Forecasting of volatility and risk premia in electricity markets

2026-06-04

AI Summary

This study addresses the challenge of accurately forecasting realized covariance and forward risk premia in electricity markets. It introduces, for the first time, a matrix heterogeneous autoregressive (HAR) model into high-dimensional power markets, establishing a parsimonious framework to predict weekly realized covariance by integrating multi-scale historical volatility and renewable generation data. This approach substantially enhances covariance forecast accuracy and, in turn, improves risk premium estimation. Compared to conventional methods based on historical realized volatility, the proposed model delivers significantly superior performance in both realized covariance and spread risk premium forecasts, highlighting its innovative capacity to capture high-dimensional dynamic covariance structures and effectively incorporate exogenous information.

Abstract

We study forecasting of the realized covariation in electricity markets. The realized covariation in this context is a matrix-valued representation of the latent infinite-dimensional covariance operator and a parsimonious matrix-HAR type model is constructed to facilitate estimation. We test the model on one-week ahead forecasts of the weekly realized covariation and find that the inclusion of longer time horizons and renewable generation information adds important predictive power. We also investigate the prediction of risk premia in electricity forward markets and find that our variance forecasts provide substantially improved forecasts of spread risk premia compared to standard methods relying on backward looking volatility.

Problem

• volatility forecasting
• risk premia
• electricity markets
• realized covariation
• forward markets

Innovation

• matrix-HAR
• realized covariation
• electricity markets
• risk premia forecasting
• renewable generation