• Title/Summary/Keyword: 국제현물원유가

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로테르담 원유가격 무엇이 결정하나

  • Korea Petroleum Association
    • Korea Petroleum Association Journal
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    • no.5 s.39
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    • pp.55-58
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    • 1984
  • 지금부터 4년전 다시 말해 현재의 국제원유시장의 공급과잉 사태가 발생되기전 원유의 현물가격은 공시판매가격보다 웃도는 상태를 지속했었다. 원유가격 형성의 기본이 되는 현물시장의 가격수준이 공시가격의 방향을 예시하는 나침판 역할을 하기 때문에, 원유의 안정적인 공급을 확보키 위한 일환으로 산유국과 장기계약을 선호하던 당시, 석유소비국들은 로테르담 석유시장을 원망하는 태도로 바라보았다. 왜냐하면 악덕거래업자들의 조작으로 현물시장 가격은 계속 상승일로에 있었기 때문이다. 산유국정부들은 표면적으로 가격은 시장의 원리에 입각해서 형성되어야 한다고 주장하면서 그들의 공시가격이 현물가격보다 낮다는 점을 들어 공시가격 수준을 합리화했던 것이다. 이하에서 현물시장 가격 형성에 영향을 주는 요인들을 설명하기로 한다. <편집자 주>

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A Study on Price Discovery Process for International Crude Oil using Error Correction Model and Graph Theory (오차수정모형과 그래프 이론을 이용한 국제유가의 동시 및 단기 가격발견과정에 관한 연구)

  • Park, Hojeong;Yun, Won-Cheol
    • Environmental and Resource Economics Review
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    • v.15 no.3
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    • pp.479-504
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    • 2006
  • This paper analyzes a price discovery process for international crude oils including the WTI, Brent and Dubai. Error correction model is employed considering non-stationarity property of crude oil price and the contemporaneous causality is constructed by graph theory to analyze the short-term causality. The empirical analysis for January 4., 1999 to July 15., 2005 reveals that the Brent price interconnects between the WTI price and the Dubai price. This result implies the substantial influence of the Brent price as a marker oil.

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Analysis of Extreme Values of Daily Percentage Increases and Decreases in Crude Oil Spot Prices (국제현물원유가의 일일 상승 및 하락율의 극단값 분석)

  • Yun, Seok-Hoon
    • The Korean Journal of Applied Statistics
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    • v.23 no.5
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    • pp.835-844
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    • 2010
  • Tools for statistical analysis of extreme values include the classical annual maximum method, the modern threshold method and variants improving the second one. While the annual maximum method is to t th generalized extreme value distribution to the annual maxima of a time series, the threshold method is to the generalized Pareto distribution to the excesses over a high threshold from the series. In this paper we deal with the Poisson-GPD method, a variant of the threshold method with a further assumption that the total number of exceedances follows the Poisson distribution, and apply it to the daily percentage increases and decreases computed from the spot prices of West Texas Intermediate, which were collected from January 4th, 1988 until December 31st, 2009. According to this analysis, the distribution of daily percentage increases as well as decreases turns out to have a heavy tail, unlike the normal distribution, which coincides well with the general phenomenon appearing in the analysis of lots of nowaday nancial data.

Analysis of dependency structure between international freight rate index and crude oil price (국제운임지수와 원유가격의 의존관계 분석)

  • Kim, Bu-Kwon;Kim, Dong-Yoon;Choi, Ki-Hong
    • Journal of Korea Port Economic Association
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    • v.35 no.4
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    • pp.107-120
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    • 2019
  • Crude oil is a resource that is being used as a raw material in major industries, representing the price of the raw material market. It is also an important element that affects the shipping market in terms of fuel costs for freight vessels. As a result, crude oil and freight rates are closely related. Therefore, from January 2009 to June 2019, this study analyzed the dependency structure between oil price (WTI) and freight rates (BDI, BCI, BPI, BSI, and BHI) using daily data. The main results are summarized as follows. First, according to the copula results, survival Gumbel copula in WTI-BDI, Clayton copula in WTI-BCI, Survival Joe copula in WTI-BPI, Joe copula in WTI-BSI, and survival Gumbel copula in WTI-BHI were selected as the best-fitted model. Second, looking at Kendall's tau correlation, there is a positive correlation between BDI and oil price. Furthermore, freight rate index (BCI, BPI, BSI) and oil price show positive dependencies. In particular, the strongest dependence was found in BCI and oil price returns. However, BHI and oil price show a negative dependency. Third, looking at the tail-dependency structure, a pair between oil price and BDI, BCI showed a lower tail-dependency. The pair between oil price and BSI showed the upper tail-dependency.

Comparison of Price Predictive Ability between Futures Market and Expert System for WTI Crude Oil Price (선물시장과 전문가예측시스템의 가격예측력 비교 - WTI 원유가격을 대상으로 -)

  • Yun, Won-Cheol
    • Environmental and Resource Economics Review
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    • v.14 no.1
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    • pp.201-220
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    • 2005
  • Recently, we have been witnessing new records of crude oil price hikes. One question which naturally arises would be the possibility and accuracy of forecasting crude oil prices. This study tries to answer the relative predictability of futures prices compared to the forecasts based on experts system. Using WTI crude oil spot and futures prices, this study performs simple statistical comparisons in forecasting accuracy and a formal test of differences in forecasting errors. According to statistical results, WTI crude oil futures market turns out to be equally efficient relative to EIA experts system. Consequently, WTI crude oil futures market could be utilized as a market-based tool for price forecasting and/or resource allocation for both of petroleum producers and consumers.

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A Study on Price Asymmetries in Local Petroleum Markets (석유제품의 가격 비대칭성에 관한 연구)

  • Kim, Jin Hyung
    • Environmental and Resource Economics Review
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    • v.16 no.4
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    • pp.833-854
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    • 2007
  • Output prices tend to respond faster to input price increases than to decreases. The 'rockets and feathers' hypothesis of asymmetric price behavior in petroleum market is tested by a full adjustment error correction model. Using monthly data for the period January 1977 to June 2006, evidence is found that there is a significant degree of asymmetry in the adjustment of wholesale prices to increases and to decreases in crude oil price. A similar hypothesis in regard to the exchange rate is also rejected by the data. Using weekly data over the period examined, evidence of asymmetry for gasoline, diesel and heating oil is also found in the transmission of price changes from wholesale to retail: retail prices increase more quickly in response to the wholesale price increases than to wholesale price decreases.

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