Abstract
AbstractWeather has been shown to affect natural gas markets, but there is limited research on the strength and manner in which weather affects predictions of natural gas volatility. In this study, six weather indicators are used as exogenous variables, and seasonal‐trend decomposition‐generalized autoregressive conditional heteroskedasticity‐Weather (STL‐GARCH‐W) and STL‐GJR‐GARCH‐W models are constructed to explore the effect of weather on global natural gas market. The empirical findings indicate that temperature and precipitation have a notable positive effect on natural gas, while solar radiation has a prominent negative effect. Furthermore, the STL‐GARCH‐W model outperform the STL‐GJR‐GARCH‐W model and the benchmark STL‐GARCH model when temperature, precipitation, and solar radiation are considered. In addition, the January effect has been shown to significantly influence natural gas price volatility. Finally, most parameters in both models are of statistical significance, demonstrating that both models accurately forecast natural gas volatility and emphasizing the importance of weather indicators for modelling natural gas price volatility. Our study provides new insights for energy market investors and policy makers.
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