Hongshuai Dai

Abstract

Accurate volatility forecasting in the European Union Emissions Trading System is crucial for risk management and policy design. However, conventional jump detection methods are prone to false discoveries, which can severely undermine predictive performance. To address this, we apply a universal threshold approach to reliably distinguish statistically significant true jumps from noise‐induced spurious jumps, and develop a suite of extended HAR and LHAR models that incorporate these disentangled volatility components. Using high‐frequency EUA futures data, our empirical analysis shows that models incorporating only true jumps consistently achieve superior in‐sample fit and out‐of‐sample forecast accuracy. In contrast, including false jumps yields no meaningful improvement in predictive performance. These findings underscore that robust jump identification is essential for enhancing volatility forecasts. True jumps are predominantly associated with policy announcements and energy market shocks, whereas false jumps largely reflect market microstructure noise. This study offers empirical insights for carbon market volatility modeling and delivers actionable guidance for risk management and trading strategies.

Source: semanticscholar · PDF

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