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Step‐ahead spot price densities using daily synchronously reported prices and wind forecasts

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  • Per B. Solibakke

Abstract

This paper uses non‐linear methodologies to follow the synchronously reported relationship between the Nordic/Baltic electric daily spot auction prices and geographical relevant wind forecasts in MWh from early 2013 to 2020. It is a well‐known market (auctions) microstructure fact that the daily wind forecasts are information available to the market before the daily auction bid deadline at 11 a.m. The main objective is therefore to establish conditional and marginal step ahead spot price density forecast using a stochastic representation of the lagged, synchronously reported and stationary spot price and wind forecast movements. Using an upward expansion path applying the Schwarz (Bayesian information criterion [BIC]) criterion and a battery of residual test statistics, an optimal maximum likelihood process density is suggested. The optimal specification reports a significant negative covariance between the daily price and wind forecast movements. Conditional on bivariate lags from the SNP information and using the known market information for wind forecast movements at t1, the paper establishes one‐step‐ahead bivariate and marginal day‐ahead spot price movement densities. The result shows that wind forecasts significantly influence the synchronously reported spot price densities (means and volatilities). The paper reports day‐ahead bivariate and marginal densities for spot price movements conditional on several very plausible price and wind forecast movements. The paper suggests day‐ahead spot price predictions from conditional and synchronously reported wind forecasts movements. The information should increase market participants spot market insight and consequently make spot price predictions more accurate and the confidence interval considerably narrower.

Suggested Citation

  • Per B. Solibakke, 2022. "Step‐ahead spot price densities using daily synchronously reported prices and wind forecasts," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(1), pages 17-42, January.
    • Handle: RePEc:wly:jforec:v:41:y:2022:i:1:p:17-42
    DOI: 10.1002/for.2759
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