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      KCI등재

      The Future Is Now: Evidence from Crude Oil Futures Trading

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      https://www.riss.kr/link?id=A106371486

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      다국어 초록 (Multilingual Abstract)

      The global crude oil markets have gained fervent academic attention since Adelman’s (1984) claim of a “great pool”. Recently, the Shanghai International Energy Exchange became one of the largest trading venues in the world within less than a yea...

      The global crude oil markets have gained fervent academic attention since Adelman’s (1984) claim of a “great pool”. Recently, the Shanghai International Energy Exchange became one of the largest trading venues in the world within less than a year since the listing of crude oil futures. In this research, using the frameworks of the vector autoregression and vector error correction models, we analyze the co-movement of crude oil futures prices among Shanghai crude, Brent crude, and West Texas Intermediate (WTI) crude. We find that Shanghai Granger-causes Brent and WTI, but not vice versa. Moreover, vector autoregression and vector error correction models imply that nearly half of Brent and WTI fluctuations originates from Shanghai and the influence peaks out on the fourth trading day following the initial shock. This research contributes to the literature as a documentation of China’s leadership in the price discovery of futures prices of crude oil fragmented across the world.

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      참고문헌 (Reference)

      1 고희운, "상품시장 변동성과 한국주식시장 변동성 간의 상호관계분석" 한국재무관리학회 35 (35): 273-294, 2018

      2 Hammoudeh, S, "Threshold Cointegration Analysis of Crude Oil Benchmarks" 29 : 79-95, 2008

      3 Li, R, "The Integration of China into the World Crude Oil Market since 1998" 39 : 5159-5166, 2011

      4 Spiller, P. T, "The Estimation of Transaction Costs in Arbitrage Models" 39 : 309-326, 1988

      5 Saïd, S. E, "Testing for Unit Roots in Autoregressive-Moving Average Models of Unknown Order" 71 : 599-607, 1984

      6 Meng, M, "Shanghai Exchange Mulls Market Makers to Boost Crude Futures Liquidity"

      7 An, H. Z, "Research on Patterns in the Fluctuation of the Co-movement between Crude Oil Futures and Spot Prices: A Complex Network Approach" 136 : 1067-1075, 2014

      8 Ji, Q, "Oil Price Volatility and Oil-related Events: An Internet Concern Study Perspective" 137 : 256-264, 2015

      9 Sims, C. A, "Macroeconomics and Reality" 48 : 1-48, 1980

      10 Rodriguez, A. E, "Is the World Oil Market ‘One Great Pool’? A Test" 5 : 121-130, 1993

      1 고희운, "상품시장 변동성과 한국주식시장 변동성 간의 상호관계분석" 한국재무관리학회 35 (35): 273-294, 2018

      2 Hammoudeh, S, "Threshold Cointegration Analysis of Crude Oil Benchmarks" 29 : 79-95, 2008

      3 Li, R, "The Integration of China into the World Crude Oil Market since 1998" 39 : 5159-5166, 2011

      4 Spiller, P. T, "The Estimation of Transaction Costs in Arbitrage Models" 39 : 309-326, 1988

      5 Saïd, S. E, "Testing for Unit Roots in Autoregressive-Moving Average Models of Unknown Order" 71 : 599-607, 1984

      6 Meng, M, "Shanghai Exchange Mulls Market Makers to Boost Crude Futures Liquidity"

      7 An, H. Z, "Research on Patterns in the Fluctuation of the Co-movement between Crude Oil Futures and Spot Prices: A Complex Network Approach" 136 : 1067-1075, 2014

      8 Ji, Q, "Oil Price Volatility and Oil-related Events: An Internet Concern Study Perspective" 137 : 256-264, 2015

      9 Sims, C. A, "Macroeconomics and Reality" 48 : 1-48, 1980

      10 Rodriguez, A. E, "Is the World Oil Market ‘One Great Pool’? A Test" 5 : 121-130, 1993

      11 Weiner, R. J, "Is the World Oil Market ‘One Great Pool’" 12 : 95-107, 1991

      12 Liu, L, "Is World Oil Market ‘One Great Pool’? An Example from China’s and International Oil Markets" 35 : 364-373, 2013

      13 Narayan, P. K, "Investigating Price Clustering in the Oil Futures Market" 88 : 397-402, 2011

      14 Granger, C. W. J, "Investigating Causal Relations by Econometric Models and Crossspectral Methods" 37 : 424-438, 1969

      15 Zhang, Y. J, "Interpreting the Crude Oil Price Movements: Evidence from the Markov Regime Switching Model" 143 : 96-109, 2015

      16 Adelman, M. A, "International Oil Agreements" 5 (5): 1-9, 1984

      17 Reboredo, J. C, "How Do Crude Oil Prices Co-move? A Copula Approach" 33 : 948-955, 2011

      18 Engle, R. F, "Forecasting and Testing in Co-integrated Systems" 35 : 143-159, 1987

      19 Ji, Q, "Evolution of the World Crude Oil Market Integration: A Graph Theory Analysis" 53 : 90-100, 2015

      20 Johansen, S, "Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models" 59 : 1551-1580, 1991

      21 Davidson, J. E. H, "Econometric Modelling of the Aggregate Time-Series Relationship between Consumers’ Expenditure and Income in the United Kingdom" 88 : 661-692, 1978

      22 CNPC Economics and Technology Research Institute, "Development Report of Domestic and Foreign Oil and Gas Industry in 2017" 2017

      23 Giulietti, M, "Crude Oil Price Differentials, Product Heterogeneity and Institutional Arrangements" 46 : 28-32, 2014

      24 MacKinnon, J. G, "Critical Values for Cointegration Tests" Queen’s University 2010

      25 Engle, R. F, "Co-integration and Error Correction: Representation, Estimation, and Testing" 55 : 251-276, 1987

      26 Phillips, P. C. B, "Asymptotic Properties of Residual Based Tests for Cointegration" 58 : 165-193, 1990

      27 Kleit, A. N, "Are Regional Oil Markets Growing Closer Together? An Arbitrage Cost Approach" 22 : 1-15, 2001

      28 Spiller, P. T, "Arbitrage during the Dollar-Sterling Gold Standard" 96 : 882-892, 1988

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      학술지 이력

      학술지 이력
      연월일 이력구분 이력상세 등재구분
      2026 평가예정 재인증평가 신청대상 (재인증)
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      2013-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2010-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2008-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2005-01-01 평가 등재학술지 선정 (등재후보2차) KCI등재
      2004-01-01 평가 등재후보 1차 PASS (등재후보1차) KCI등재후보
      2003-01-01 평가 등재후보학술지 선정 (신규평가) KCI등재후보
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      학술지 인용정보

      학술지 인용정보
      기준연도 WOS-KCI 통합IF(2년) KCIF(2년) KCIF(3년)
      2016 0.5 0.5 0.63
      KCIF(4년) KCIF(5년) 중심성지수(3년) 즉시성지수
      0.71 0.72 1.145 0.08
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