Abstract
Fluctuations in energy prices impact production costs and inflation. This study examines whether inflation data can predict volatility in energy markets. Both inflation and energy market volatility exhibit complex behaviour over time, including structural shifts due to demand and supply shocks. Accounting for differences in data frequencies, we use an extended GARCH model (MIDAS) with Laguerre polynomials for time-varying parameters. The empirical results demonstrate that including low-frequency inflation data enhances energy model predictability particularly during periods of high volatility and extreme price fluctuations. Considering inflation improves forecasting for energy market models, benefiting portfolio management and helping policymakers manage inflation.
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