Riding out the storm: High-frequency data for enhanced oil market risk forecasting

The stability of global energy markets is fundamental to effective energy strategy, influencing national policy and corporate investment decisions. Extreme volatility, such as during the COVID-19 pandemic, exposes the limitations of conventional risk assessment tools and undermines strategic planning. This paper introduces and validates an enhanced methodological approach for oil market risk forecasting using high-frequency, minute-by-minute market data. We test this methodology against established models that rely on daily price information to demonstrate its analytical value. The evaluation focuses on forecasting Value at Risk, a critical metric for risk assessment. Our findings reveal three key insights: (1) intraday data captures essential market dynamics that daily data overlooks; (2) increased model complexity does not necessarily improve forecasting accuracy; and (3) high-frequency data approach consistently outperform traditional counterparts. These findings have significant implications for energy strategy and policy. By providing more accurate and robust forecasting capabilities, this methodology enables energy corporations, investors, and policymakers to make better-informed decisions regarding capital allocation, hedging strategies, and infrastructure investment, ultimately contributing to enhanced energy market stability and security. • High-frequency data improves oil market risk forecasting. • Data-rich, simpler models outperform complex daily-data models. • Intraday data models show greater resilience during market crises.