Role of weather in the natural gas market: Insights from the STL‐GARCH‐W method

• Published date: 01 December 2023
• Author: Lijuan Peng,Zhenglan Xia,Yisu Huang,Zhigang Pan
• Date: 01 December 2023
• DOI: http://doi.org/10.1111/infi.12437

Received: 21 April 2023

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Accepted: 17 August 2023

DOI: 10.1111/infi.12437

ORIGINAL ARTICLE

Role of weather in the natural gas market:

Insights from the STL‐GARCH‐W method

Lijuan Peng

1

|Zhenglan Xia

1

|Yisu Huang

2

|Zhigang Pan

1

1

Department of Mathematics, Southwest

Jiaotong University, Chengdu, China

2

Department of Economics &

Management, Southwest Jiaotong

University, Chengdu, China

Correspondence

Zhigang Pan, Department of

Mathematics, Southwest Jiaotong

University, Chengdu 610031, China.

Email: panzhigang@swjtu.edu.cn

Funding information

National Natural Science Foundation of

PR China, Grant/Award Numbers:

71902128, 72071162, 72271204; Post‐

funded Project of the National Social

Science Foundation of PR China,

Grant/Award Number: 22FJYB062;

Degree and Postgraduate Education and

Teaching Reform Project of Southwest

Jiaotong University,

Grant/Award Number: YJG5‐2022‐Y033;

Fundamental Research Funds for the

Central Universities,

Grant/Award Numbers: 2682020ZT98,

2682022ZTPY063; Special subject of

characteristic philosophy and social

science planning in Sichuan,

Grant/Award Number: SC22TJ06;

72271204

Abstract

Weather 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‐Wmodelsare

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‐Wmodel

outperform the STL‐GJR‐GARCH‐W model and the

benchmark STL‐GARCH model when temperature, pre-

cipitation, 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.

KEYWORDS

decomposition‐ensemble technique, natural gas market, weather

JEL CLASSIFICATION

C22, C53, Q43

International Finance. 2023;26:304–323.wileyonlinelibrary.com/journal/infi304

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© 2023 John Wiley & Sons Ltd.

1|INTRODUCTION

As an important energy source, natural gas has the advantage of being cleaner, simpler to store

and transport than fossil fuels, thus attracting the eyes of many environmentalists. At the same

time, with countries around the world developing low‐carbon energy systems and pursuing the

goal of achieving net‐zero emissions, low‐carbon natural gas is increasingly favoured by all

walks of life. More importantly, natural gas is an important strategic commodity whose future

price has a significant impact on energy markets, economic construction, and national security.

Therefore, accurate forecasting of natural gas price volatility (NGPV) is crucial for investors and

researchers to hedge and prevent energy market risks while promoting a green and sustainable

economy and maintaining the stability and health of energy markets. However, early research

on the impact of weather on energy market volatility has mostly focused on the oil market and

renewable energy, while studies on NGPV have concentrated more on other factors (Liang

et al., 2021). This prompts us to focus on the influence of weather on NGPV.

Behavioural finance argues that investors often behave irrationally. Thus, individual psychological

aspects should be considered when modelling financial markets because some investors may use

various investment methods even when dealing with the same information (Daniel et al., 2002).

Weather, as an important factor affecting human emotions, has been shown to significantly influence

financial markets. For example, Saunders (1993) argued that the sunshine effect has a negative

impact on stock returns based on sentiment misattribution. Then, Daniel et al. (1998)statedthat

investors are eager to purchase equities when the economy is doing well and they are feeling upbeat.

Later, Goetzmann et al. (2015) discovered that institutional investors' views of mispricing and trading

actions are influenced by weather‐based sentiment indicators, which increase stock market volatility.

Shahzad (2019) explored the relationship between weather indicators such as humidity, temperature,

barometric pressure, and stock volatility in Greater China and found that weather indicators have a

magnitude impact on capital markets in Greater China. Furthermore, the close relationship between

the weather and crude oil market, agricultural market and carbon market was also verified. On the

other hand, global weather extremes have been frequent in recent years and have had a significant

impact on financial markets. Extreme weather is a common extreme shock and is considered to be

one of the primary elements influencing the natural gas market. Weather may cause significant

volatilities in natural gas price by affecting the supply and demand of natural gas. The

Russia–Ukraine conflict in 2022 has also increased awareness among governments of the necessary

of forecasting NGPV in the event of extreme shocks. Even changes in climate policy can affect energy

market volatility (Liang, Umar, et al., 2022). Therefore, it is evident that while modelling NGPV,

weather factors should be taken into account.

The magnitude influence of weather on NGPV has been recognized by a number of researchers.

For example, Mu (2007) found that weather factors have a significant effect on both the conditional

mean and volatility of natural gas futures returns. Considine (2000)examinedthelinkbetweenUS

natural gas consumption and weather, and showed that warm weather does reduce carbon emissions

and natural gas consumption in the United States. Chan et al. (2009)analyzedtheshort‐term demand

role of weather indicators in the time‐varying volatility of the natural gas market. Nick and Thoenes

(2014) examined the main influences on the German natural gas market using a structural VAR

approach, finding that temperature and supply shocks affect NGPV. Liang, Xia, et al. (2022)basedon

an extended GARCH‐MIDAS model to consider the predictability of extreme weather information on

NGPV, and the empirical results show that the forecasting model with the addition of weather

indicators does outperform the forecasting model without the addition of weather indicators.

Importantly, based on various out‐of‐sample tests, some extreme weather indicators can provide more

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