Distributional Modeling and Forecasting of Natural Gas Prices

TL;DR

This paper develops advanced probabilistic models for European natural gas prices, capturing heavy tails, seasonality, and other stylized facts, leading to improved forecasting accuracy for risk management.

Contribution

It introduces skewed, heavy-tailed state-space models incorporating multiple relevant factors, outperforming benchmarks in probabilistic forecasting of gas prices.

Findings

13% reduction in CRPS for Day-Ahead forecasts

9% reduction in CRPS for Month-Ahead forecasts

Models effectively capture volatility and heavy tails

Abstract

We examine the problem of modeling and forecasting European Day-Ahead and Month-Ahead natural gas prices. For this, we propose two distinct probabilistic models that can be utilized in risk- and portfolio management. We use daily pricing data ranging from 2011 to 2020. Extensive descriptive data analysis shows that both time series feature heavy tails, conditional heteroscedasticity, and show asymmetric behavior in their differences. We propose state-space time series models under skewed, heavy-tailed distributions to capture all stylized facts of the data. They include the impact of autocorrelation, seasonality, risk premia, temperature, storage levels, the price of European Emission Allowances, and related fuel prices of oil, coal, and electricity. We provide rigorous model diagnostics and interpret all model components in detail. Additionally, we conduct a probabilistic forecasting study with significance tests and compare the predictive performance against literature benchmarks. The proposed Day-Ahead (Month-Ahead) model leads to a 13% (9%) reduction in out-of-sample continuous ranked probability score (CRPS) compared to the best performing benchmark model, mainly due to adequate modeling of the volatility and heavy tails.