Prequential forecasting in the presence of structure breaks in natural gas spot markets
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Prequential forecasting in the presence of structure breaks in natural gas spot markets Kannika Duangnate1 · James W. Mjelde2 Received: 20 November 2018 / Accepted: 4 May 2019 © Springer-Verlag GmbH Germany, part of Springer Nature 2019
Abstract The natural gas sector has undergone major regulatory and technological changes. These changes may induce structural changes in price relationships among natural gas markets. Tests for structural breaks suggest two potential structural breaks, around 2000 and 2009. Previous forecasting studies on natural gas prices/returns largely are point forecasts and focus on a single spot market; unlike those, this study undertakes simultaneous probabilistic forecasts of eight spot markets. Prequential forecasting analysis examines: (1) whether differences exist in the ability to probabilistically forecast returns among various natural gas markets and (2) how the presence of structural breaks in the natural gas sector influences the probability forecasts. The ability to forecast natural gas markets differs based on the different criteria. Disparities may be explained by each market’s role in price discovery, the alteration of the market’s participation, and whether the market is located in an excess supply or demand region. Irrespective of the models, Henry Hub and AECO returns appear to be easier to forecast, as they generally have the smaller root-mean-squared error, Brier score, and ranked probability score, while Dominion South and Chicago returns appear to be more difficult to forecast. Models using longer periods of data appear to forecast returns better than models using data starting after the breaks; the latter always produces the largest root-mean-squared error, Brier score, and ranked probability score. Keywords Natural gas · Parameter constancy · Structural break · Prequential · Probability forecast JEL Classification C13 · Q41 · Q4
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Kannika Duangnate [email protected] James W. Mjelde [email protected]
1
Faculty of Economics, Chiang Mai University, Chiang Mai 50200, Thailand
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Department of Agricultural Economics, Texas A&M University, College Station, TX, USA
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K. Duangnate, J. W. Mjelde
1 Introduction Natural gas, a major energy source worldwide, contributes to multiple sectors of the economy (National Energy Technology Laboratory 2013). Given this importance, it is not surprising that natural gas is one of the top ten commodities traded (by volume) in the USA (Investorguide Staff 2016; Kowalski 2016) and worldwide (Swift 2013). Forecasting price movements among natural gas markets are obviously important. Institutional changes and technological advances, however, may alter the supply and demand relationships in the natural gas sector inducing structural changes in price relationships among natural gas markets. Models ignoring structural changes may generate forecasts that are worse than models accounting for such changes (Banerjee et al. 2014; Chen et al. 2014). Given the importance of natural gas, it is not surprising to find a multitude of literature o
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