Electricity Spot Prices Forecasting Using Stochastic Volatility Models

ArXiv ID: 2406.19405 “View on arXiv”

Authors: Unknown

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

Keywords: Stochastic Volatility, Electricity Price Forecasting, Exogenous Regressors, Bayesian Inference, Autoregression, Energy

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 8.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced Bayesian inference and stochastic volatility modeling with mathematical derivations (e.g., posterior predictive distributions), while also demonstrating strong empirical rigor through out-of-sample forecasting, cross-validation, and implementation in Stan with real data.

  flowchart TD
    Goal["Research Goal<br/>Forecast Day-Ahead Electricity Spot Prices<br/>Using Probabilistic Methods"]
    
    subgraph Methodology["Methodology: Stochastic Volatility Models"]
        direction LR
        Baseline["Baseline Model<br/>SV with Latent Volatility"]
        Enriched["Enriched Model<br/>SV + Exogenous Regressors"]
    end

    Data["Data/Inputs<br/>Electricity Spot Prices<br/>Exogenous Variables<br/>Weather, Demand"]
    
    Compute["Computational Process<br/>Bayesian Inference<br/>Stan Implementation<br/>MCMC Sampling"]
    
    Outcome["Key Findings/Outcomes<br/>1. Enriched Model Outperforms Baseline<br/>2. Out-of-Sample Forecast Validation<br/>3. Applicability for Risk Hedging<br/>in Financial Derivatives"]

    Goal --> Data
    Data --> Baseline
    Baseline --> Enriched
    Enriched --> Compute
    Compute --> Outcome