Electricity price forecast based on weekly weather forecast and its application to arbitrage in the forward market

Abstract

This study constructs multiple models for forecasting weekly average electricity prices using weekly weather forecasts, and applies it to arbitrage trading in the forward market. In particular, we compare models that used different approaches for forecasting weekly average price and price density, and clarify the following empirical results using the data from Japan Electric Power Exchange (JEPX): 1) Instead of using forecasted temperature directly as an explanatory variable, the two-step forecast method with measured temperature is more likely to reduce the forecast error; 2) Quantile regression has better density forecast accuracy than a GARCH based model; 3) The multiplicative model using the logarithmic price series tends to have higher forecast accuracy than the additive model without logarithmic conversion; 4) Weather forecasts contribute to improving the forecast accuracy of weekly electricity prices and also play an important role in earning profits in forward market trading. The proposed arbitrage trading method can be utilized by many participants in that the strategy can be flexibly changed according to the level of risk tolerance. The existence of considerable arbitrage opportunities in the JEPX forward market, revealed by the empirical results, may have practical implications on attracting traders and stimulating the market.