Electricity Spot Prices Forecasting Using Stochastic Volatility Models

TL;DR

This paper develops and tests stochastic volatility models with exogenous regressors to improve probabilistic day-ahead electricity price forecasts, demonstrating enhanced accuracy and robustness for risk management in trading.

Contribution

It introduces an enriched stochastic volatility model with exogenous regressors for electricity prices, improving forecast accuracy over baseline models.

Findings

Enhanced model fits the data better than baseline models.

Out-of-sample forecasts show robustness and applicability.

Model can be used for risk hedging in electricity trading.

Abstract

There are several approaches to modeling and forecasting time series as applied to prices of commodities and financial assets. One of the approaches is to model the price as a non-stationary time series process with heteroscedastic volatility (variance of price). The goal of the research is to generate probabilistic forecasts of day-ahead electricity prices in a spot marker employing stochastic volatility models. A typical stochastic volatility model - that treats the volatility as a latent stochastic process in discrete time - is explored first. Then the research focuses on enriching the baseline model by introducing several exogenous regressors. A better fitting model - as compared to the baseline model - is derived as a result of the research. Out-of-sample forecasts confirm the applicability and robustness of the enriched model. This model may be used in financial derivative instruments for hedging the risk associated with electricity trading. Keywords: Electricity spot prices forecasting, Stochastic volatility, Exogenous regressors, Autoregression, Bayesian inference, Stan