Showing 10 results for Oil Price
Dr Behzad Salmani, Dr Davood Behbudi , Siab Mamipour ,
Volume 2, Issue 4 (6-2011)
Abstract
The optimal usage of oil as a natural resource is an important problem in exporting countries. These countries always are encountered with uncertainty and volatility of oil prices and its effects on real exchange rate. The main purpose of this paper is to investigate the relationship of between oil prices and exchange rate by emphasizing institutional quality in during 1995-2006. The model of this paper is estimated by panel data approach. Findings show that the oil prices have a positive effect on real exchange rate and it reduces international competition power. But institutional quality affects the extent to which the real exchange rates of oil-exporting countries co-move with the oil price. The results show that countries with high institutional quality such as control of corruption and regularity quality have real exchange rates which co-move less with the oil price.
Dr Nader Mehregan, Dr Parviz Mohammadzadeh, Dr Mahmoud Haghani, Yunes Salmani,
Volume 4, Issue 12 (7-2013)
Abstract
Price shocks lead to oil price volatility in world oil markets. In response to this volatility, economic growth may take different regime and behavior patterns in different situation. Investigating this multi behavior patterns can be useful for policymakers to reduce the effect of oil price volatility. In this study, an EGARCH model has developed using the seasonal data of OPEC oil basket nominal prices during 1367:Q1-1389:Q4. Markov switching models is also applied to investigate the multi behavior patterns of economic growth in response to oil price volatility in Iran.
The results show that positive oil price shocks sharply lead to formation of oil price volatility, but, the negative price shocks will slightly reduce oil price volatility. Iranian economic growth is affected by this volatility under three different behavior regimes. If the economy switch to one of the regimes (low, medium, high economic growth), the probability of transition between these regimes and their duration is different. So, oil price volatility as a reason for low economic growth in Iran may cause the economy switch to its lower situation.
, , ,
Volume 4, Issue 14 (12-2013)
Abstract
ABSTRACT
Considering the major impact which changes in the real exchange rate and crude oil prices have on various sectors of Iran's economy and the importance of the financial markets role in economic growth and development, this paper aimed to investigate the effects of the changes in real exchange rate and crude oil prices on Tehran stock exchange using the Markov-Switching's nonlinear models. To this end, daily data which belonged to the following periods were used: 20:03: 2005 - 13:10:2010
The result of the estimations obtained through the Markov Switching Models indicated that MSIAH model with two regimes out of the various MS model are the most suitable ones. The result of the research showed that the changes in the exogenous variable of real exchange rate and the crude oil price have lagging positive effect on the Stock Exchange Index. Moreover, the effects of these changes with two lagging time intervals are significant and negative. The practical implications of these findings could be beneficial to the investors and policy makers who need to be aware of the exact nature of the effects which changes in the exchange rate and crude oil prices have on the stock exchange index.
Abbass Memarzadeh, Ali Emami Meibodi, Hamid Amadeh, Amin Ghasemi Nejad,
Volume 4, Issue 14 (12-2013)
Abstract
Abstract
Forecasting of crude oil price plays a crucial role in optimization of production, marketing and market strategies. Furthermore, it plays a significant role in government’s policies, because the government sets and implements its policies not only according to the current situation but also according to short run and long run predictions of important economic variables like oil price. The main purpose of this study is modeling and forecasting spot oil price of Iran by using GARCH model and A Gravitational Search Algorithm. Performed forecasts of this study are based in static and out-of-sample forecasting and each subseries data is divided in to two parts: data for estimation and data for forecasting. The forecast horizon is next leading period and its length is one month. In this study the selected models for forecasting spot oil of Iran are GARCH(2,1) and a Cobb Douglas function which is functional of prices of 5 days ago. Finally, the performances of these models are compared. For comparison of these models MSE, RMSE, MAE, and MAPE criteria are used and the results indicate that except in MAPE criterion, the mentioned criteria are smaller for GARCH model in comparison to GSA algorithm.
Shahram Fattahi, Kiomars Sohaili, Hamed Abdolmaleki,
Volume 5, Issue 17 (10-2014)
Abstract
The fluctuations in the oil price with uncertainty, as an exogenous variable, is the most important factor affecting the fluctuations in the GDP of the countries especially OPEC. This study examines the effect of oil price uncertainty on the Iran’s GDP growth using the seasonal data for the period 1988(1)-2011(4). The model used in this study is the asymmetric VARMA, MVGARCH-M and the estimated method is quasi maximum likelihood (QML). The results indicated that there is a negative and significant relationship between oil price and economic growth over the period. Furthermore, the results show that the conditional variance-covariance process underlying output growth and change in oil price exhibits non-diagonality and asymmetry.
Nooshin Bordbar, Ebrahim Heidari,
Volume 8, Issue 27 (3-2017)
Abstract
The present article studies the interactive relationships between oil price volatility and industries stocks of basic metals, petroleum and chemical products by using Vector Auto Regressive (VAR) and Multivariate Generalized Autoregressive Conditional Heteroskedastisity (GARCH) models from March 2004 to March 2015 empirically . In this research, the VAR-GARCH model is proposed, which is developed by Ling and McAleer (2003). The model survives the return and volatility problems among the considered series and this is the VAR-GARCH advantage. The results show that there are Average effects between oil market and stocks market of basic metals and petroleum products, But this effects are not true for chemical industry market. The volatility effects between world oil price and chemical and basic metals industry markets is not existed, but between oil market volatility and petroleum products stock volatility, Significant negative relationship is existed. There for, the investors should reduce their portfolios basket dependences on oil price as much as possible.
Morteza Behrouzifar, Ali Emami Meibodi, Abdolrassoul Ghassemi, Mohammad Bagher Heshmatzadeh,
Volume 8, Issue 27 (3-2017)
Abstract
Expectation has an important role in oil price fluctuation and it seems which one of the important factors is for changing supply behaviour however oil price changes. Identification of mentioned expectation could help us for partly and continuously control the oil market situation.one of the important factor that could have effects on future oil price expectation is volume of current reserve oil and specifically OPEC members reserves. For OPEC members not only high reserve oil is prestige but also give them chance for having more OPEC production share however after applying market sharing system based on production for OPEC members in early 1980s,volume of reserve oil considered as a main benchmark and after that started increase reserve oil competition among OPEC members. In this paper tried study transition s of Iran’s oil reserves and its effectiveness on the oil producer’s countries’ information and also its accuracy. According to some statement reserve oil extra announcement could create chaos in oil market. Based on this study there is no any relation between increasing of oil reserves and oil production changes in Iran as one of the OPEC member's country and it seems extra reserve oil announcement more than reality is a hidden competition among members for getting more credit.
Siab Mamipour, Hadis Abdi,
Volume 9, Issue 34 (12-2018)
Abstract
The business cycles are one of the most important economic indicators that they show the changes in economic activities during time. The study of business cycles is important because the understanding fluctuations in GDP and effective factors on these fluctuations help policy makers to plan better and more efficient. The main purpose of this paper is to investigate the effects of oil price shocks on business cycles dynamics in Iranian economy during period of 2005 to 2017 by using non-linear Markov switching model with the time varying transitional probabilities (MS-TVTP). So, first, the oil price shocks were extracted in four different modes, and then the effect of them on recession and boom regimes are investigated. The results of MS-TVTP model show that business cycles are affected by oil price fluctuations and shocks in Iran’s economy. The results indicate that, in all four modes which oil price shocks were calculated, the positive shocks in oil price increase the probability of staying in boom regime. Also positive oil price shocks increase the probability of transition from the recession regime in Iran’s economy. Also, with relative comparison of the coefficients of oil price shocks in the probability of staying in boom regime and transition from recession to boom regime, it can be argued that positive oil price shocks in recession period increases the probability of transition from recession more than the boom regime. In other words, oil price shocks in recession periods have a greater effect on rotation of economic situation and increase the probability of transition from recession regime, but in the boom regime, the positive oil price shock lead to increases the probability of staying in boom regime a little.
Ali Takroosta, Parisa Mohajeri, Taymour Mohammadi, Abbas Shakeri , Abdoulrasoul Ghasemi ,
Volume 10, Issue 37 (10-2019)
Abstract
Oil price wild fluctuations impact the economies of developing countries as well as those of developed ones. Focusing on OPEC’s political risks as a proxy of precautionary demand, this study aims to disentangle oil price factors using an SVAR approach for 1994Q1 to 2016Q4. We disentangled oil price shocks into political risks, supplies, global demand for industrial goods and other oil price shocks. Our results highlight that shocks originated from different sources affect oil prices differently in terms of both their lifetime and directions. Besides, it is revealed that the structure of oil market has changed due to the 2008 financial crisis, increased oil price fluctuations, changes in OPEC’s behaviour and accordingly its market power, and the advent of new shale oil technologies, thus affecting oil price sensitivities. Therefore, we found out that OPEC’s political risks affected oil markets way more significantly in 2008-2016.
Navid Salek, Morteza Khorsandi,
Volume 13, Issue 47 (5-2022)
Abstract
The price of crude oil is one of the factors affecting economic indicators. Therefore, the prediction of oil prices and the accuracy of the applied methods have always been discussed by economists. In this study, the effect of all effective variables on the supply and demand of crude oil based on McAvoy's competitive theory is investigated, and the supply and demand are estimated using the system of simultaneous equations and conventional statistical methods. Then, using algebraic operations and the assumption of equality of oil supply and demand in the long term, the long-term potential of oil supply and demand is extracted with respect to each of the variables in the model. Based on the results, the world's gross domestic product (GDP) has the greatest impact on oil prices with a demand potential of 0.6039, and the world's military and security tensions have the least impact with a demand potential of –0.0110. After estimating the model, the prediction accuracy of three combined mothod is compared with conventional and single-variable methods of neural network and ARIMA. These three combined methods are: (a) neural network and system of simultaneous equations, (b) ARIMA and system of simultaneous equations, (c) neural network and ARIMA and system of simultaneous equations. The results showed that the combined method of ARIMA and simultaneous equation system provides better reslts for 5-year forecasts while the combined method of neural network and ARIMA and simultaneous equation system shows better results for 10-year forecasts.
