نوع مقاله : مقالات پژوهشی

نویسندگان

1 دانشگاه صنعت نفت

2 فردوسی مشهد

چکیده

بررسی روابط بین نفت خام های شاخص با قیمت نفت خام ایران در بازارهای مختلف در تبیین قیمت نفت‌خام کشور در این مناطق (شمال غرب اروپا, مدیترانه و آسیا) بسیار حائز اهمیت می باشد. از آن جایی که مسئولیت قیمت‌گذاری وفروش نفت‌خام به هر یک از مناطق  با امور بین الملل شرکت ملی نفت ایران می‌باشد; لذا ،ارائه یک مدل بهینه برای تعیین قیمت  نفت خام ایران می تواند در کنترل ریسک ناشی از نوسانات قیمت نفت خام وتغییرات درآمد نفتی  کشور موثر باشد.  بدین منظور با استفاده از داده های ماهانه سری زمانی سال‌های 16- 2010 و تکنیک مدل سازی گارچ به مدل قیمت نفت خام سبک ایران در بازارشمال غرب اروپا پرداخته است. پس از برآوردهای مختلف قیمت نفت خام سبک ایران در بازار شمال غرب اروپا، نتایج حاصل از آن نشان می دهد مدلی که در آن تغییرات قیمت نفت‌خام سبک ایران در بازار شمال غرب اروپا تابعی از اختلاف کیفی نفت‌خام سبک عربستان با ایران، تغییرات اختلاف قیمت نفت‌خام برنت موعددار با ICE1 ، تغییرات قیمت نفت‌خام اورال رتردام و تغییرات کانتانگو یا بکواردیشن نفت‌خام برنت می باشد، دارای قابلیت پیش بینی دقیق تری نسبت به سایر مدل ها می باشد.
 

کلیدواژه‌ها

عنوان مقاله [English]

Pricing Model of Iranian Light Crude Oil Prices in the NorthWest European Market

نویسندگان [English]

  • Seyyed Abdollah razavi 1
  • mostafa salami far 2

1 The Petroleum University of Technology

2 ferdousi mashhad

چکیده [English]

Introduction
The Extreme volatility of oil prices was led the numerous disruptions in the world market of oil andtherefore in the world economy in the late twentieth century. Many stocks that oil speculators imported to the market for using the windfall profits affected major oil producers more than any other group because the index of crude oil pricing was always in change. This volatility not only affected their sale market in the spot market but also the obtained revenue from sales of their long-term contracts that were also established a base on the same index. Therefore, manufacturers' revenue faces with instability. Also given the vital role of volatility in financial markets, there has been considerable attention to the analysis of volatility forecasts in recent years.
This paper seeks to investigate the impact of financial markets on the behavior of Iran's light crude oil price in the market of East regions, northwest Europe and the Mediterranean. Due to high share of oil revenues in GDP in Iran, as well as high dependence of government budget to oil revenues, any volatility in world prices of oil creates severe disorders in development plans and annual plans of the country, which in turn is led to structural bottleneck in the long term. 
Methodology
Scuch (1974) for the first time pointed to the importance of macroeconomic and financial factors role in the price of agricultural products. But in all subsequent papers, the exchange rate was known as the only mechanism of transmitting monetary policy to the price of agricultural products Chambers and Just (1981, 1982). Monetary policy, even in a closed economy, also affects the price of products such as agricultural products and other basic commodities. The theory indicates that there exists a relationship between the interest rate and the real spot price of oil over the long-term trend. In the framework of this theory, when interest rate increases, the extraction of natural resources increases. The increase in oil supply intensifies the reduction of oil price. This process continues until the beneficiaries (exporters) believe that oil price closes to the final cost of withdrawal. In this situation, there is the expectation of increasing the real oil price so exports reduce and the real price of oil increases to reach the long-term adjusted level. The current model is the model of investigating the effect of monetary policy on basic commodities such as oil. The model shows the negative impact of real interest rate and the positive impact of the expected growth rate of the money supply. Stevens (1995) argues that there have been developments in the oil market from 1980 onwards and by forming and expanding the oil bourses, the oil market has changed to the competitive market and the mechanism of forming the crude oil price has changed that have increased the prices. Moreover, the increase in non-OPEC production has intensified the price volatility and mechanism of forming prices has basically changed after 1980 in such a way that the previous models of forming crude oil prices cannot justify the changes in this period. Therefore, it is necessary to form novel models to include new conditions.
Frankel (2010) states that one of the important factors in explaining the behavior of crude oil price in short term can be attributed to changing in the interest rate so that in short term changing interest rate causes deviation in the direction of crude oil price from the balanced way. The process is created by changes in the interest rate that is mainly due to the monetary policy of open market in the Federal Reserve, for example, reducing of interest rate encourages the increase of bonds purchase, which in turn its price increases due to the increase of demand for bonds. Because the bond price has an inverse relationship with the interest rate of bonds, causing the leading of cash flow to the futures market, and its price increases. In the meantime, the decision on the purchase of spot or future of crude oil, in particular, will be taken into consideration to purchasers. Thus, if the obtained cost of maintaining crude oil is more than the profit in the futures market, deal with to purchase future contracts. The reverse can also happen. So it can be said that first, the interest rate is caused volatility in the bond market by changing in the monetary market and then, it has affected future markets, stock, and oil. Therefore, in short-term, changes in the interest rate cause the price deviance from the long-term direction. This means that long-term behavior is determined by fundamental factors, but in short-term, it may be the case that the price is lower or higher than the long-term direction that is due to changes of rate interest.
Results
The results of the test showed that the difference between the actual price of Iranian crude oil in the northwest market of Europe with its predicted price based on the first and second models has a more favorable situation than the difference in the third and fourth models. The RMSE index for the first and second models is 1.04 and 1.03, respectively. Since in this method, the index indicates a prediction error, therefore, a model with less error than the other, has higher predictive accuracy. So, it can be said that the second model has better predictive power than other models. Therefore, it is suggested that the National Iranian Oil Company (NIOC) determines its light oil price formula in the northwestern market based on the second model, which will determine the country's crude oil price in this market due to differences in the quality differences of Saudi Arabia's crude oil with Iran, price differences of Brent crude oil with ICE1, usual crude oil price changes and contangoand backwardation Brent structure.

کلیدواژه‌ها [English]

  • brent dated
  • urual
  • defferential of quality
  • sudia crude
  1. بکی حسکوئی، مرتضی. و خواجه وند، فاطمه(1393). پیش بینی نوسانات بازارهای آتی های نفت. فصلنامه دانش مالی تحلیل اوراق بهادار، سال هفتم، 23، 108-75.
  2. کشاورزیان، مریم. و زمانی، مهرزاد. (1389). اثر سرریز نرخ دلار آمریکا بر روی قیمت نفت خام، فصلنامه مطالعات اقتصاد انرژی، سال هفتم، 27، 150-131.
  3. مهاجری، پریسا .(1390) . بررسی روابط قیمتی اسپات و آتی نفت خام وست تگزاس اینترمدیت. فصلنامه تحقیقات مدلسازی اقتصادی ، 5 ، 102-75.
  4. Arouri, M. & Nguyen, D.K. (2010), Oil prices, Stock markets and portfolio investment: evidence from sector analysis in europe over the last decade, Energy policy, 38, 4528-4539.
  5. Chen, Y. C. & Rogoff, K. )2003(, Commodity Currencies Journal of International Economics, 60, 133-160.
  6. Cologni, A. & Manera, M. (2008), Oil Prices, Inflation and Interest Rates in a Structural cointegrated var model for the G-7 countries. Energy economics, 30, 856–888.
  7. Cremer, J. and Salehi Esfahani (1991). The Rise and Fall of Oil Prices: A Competitive View, Working Paper, University of Pennsylvania, Phil.
  8. Enders, W. (2004), Applied econometric time series, University of Alabama, U.S.A.
  9. Ferderer, J. P. (1996), Oil price volatility and the macroeconomy, Journal of Macroeconomics, 18, 1–26.
  10. Filis, G. & Degiannakis, S. & Floros, Ch. (2011), Dynamic correlation between stock market and oil prices: The case of oil-importing and oil-exporting countries, International review of financial analysis, 20.
  11. Frankel, J. )2006"(The Effect of Monetary Policy on Real Commodity Prices, NBER working paper 12713.
  12. Hamilton, J. D. (1994), Time series analysis, Princeton university press.
  13. Horsnell, P. (1990). Oil price differentials:markets in disarray (5rd ed.). Oxford: Oxford Institute for Energy Studies.
  14. Huang, B.N. & Yang, C.W. & Hwang, M.J. (2009) The dynamics of a nonlinear relationship between crude oil spot and futures prices: A multivariate threshold regression approach, Energy economics, 31(1), 91-98.
  15. Kaufmann & Robert, K. (2011), The role of market fundamentals and speculation in recent price changes for crude oil, Energy policy, 39(3), 105-115.
  16. Lardic, S. & Mignon, V. (2006), The impact of oil prices on gdp in european countries: an empirical investigation based on asymmetric cointegration, Energy policy, 34, 3910–3915.
  17. Martell, T. F. & Wolf, A. S. )1987(,Determinants of trading volume in futures markets, The journal of futures markets, 7(3), 233-44.
  18. Taylor, J.S. & Spriggs, J. (1989), Effects of monetary macro-economy on canadian agricultural prices, The Canadian journal of economics, 22(2), 278-289.
  19. Pindyck & Robert, S. (2001), The dynamics of commodity spot and futures markets: a primer, The energy journal, 22(3), 1-29.
  20. Sadorsky, P. (1999), Oil price shocks and stock market activity, Energy Economics, 21(5), 449-469.
  21. Yousefi, A., & Wirjanto, T.S., (2003). Exchange Rate of the U.S. Dollar and the J Curve: the Case of Oil Exporting Countries. Energy Economics, 25, 741-765.
CAPTCHA Image