Non-Linear Forecasting of Energy Futures: Oil, Coal and Natural Gas

2014 ◽  
Author(s):  
Germmn G. Creamer
Keyword(s):  
Natural Gas ◽  
Energy Futures ◽  
Non Linear

scholarly journals Regulating Subsidence and its uncertainty in the Dutch Wadden Sea

2020 ◽  
Vol 382 ◽  
pp. 63-70
Author(s):  
Johannes A. de Waal ◽  
Mathijs W. Schouten
Keyword(s):  
Natural Gas ◽  
The Netherlands ◽  
Land Subsidence ◽  
Wadden Sea ◽  
Gas Production ◽  
Reservoir Rock ◽  
Proper Management ◽  
Subsidence Rate ◽  
Non Linear ◽  
Regulatory Approach

Abstract. At the start of gas production its effects on land subsidence are not certain. There are uncertainties in mechanisms, models and parameters. Examples are non-linear deformation of reservoir rock, fault transmissibility, behaviour of overlaying salt and aquifer activity. Looking back at historical cases in the Netherlands, a factor two or three difference between initial prediction and final outcome is quite common. As the Dutch regulator, SSM is tasked with assuring proper management by operators of the risks associated with land subsidence from natural gas production in The Netherlands. Large initial uncertainties can only be tolerated if operators can demonstrate that timely actions can still be taken when predefined subsidence limits are at risk of being exceeded now or in the future. The applied regulatory approach is illustrated by the case history of gas production induced subsidence in the Dutch Wadden Sea area. This environmentally highly sensitive UNESCO World Heritage Site is a natural gas province. Extensive legal, technical and organisational frameworks are in place to prevent damage to its natural values. Initial uncertainties in the predicted subsidence (rate) were later exacerbated by the detection of strong non-linear effects in the observed subsidence behaviour, leading to new concerns. It was realised that – depending on the underlying physical cause(s) – there will be a different impact on future subsidence. To assure proper management of the additional uncertainty by the operator, several improvements in the regulatory approach have been implemented. Possible underlying mechanisms had to be studied in depth and improved data analysis techniques were requested to narrow down uncertainties as time progresses. The approach involves intensified field monitoring, scenario's covering the full range of uncertainties and a particle filter approach to handle uncertainties in predictions and measurements. Spatial-temporal double differences, production data and the full covariance matrix are used to confront scenario predictions against measurements and to assess their relative probability. The regulator is actively involved in assuring this integrated control loop of predictions, monitoring, updating, mitigation measures and the closing of knowledge gaps. The regulator involvement is supported in the Mining law and by appropriate conditions in the production plan assent. With the approach it can be confidently assured that subsidence (rate) will remain within the allowed range.

scholarly journals How to deal with non-linear pathways towards energy futures

2019 ◽  
Vol 28 (3) ◽  
pp. 20-26
Author(s):  
Stefan Vögele ◽  
Witold-Roger Poganietz ◽  
Philip Mayer
Keyword(s):  
Policy Advice ◽  
Linear Extrapolation ◽  
Energy Futures ◽  
The Past ◽  
Non Linear ◽  
Simplifying Assumptions ◽  
Novel Approaches ◽  
The Creation ◽  
Linear Trends

Energy scenarios currently in use for policy advice are based on a number of simplifying assumptions. This includes, in particular, the linear extrapolation of trends. However, this approach ignores the fact that central variables were highly dynamic in the past. For an assessment of energy futures and the specification of measures, novel approaches are necessary which can implement non-linear trends. In this paper, we show how cross-impact balance (CIB) analysis can be applied to map dynamic trends. Using a small CIB model, we highlight the need for novel approaches in the creation and evaluation of energy futures and the possible contribution of CIB analysis.

scholarly journals Effect of Spark Condition on Non-Linear Dynamic Characteristics of Natural Gas Engine

2012 ◽  
Vol 1 ◽  
pp. 150-155 ◽  
Author(s):  
Wang Li-Yuan ◽  
Yang Li-Ping ◽  
Jing Hai-Guo ◽  
Qaisar Hayat ◽  
Liu Guo-Dong
Keyword(s):  
Natural Gas ◽  
Dynamic Characteristics ◽  
Gas Engine ◽  
Natural Gas Engine ◽  
Linear Dynamic ◽  
Non Linear

scholarly journals Best Fitting Fat Tail Distribution for the Volatilities of Energy Futures: Gev, Gat and Stable Distributions in GARCH and APARCH Models

2018 ◽  
Vol 11 (2) ◽  
pp. 30 ◽  
Author(s):  
Samet Gunay ◽  
Audil Khaki
Keyword(s):  
Natural Gas ◽  
Normal Distribution ◽  
Stable Distributions ◽  
Energy Markets ◽  
Trading Strategies ◽  
Fat Tails ◽  
Oil Futures ◽  
Energy Futures ◽  
Heating Oil ◽  
Risk Managers

Precise modeling and forecasting of the volatility of energy futures is vital to structuring trading strategies in spot markets for risk managers. Capturing conditional distribution, fat tails and price spikes properly is crucial to the correct measurement of risk. This paper is an attempt to model volatility of energy futures under different distributions. In empirical analysis, we estimate the volatility of Natural Gas Futures, Brent Oil Futures and Heating Oil Futures through GARCH and APARCH models under gev, gat and alpha-stable distributions. We also applied various VaR analyses, Gaussian, Historical and Modified (Cornish-Fisher) VaR, for each variable. Results suggest that the APARCH model largely outperforms the GARCH model, and gat distribution performs better in modeling fat tails in returns. Our results also indicate that the correct volatility level, in gat distribution, is higher than those suggested under normal distribution with rates of 56%, 45% and 67% for Natural Gas Futures, Brent Oil Futures and Heating Oil Futures, respectively. Implemented VaR analyses also support this conclusion. Additionally, VaR test results demonstrate that energy futures display riskier behavior than S&P 500 returns. Our findings suggest that for optimum risk management and trading strategies, risk managers should consider alternative distributions in their models. According to our results, prices in energy markets are wilder than the perception of normal distribution. In this regard, regulators and policy makers should enhance transparency and competitiveness in the energy markets to protect consumers.

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