• مطالعات اقتصادی مرتبط با حاملهای انرژی (فسیلی، تجدیدپذیر و برق)
Moslem Nilchi; Ali Farhadian
Abstract
Crude oil is the main source of energy and accounts for about a third of world energy production. Turmoil in this market will have far-reaching economic and financial consequences. Because of this, investors attach great importance to predicting volatility when investing in crude oil markets to hedge ...
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Crude oil is the main source of energy and accounts for about a third of world energy production. Turmoil in this market will have far-reaching economic and financial consequences. Because of this, investors attach great importance to predicting volatility when investing in crude oil markets to hedge risk and portfolio diversification. However, their investment strategies are often strongly influenced by volatility because, in different periods of crude oil markets, there are high and low fluctuations that are attributed to the movement of economic cycles. Accordingly, the present study compares the Markov Regime Switching (MRS) and Hidden Markov (HM) volatility models with the GJR-GARCH asymmetric model on their forecasting capabilities in the WTI and Brent crude oil markets. Empirical results show that the MRS-GJRGARCH model performs better than the HM_GJRGARCH model in predicting volatility in both markets. Accordingly, using the two criteria of value at risk and the expected deficit, the minimum loss and the expected loss for December 2021 were predicted. The results show that the expected shortfall from investing in the WTI market is greater than the Brent oil market
سیاستگذاریهای اقتصادی و مالی در حوزههای فوقالذکر در سطوح ملی، منطقهای و جهانی
mojtaba rostami; Mohammad Nabi Shahiki Tash
Abstract
Due to the strategic role of volatility and instability of crude oil prices and their effects on all countries of the world, different methods of modeling and forecasting are necessary. Over the past two decades, an extensive literature has emerged on various approaches to empirically modeling volatility ...
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Due to the strategic role of volatility and instability of crude oil prices and their effects on all countries of the world, different methods of modeling and forecasting are necessary. Over the past two decades, an extensive literature has emerged on various approaches to empirically modeling volatility in the crude oil market. In this research, WTI crude oil price volatility modeling, which is one of the most important types of crude oil in the market of this strategic commodity, is examined with six flexible stochastic volatility (SV) models. Then the experimental performance of these models is compared with each other using Bayesian methods. The findings of this study show that adding one jump in efficiency and leverage effect to the stochastic volatility (SVLJ) model greatly improves its performance compared to other models. According to the findings of this model, the stability of volatility in the WTI market is very high and on average one jump occurs in this market every year. However, this model shows that in 2020, two jumps in WTI returns occurred in April and May, which is a unique event. In addition, the correlation between the return jump component and the volatility jump (Merton correlation jump) is not confirmed in the WTI data. Also, due to the negative leverage effect, negative shocks have stronger volatility effects than positive shocks in the crude oil market.
Fatemeh Hajisami; Mohammod Hossin Mahdavi Adeli; Narges Salehnia
Abstract
Among energy carriers, the role of oil is more remarkable in economic development of developed and developing countries. But the fluctuations in oil price, existence of constant challenges between suppliers and demanders, the beginning of descending trend of production and promoting the energy security ...
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Among energy carriers, the role of oil is more remarkable in economic development of developed and developing countries. But the fluctuations in oil price, existence of constant challenges between suppliers and demanders, the beginning of descending trend of production and promoting the energy security in its consuming countries have caused besides oil, its substitutes find specific importance. Development and extraction of unconventional resources on one hand have made changes in reservoirs ranking in different areas of the world and has weakened the dependency of consuming countries and on the other hand, it has affected the changing trend of oil price. In this respect, the present study investigates the causal relationship between oil price and supplying unconventional oil and gas during time period of 2000-2015. Two techniques named Granger technique and Toda and Yamamoto technique have been used to investigate the causal relationship. The results of the research show that in all studying period (2000-2015) the unconventional supply is the strong and direct cause for oil price and the indirect and weak price are introduced as the causes of unconventional supply. Also, based on the results, the strong impact of financial markets on the supply of unconventional resources and oil prices has been achieved. On the other hands the results show that unconventional supply will affect the supply of OPEC in the long term (2000-2015). Therefore, this achievement for OPEC countries, as well as Iran, can be used as a result of a strategic change in production policy.
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
Saeed Shavvalpour; Armin Jabbarzadeh; Hossein Khanjarpanah
Abstract
Crude oil price risk is crucial for oil exporting countries. Consequently, developing a risk hedging mechanism has great importance for these countries. Given that Value at Risk (VaR) is one of the most powerful tools for evaluating price risk, this paper has tried to design a mechanism for risk management ...
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Crude oil price risk is crucial for oil exporting countries. Consequently, developing a risk hedging mechanism has great importance for these countries. Given that Value at Risk (VaR) is one of the most powerful tools for evaluating price risk, this paper has tried to design a mechanism for risk management of Iranian oil revenues using the VaR measure. In this regard, Autoregressive Conditional Heteroskedasticity models including GARCH, CGARCH and EGARCH with different destiny distribution functions are utilized for calculating VaR of OPEC crude oil price in the period of 6 October 2005 to 29 August 2015. The results show that CGARCH model with t-student distribution outperforms the other methods in terms of forecast error measures. The implementation of CGARCH model with using the data of Iranian oil production in 2014 reveals that the proposed model can lead to a significant surplus income.
