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Modeling natural gas market volatility using GARCH with different distributions

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  • Lv, Xiaodong
  • Shan, Xian

Abstract

In this paper, we model natural gas market volatility using GARCH-class models with long memory and fat-tail distributions. First, we forecast price volatilities of spot and futures prices. Our evidence shows that none of the models can consistently outperform others across different criteria of loss functions. We can obtain greater forecasting accuracy by taking the stylized fact of fat-tail distributions into account. Second, we forecast volatility of basis defined as the price differential between spot and futures. Our evidence shows that nonlinear GARCH-class models with asymmetric effects have the greatest forecasting accuracy. Finally, we investigate the source of forecasting loss of models. Our findings based on a detrending moving average indicate that GARCH models cannot capture multifractality in natural gas markets. This may be the plausible explanation for the source of model forecasting losses.

Suggested Citation

  • Lv, Xiaodong & Shan, Xian, 2013. "Modeling natural gas market volatility using GARCH with different distributions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(22), pages 5685-5699.
  • Handle: RePEc:eee:phsmap:v:392:y:2013:i:22:p:5685-5699
    DOI: 10.1016/j.physa.2013.07.038
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    2. Lahmiri, Salim, 2017. "Asymmetric and persistent responses in price volatility of fertilizers through stable and unstable periods," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 466(C), pages 405-414.
    3. Lahmiri, Salim, 2016. "Interest rate next-day variation prediction based on hybrid feedforward neural network, particle swarm optimization, and multiresolution techniques," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 444(C), pages 388-396.
    4. Lahmiri, Salim, 2017. "Modeling and predicting historical volatility in exchange rate markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 387-395.
    5. repec:eee:eneeco:v:70:y:2018:i:c:p:307-333 is not listed on IDEAS

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