Modeling natural gas market volatility using GARCH with different distributions

Lv, Xiaodong; Shan, Xian

doi:10.1016/j.physa.2013.07.038

Cited by 39 publications

(24 citation statements)

References 37 publications

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“…In Lv (2013) modelling on natural gas market volatility using GARCH-class models was used with a long memory and fat-tail distributions. The study did the forecasting of price volatilities of spot and futures prices.…”

Section: Review Of Literaturementioning

confidence: 99%

Understanding volatility and leverage effects in bunker markets

Ranjan¹,

Raju

Jayaraj

et al. 2018

IJGEI

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The research analysed the volatility and the leverage effect in the bunker prices in IFO 380 and IFO 180 grades. Limited studies were found on bunker prices using EGARCH and bivariate analysis. The research investigated the causal relationships between IFO 380 and IFO 180 grades of bunkers. Serial correlations were found through using Lagrange multiplier (LM test) and heteroskedastic tests in the bunker price data. The analysis exhibited the model to be highly significant at 1%. Both the grades showed the short and long-run shocks and a positive leverage effect. The recommendations were made to the maritime industry to strategise during the long run and short-run shocks of bunker markets.

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Section: Review Of Literaturementioning

confidence: 99%

Understanding volatility and leverage effects in bunker markets

Ranjan¹,

Raju

Jayaraj

et al. 2018

IJGEI

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show abstract

“…In addition, we checked the reference lists of the identified papers [60]. Overall, 15 articles have been identified [2], [7], [45], [26], [28], [33], [8], [32], [29], [39], [31], [46], [47], [61], [36]. For the majority of articles, the topic of price prognosis is in the research focus.…”

Section: Decision Support In Natural Gas Tradingmentioning

confidence: 99%

“…For the majority of articles, the topic of price prognosis is in the research focus. For this purpose, time-series analysis based on regression equations are predominantly used [7], [28], but machine learning methods are represented as well [2], [8]. volatility to predict bigger price spikes by applying regression equations [28] and Markov chains [26].…”

Section: Decision Support In Natural Gas Tradingmentioning

confidence: 99%

Detecting similar news tickers in the area of natural gas trading

Dreikorn

Felden

Pospiech

et al. 2015

Proceedings of the 7th International Conference on Management of Computational and Collective intElligence in Digital EcoSystem

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The volatility of the natural gas market founded a need for the ability to analyze upcoming events in real time in order to manage profits and risks for participants. News ticker provide information being of utmost importance for the analysis. The research presented among this paper describes features of a software prototype supporting the analytical price prognosis tasks for gas traders. By knowing market development at the time of a certain past situation, the outcome of that situation can be used to predict the future market development of a current analyzed situation with similar content. For that, similar situations have to be detected in order to reduce uncertainty about future. Fitting into design science, we use task-technology-fit theory and technologyacceptance-model to identify information needs and to evaluate the artifact. This novel approach serves as a further step to gain a decision support with integrated structured and unstructured data.

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“…There are many methods for volatility forecasting but the most popular in the literature are the generalized autoregressive conditional heteroscedasticity (GARCH) models. In particular, they have already been applied in plenty of studies for energy commodities, including: Crude oil (e.g., [2][3][4][5][6][7][8][9][10][11]), natural gas (e.g., [4,6,12]), heating oil (e.g., [2,3,6]), gasoline (e.g., [3,6]) and gasoil (e.g., [2]).…”

Section: Introductionmentioning

confidence: 99%

Forecasting Volatility of Energy Commodities: Comparison of GARCH Models with Support Vector Regression

2020

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We compare the forecasting performance of the generalized autoregressive conditional heteroscedasticity (GARCH) -type models with support vector regression (SVR) for futures contracts of selected energy commodities: Crude oil, natural gas, heating oil, gasoil and gasoline. The GARCH models are commonly used in volatility analysis, while SVR is one of machine learning methods, which have gained attention and interest in recent years. We show that the accuracy of volatility forecasts depends substantially on the applied proxy of volatility. Our study confirms that SVR with properly determined hyperparameters can lead to lower forecasting errors than the GARCH models when the squared daily return is used as the proxy of volatility in an evaluation. Meanwhile, if we apply the Parkinson estimator which is a more accurate approximation of volatility, the results usually favor the GARCH models. Moreover, it is difficult to choose the best model among the GARCH models for all analyzed commodities, however, forecasts based on the asymmetric GARCH models are often the most accurate. While, in the class of the SVR models, the results indicate the forecasting superiority of the SVR model with the linear kernel and 15 lags, which has the lowest mean square error (MSE) and mean absolute error (MAE) among the SVR models in 92% cases.

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Modeling natural gas market volatility using GARCH with different distributions

Cited by 39 publications

References 37 publications

Understanding volatility and leverage effects in bunker markets

Understanding volatility and leverage effects in bunker markets

Detecting similar news tickers in the area of natural gas trading

Forecasting Volatility of Energy Commodities: Comparison of GARCH Models with Support Vector Regression

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