Figure 2- - uploaded by Saud M. Al-Fattah
Content may be subject to copyright.
Petroleum Market Module Overview. 

Petroleum Market Module Overview. 

Source publication
Article
Full-text available
During the 1970s, oil market models offered a framework for understanding the growing market power being exercised by major oil producing countries. Few such models have been developed in recent years. Moreover, most large institutions do not use models directly for explaining recent oil price trends or projecting their future levels. Models of oil...

Context in source publication

Context 1
... EIA is actively designing and building a new module to deal with the liquids fuel market, but currently most of the oil modeling is handled in two modules: the Petroleum Market Model (PMM) and the International Energy Module (IEM). The PMM is centered on the refining sector, which is optimized with a linear program. Using refining constraints, refined product demands and prices, and crude supplies and prices, domestic and international product production are calculated in equilibrium. An overview of the PMM's structure is presented in Figure 2 (Office of Integrated Analysis and Forecasting 2010). ...

Similar publications

Article
Full-text available
We investigate the relationship between energy commodities bases, inventory and financial stress from 1994 to 2018. We find that, from the 1998 Asian crisis the effect of financial stress on energy commodities bases gradually increased and from the 2008 crisis became positive, while the effect of inventory showed a gradual decline over time. The re...

Citations

... Like other IAMs, SIAMESE assumes perfect energy markets (Edenhofer et al., 2010;Massetti and Sferra, 2010;Huntington et al., 2013), where energy prices coincide with marginal costs. As a result, we compute the energy prices as the derivative of GDP with respect to energy consumption (marginal utility of energy), as in Equation (A.5). ...
Article
Nationally Determined Contributions (NDCs) submitted so far under the Paris Agreement are not in line with its long-term temperature goal. To bridge this gap, countries are required to provide regular updates and enhancements of their long-term targets and strategies, based on scientific assessments. The goal of this paper is to demonstrate a policy-support approach for evaluating NDCs and guiding enhanced ambition. The approach rests on deriving national targets in line with the Paris Agreement by downscaling regional results of Integrated Assessment Models (IAMs) to the country level. The method of downscaling relies on a reduced complexity IAM: SIAMESE (Simplified Integrated Assessment Model with Energy System Emulator). We apply the approach to an EU28 member state – Finland – with the aim of providing useful insights for policy makers to consider cost-effective mitigation options. Results over the historical period confirm that our approach is valid when national policies are similar to those across the larger IAM region, but must include country-specific circumstances. Strengths and limitations of the approach are discussed. We assess the remaining carbon budget and analyse the different implications of 2 °C and 1.5 °C global warming limits for the emissions pathway and energy mix in Finland over the 21st century.
... However, generally oil price simulation models include four different types: Structural models, computational models, reduced form/financial models, and artificial intelligence models. (Huntington et al., 2013) These four different types will be introduced very briefly: 1. Structural models are based on microeconomics and players' ...
... (Mohaghegh et al., 2011) Artificial neural networks, fuzzy logic and genetic algorithms are among the artificial intelligence paradigms. (Huntington et al., 2013) An example of these models is (Al-fattah, 2013). ...
Full-text available
Article
An agent-based model is employed for simulating the price of oil futures. The model proceeds as follows: On each time step agents choose their rule for price expectation formation. Next, they bid and ask based on their price and trend expectations. The new price is formed using “the market mechanism”. Finally, the time steps forward and the process is repeated in the next day. The agents use 6 different rules to make price and trend expectations. Brent future prices in a 2-year-period (2010-2011) and in 2012 are used for model calibration and validation, respectively. It was shown that market participants weigh U.S. stocks data more than other factors, while OECD stock’s data were not that important for the market. It was also inferred that the market does not weigh the technical aspects of the oil price as much as the fundamental aspects.
Full-text available
Article
This study aims to analyze two important features of crude oil price data, namely, persistence and parameter instability. We apply an autoregressive fractionally integrated moving average model in which crude oil price persistence measured through the fractional integration parameter, conditional innovation variance and persistence change through Markov-switching dynamics. Model estimation is conducted using Gibbs sampling combined with data augmentation. Applied to monthly West Texas Intermediate crude oil price data from September 1859 to December 2017, we find evidence of four regimes. The main effect of regime switching is in the conditional variance and persistence of the innovations, but there is the possibility that regime switching also affects the fractional integration parameter. Across regimes crude oil price is very persistent with the order of integration estimated close to or slightly higher than one. Finally, discarding parameter instability leads to overestimation of the degree of persistence in the price of crude oil. It also results in inferior density forecasts. © 2018 Springer Science+Business Media, LLC, part of Springer Nature