Mohammadreza Asghari Oskoei; Farhad Fallahi; Meysam Doostizadeh; saeed Moshiri
Abstract
With increasing competition in the wholesale Electricity markets and advances in behavioral economics in recent years, the multi-agent modeling approach has been applied widely to simulate the outcome of the markets. The electricity market consists of power generating agents that compete over production ...
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With increasing competition in the wholesale Electricity markets and advances in behavioral economics in recent years, the multi-agent modeling approach has been applied widely to simulate the outcome of the markets. The electricity market consists of power generating agents that compete over production in daily auction conducted by an independent system operator (ISO). The market clearing mechanism can be seen as a static game that repeats every hour. In this game, an agent proposes her price for the next day and the ISO chooses the best proposals that minimizes the total costs given the demand and the technical constraints. Agents are also assumed to learn from the outcomes and adjust their biding strategy accordingly. In this paper, we develop an agent-based model for the day-ahead and pay-as-bid electricity market in Iran. The objective is to compare the outcome of the market measured by the agents profit and the time to converge using three different strategies: greedy, random and reinforcement learning. The simulation results indicate that the reinforcement learning leads to higher profits with a faster convergence rate than the other two strategies.
Elham Hajikaram; Roya Darabi
Abstract
Anticipating process of crude oil prices and its fluctuations volatility has always been one of the challenges the traders face in the exchange oil markets. This study estimates the Brent crude oil daily price forecast with a proposed hybrid model. The sample is Brent North Sea crude oil daily prices ...
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Anticipating process of crude oil prices and its fluctuations volatility has always been one of the challenges the traders face in the exchange oil markets. This study estimates the Brent crude oil daily price forecast with a proposed hybrid model. The sample is Brent North Sea crude oil daily prices from July 2008 to July 2016 that is selected from the total oil daily prices in all of the oil markets. In this research, a model for combining statistical and artificial intelligence (PCA-SVR) methods is presented. With regard to the superiority of the accuracy of the prediction of the support vector regression model (SVR) in comparison with other predictive methods in past studies, the main goal in this research is to improve the prediction of the support vector regression using the initial pre-processing of data by principal components analysis (PCA). To do research, after carrying out a static test, using principal components analysis, the input variables are converted into the principal components that cover the entire data scattering and considered as an input to the prediction model. Then, using supporting vector regression model and simulate it in MATLAB software we predict daily price of Brent crude oil. In order to compare the performance of the SVR and PCA-SVR models, we used the paired comparison test. The result of this study was that the initial pre-processing by means of the principal components analysis on the data gives rise to reducing suggested model error
Reza talebloo; Hossein Sheikhi
Abstract
he purpose of this paper is to test the CAPM and APT pricing model for pricing petrochemical companies in Tehran Stock Exchange. In this regard, seasonal data related to stock returns of 18 active chemical and petrochemical companies in the stock market and some important macroeconomic variables as risk ...
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he purpose of this paper is to test the CAPM and APT pricing model for pricing petrochemical companies in Tehran Stock Exchange. In this regard, seasonal data related to stock returns of 18 active chemical and petrochemical companies in the stock market and some important macroeconomic variables as risk factors in the period 1395-1386 were used. First, the CAPM was tested using the GRS test and then by Fama and Macbeth tests. Then, the factor model for the APT test was using factors including real exchange rate, total stock returns, oil returns, yields of the price index Chemical and petrochemical products, risk-free returns, inflation rate, asset risk, GDP volatility, SMB, and sanction factor.
fotros mohammadhasan; mostafa omidali; amirmohammad galavani
Abstract
The aim of this study is to estimate the domestic balance of natural gas per capita in the Iran, as well as its forecast for the period 2017 - 2037. In this study, with employing dynamic models Autoregressive Distributed Lag (ARDL), at first, long-term and short-term elasticity of per capita natural ...
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The aim of this study is to estimate the domestic balance of natural gas per capita in the Iran, as well as its forecast for the period 2017 - 2037. In this study, with employing dynamic models Autoregressive Distributed Lag (ARDL), at first, long-term and short-term elasticity of per capita natural gas demand in Iran for the period 1981-2016 is estimated. Then with using a hybrid ARDL and ARIMA model, we predict the balance natural gas per capita up to the year 2037. The results show that amount of per capita natural gas demand will reach 4177.36 million cubic meters in 2037, as well as the amount of per capita natural gas supply will reach 3417.26 million cubic meters in this years. For responding this excess demand should be adopting policies to increase production or constrainting natural gas demand.
Abolghasem Golkhandan; Mohammad Alizadeh
Abstract
This study investigates the causal linkages between consumption of energy carriers and value added in the Iranian economic sectors for the period 1974-2013 by using the granger causality test in heterogeneous mixed panels. For this, the panel causality testing approach, the method developed by Emirmahmutoglu ...
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This study investigates the causal linkages between consumption of energy carriers and value added in the Iranian economic sectors for the period 1974-2013 by using the granger causality test in heterogeneous mixed panels. For this, the panel causality testing approach, the method developed by Emirmahmutoglu and Kose (2011) based on the vector autoregressive (VAR) model and Wald tests with the country specific bootstrap critical values, is applied. This test, accounts cross dependency and heterogeneity among the members of the panel and also, co-integration between variables. Based on the results, the existence of a unidirectional causality relation of oil and gas to value added and the existence of a bidirectional causality relation between electricity and value added in the service sector and the entire sectors is confirmed. In agriculture sector, there is only a causal relation of electricity on value added. In industry sector, the existence of a bidirectional causality relation between gas and electricity with value added and the existence of a unidirectional causality relation of value added to oil is confirmed. In transportation sector, there is a causal relation of gas and electricity to value added and there is a bidirectional causality relation between the oil and the value added. The results can provide important policy recommendations in planning and explaining energy sector policies at the level sector in country.
Mahmood Mohammadi Alamuti; mohammad reza haddadi; Younes Nademi
Abstract
Because of high reliance of Iranian economy to oil revenues, it is affected by the price volatility of the oil market. Therefore, the forecast of the oil price movement is very important at least in two aspects including determining the correct oil price in the government budget and also for controlling ...
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Because of high reliance of Iranian economy to oil revenues, it is affected by the price volatility of the oil market. Therefore, the forecast of the oil price movement is very important at least in two aspects including determining the correct oil price in the government budget and also for controlling the high price volatility for macroeconomic policy makers. Based on the importance of forecasting oil price movement, the purpose of this paper is to present an Early Warning System (EWS) for high oil price volatility in the OPEC crude oil market. This system, by forecasting the probability of staying in high volatility oil price in future periods, give a proper view of the trend of oil prices to policy makers. For this purpose, in the first step, by a Markov Switching GARCH model, the oil price trend and its volatility have been modeled and estimated during the period of 2010-2016. Then, using this model, the transition probability matrix, which involves the probability of staying in the high-volatility and low-volatility regimes, and the probability of switching between the regimes, has been obtained. Based on this matrix, the probability of being in a low-volatility and high-volatility crude oil price have been forecasted. so the policymakers and activists in the oil market can make better decisions to avoid of damaging effects of high oil price volatility
