Forecasting Day-Ahead Electricity Prices with Stochastic Volatility

Summary

This research develops probabilistic forecasts for day-ahead electricity spot prices using stochastic volatility models. It treats prices as a non-stationary time series whose variance changes over time, with volatility represented as a latent stochastic process in discrete time. The study first explores a baseline stochastic volatility model, then extends it by adding several exogenous regressors.

The description reports that the enriched model fits better than the baseline and that out-of-sample forecasts support its applicability and robustness. It does not identify the market, the regressors, the forecast evaluation measures, or the size of the improvement, so the evidence cannot be independently assessed from the summary provided. The authors suggest the model could inform financial derivatives used to hedge electricity trading risk. That application is a proposed use, and the description does not provide details on derivative design, hedge performance, or how well forecasts transfer to other power markets.

Key ideas

• The study forecasts day-ahead electricity spot prices with probabilistic stochastic-volatility models.
• Its baseline represents volatility as a latent process that changes through time.
• Adding exogenous regressors produces a better-fitting model than the baseline in the reported research.
• Out-of-sample forecasts are described as supporting the enriched model’s applicability and robustness.
• Forecasts may support hedging through electricity derivatives, though hedge results are not provided.

Tags

# Electricity Spot Prices Forecasting Using Stochastic Volatility Models


# Electricity Spot Prices Forecasting Using Stochastic Volatility Models









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

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