石油市场的内外部联系、价格发现与风险管理研究
|
摘要
近年来，石油市场迅猛发展，吸引了大批商业机构和金融机构参与。石油期货市场与现货市场、石油市场与关联市场的联系愈加紧密。次贷危机、能源市场事件等严重冲击了石油市场，造成油价剧烈波动。这些发展态势给石油市场内外部联系、价格发现、风险管理的研究带来新的挑战。而现有研究未能契合石油市场发展动态，存在角度单一、方法局限、结论矛盾等不足。
本文立足石油市场发展动态，关注次贷危机、能源市场事件的影响，发展并改进多种计量经济模型与方法，从多角度分析石油市场内外部联系的新特征、石油期货市场价格发现功能的变化与机理、石油市场风险值预测与分位数建模、石油期货对冲比率的模型选择等问题。主要研究内容与结论如下：
首先，考虑次贷危机、能源市场事件的影响，本文分别从价格、收益率、分位数等层面，采用随机协整检验、非线性协整检验、异方差识别法、风险的Granger因果关系检验等方法考察石油期货价格与现货价格的协整关系、石油价格与天然气价格的协整关系、油价变动对股票市场的影响、石油市场与美元市场的极端风险溢出效应等。研究表明，次贷危机期间石油期货价格与现货价格间存在随机协整关系；石油价格与天然气价格间存在结构变化协整关系和机制转换协整关系；油价变动与股价变动间存在交互作用；次贷危机加强了市场间的风险传导，石油市场与美元市场间存在双向极端风险溢出效应。
其次，针对现有研究关于石油期货市场价格发现功能的不同观点，本文提出了改进的机制转换协整检验方法，采用该方法并结合永久短暂模型分析石油期货市场价格发现功能的变化与机理。研究表明，石油期货市场的价格发现功能在油价平稳（低波动）时较强，在油价波动（高波动）时较弱；石油期货市场的价格发现功能与市场参与者的交易行为有关，投机交易导致价格发现功能弱而套利交易和套期保值交易导致价格发现功能强。
再次，针对CAViaR模型在估计方法和模型形式上的不足，本文提出了贝叶斯CAViaR模型和门限CAViaR模型，并采用这两种新模型分析了石油现货市场的油价VaR以及石油期货市场的收益率的分位数特征。研究表明，贝叶斯CAViaR模型在参数估计和模型检验上具有优势，门限CAViaR模型在刻画分位数动态变化模式方面具有优势；石油现货市场上油价VaR存在自回归特征，并受油价涨跌的不对称影响，且油价下跌的作用更强；石油期货市场上收益率的左尾分位数受油价涨跌影响，而右尾分位数仅受油价下跌影响。
最后，为克服石油期货对冲比率模型选择的困难，考虑石油期货收益与现货收益间存在交互作用以及次贷危机可能造成油价特征变化并影响模型的预测能力，本文采用异方差识别法分析OLS对冲比率的估计偏误，发展结构BEKK模型并估计石油期货对冲比率，并从交互作用和模型风险的角度考察石油期货对冲比率的模型选择问题。研究表明，现货收益对期货收益的反馈效应导致OLS对冲比率的估计偏误；石油期货市场与现货市场间存在收益的交互作用和波动溢出效应，可采用结构BEKK模型刻画；石油期货对冲比率的估计应综合考虑油价走势以及次贷危机的影响，在OLS模型和结构BEKK模型间做出选择。
With the rapid development of international oil markets in recent years,commercial organizations and financial institutions have become major investors in oilmarkets. The relationships between oil spot and futures markets as well as between oilmarkets and linked markets are much stronger. Besides, due to subprime crisis andenergy market events, oil price always fluctuates dramatically. All these trends bringnew challenges to research on the internal and external relations, price discovery andrisk management in oil markets. However, existing researches ignore such trends. Inaddition, these researches often adopt simple methods and obtain inconsistentconclusions from single perspectives.
Based on the oil market trends and influences of subprime crisis and energy marketevents, this dissertation improves and develops econometric methods. This dissertationalso analyzes the internal and external relations in oil markets, price discovery functionof oil futures market and its change, VaR forecasting, quantile modeling and modelselection of oil futures hedge ratio from several perspectives. The conclusions are asfollows.
Firstly, using stochastic cointegration test, nonlinear cointegration test, the methodof Identification through Heteroskedasticity and Granger causality in risk, thisdissertation examines the cointegration relationships between oil spot and futures pricesas well as between oil price and natural gas price, the influence of oil price fluctuationson stock market, the extreme risk spillover effect between oil market and US dollarmarket from price, return and quantile. It is shown that, there is a stochasticcointegration between oil spot and futures prices during subprime crisis, and regimeshifts cointegration and regime switching cointegration between oil price and naturalgas price. The interaction exists between oil price change and stock price change.Subprime crisis strengthens risk contagion, and causes bidirectional extreme riskspillover effect between oil market and US dollar market.
Secondly, according to different opinions of price discovery function in oil futuresmarket, this dissertation proposes an improved regime switching cointegration test. With this method and Permanent-Transitory model, this dissertation analyzes the changeof price discovery function in oil futures market and its reason. It is shown that, theprice discovery function of oil futures market is strong when the volatility of oil price islow and weak when the volatility of oil price is high. Price discovery function of oilfutures market is related to trading behavior of market participants. Speculation tradingwill weaken price discovery function, while arbitrage trading and hedge trading willstrengthen price discovery function.
Thirdly, considering the shortages of CAViaR model in estimating method andmodel specification, this dissertation develops Bayesian CAViaR model and ThresholdCAViaR model. Based on these models, this dissertation analyzes oil price VaR in oilspot market and quantile characteristics of return in oil futures market. It is shown that,Bayesian CAViaR model is easier to estimate parameter and test model specification.And Threshold CAViaR model is better to describe the quantile dynamics. In oil spotmarket, VaR has autoregressive effect and is affected by oil price fluctuation, and theinfluences of oil price’s reducing are stronger than oil price’s increasing. In oil futuresmarket, the left tail quantile of return is affected by oil price fluctuation, but the righttail quantile is only affected by oil price’s reducing.
Finally, existing researches can not provide the effective model selection strategyof oil futures hedge ratio estimation. Considering the interaction of oil spot and futuresreturns and the change of model forecasting ability affected by subprime crisis, thisdissertation analyzes the estimation bias of OLS hedge ratio and develops structuralBEKK model. And this dissertation discusses the model selection of oil futures hedgeratio estimation. It is shown that, due to feedback effect of spot return to futures return,the estimation bias of OLS hedge ratio exists. The return interaction and volatilityspillover effect between oil spot and futures markets can be described by structuralBEKK model. The estimation of oil futures hedge ratio should consider oil pricetendency and the influence of subprime crisis, and select the proper model between OLSmodel and structural BEKK model.
本文立足石油市场发展动态，关注次贷危机、能源市场事件的影响，发展并改进多种计量经济模型与方法，从多角度分析石油市场内外部联系的新特征、石油期货市场价格发现功能的变化与机理、石油市场风险值预测与分位数建模、石油期货对冲比率的模型选择等问题。主要研究内容与结论如下：
首先，考虑次贷危机、能源市场事件的影响，本文分别从价格、收益率、分位数等层面，采用随机协整检验、非线性协整检验、异方差识别法、风险的Granger因果关系检验等方法考察石油期货价格与现货价格的协整关系、石油价格与天然气价格的协整关系、油价变动对股票市场的影响、石油市场与美元市场的极端风险溢出效应等。研究表明，次贷危机期间石油期货价格与现货价格间存在随机协整关系；石油价格与天然气价格间存在结构变化协整关系和机制转换协整关系；油价变动与股价变动间存在交互作用；次贷危机加强了市场间的风险传导，石油市场与美元市场间存在双向极端风险溢出效应。
其次，针对现有研究关于石油期货市场价格发现功能的不同观点，本文提出了改进的机制转换协整检验方法，采用该方法并结合永久短暂模型分析石油期货市场价格发现功能的变化与机理。研究表明，石油期货市场的价格发现功能在油价平稳（低波动）时较强，在油价波动（高波动）时较弱；石油期货市场的价格发现功能与市场参与者的交易行为有关，投机交易导致价格发现功能弱而套利交易和套期保值交易导致价格发现功能强。
再次，针对CAViaR模型在估计方法和模型形式上的不足，本文提出了贝叶斯CAViaR模型和门限CAViaR模型，并采用这两种新模型分析了石油现货市场的油价VaR以及石油期货市场的收益率的分位数特征。研究表明，贝叶斯CAViaR模型在参数估计和模型检验上具有优势，门限CAViaR模型在刻画分位数动态变化模式方面具有优势；石油现货市场上油价VaR存在自回归特征，并受油价涨跌的不对称影响，且油价下跌的作用更强；石油期货市场上收益率的左尾分位数受油价涨跌影响，而右尾分位数仅受油价下跌影响。
最后，为克服石油期货对冲比率模型选择的困难，考虑石油期货收益与现货收益间存在交互作用以及次贷危机可能造成油价特征变化并影响模型的预测能力，本文采用异方差识别法分析OLS对冲比率的估计偏误，发展结构BEKK模型并估计石油期货对冲比率，并从交互作用和模型风险的角度考察石油期货对冲比率的模型选择问题。研究表明，现货收益对期货收益的反馈效应导致OLS对冲比率的估计偏误；石油期货市场与现货市场间存在收益的交互作用和波动溢出效应，可采用结构BEKK模型刻画；石油期货对冲比率的估计应综合考虑油价走势以及次贷危机的影响，在OLS模型和结构BEKK模型间做出选择。
With the rapid development of international oil markets in recent years,commercial organizations and financial institutions have become major investors in oilmarkets. The relationships between oil spot and futures markets as well as between oilmarkets and linked markets are much stronger. Besides, due to subprime crisis andenergy market events, oil price always fluctuates dramatically. All these trends bringnew challenges to research on the internal and external relations, price discovery andrisk management in oil markets. However, existing researches ignore such trends. Inaddition, these researches often adopt simple methods and obtain inconsistentconclusions from single perspectives.
Based on the oil market trends and influences of subprime crisis and energy marketevents, this dissertation improves and develops econometric methods. This dissertationalso analyzes the internal and external relations in oil markets, price discovery functionof oil futures market and its change, VaR forecasting, quantile modeling and modelselection of oil futures hedge ratio from several perspectives. The conclusions are asfollows.
Firstly, using stochastic cointegration test, nonlinear cointegration test, the methodof Identification through Heteroskedasticity and Granger causality in risk, thisdissertation examines the cointegration relationships between oil spot and futures pricesas well as between oil price and natural gas price, the influence of oil price fluctuationson stock market, the extreme risk spillover effect between oil market and US dollarmarket from price, return and quantile. It is shown that, there is a stochasticcointegration between oil spot and futures prices during subprime crisis, and regimeshifts cointegration and regime switching cointegration between oil price and naturalgas price. The interaction exists between oil price change and stock price change.Subprime crisis strengthens risk contagion, and causes bidirectional extreme riskspillover effect between oil market and US dollar market.
Secondly, according to different opinions of price discovery function in oil futuresmarket, this dissertation proposes an improved regime switching cointegration test. With this method and Permanent-Transitory model, this dissertation analyzes the changeof price discovery function in oil futures market and its reason. It is shown that, theprice discovery function of oil futures market is strong when the volatility of oil price islow and weak when the volatility of oil price is high. Price discovery function of oilfutures market is related to trading behavior of market participants. Speculation tradingwill weaken price discovery function, while arbitrage trading and hedge trading willstrengthen price discovery function.
Thirdly, considering the shortages of CAViaR model in estimating method andmodel specification, this dissertation develops Bayesian CAViaR model and ThresholdCAViaR model. Based on these models, this dissertation analyzes oil price VaR in oilspot market and quantile characteristics of return in oil futures market. It is shown that,Bayesian CAViaR model is easier to estimate parameter and test model specification.And Threshold CAViaR model is better to describe the quantile dynamics. In oil spotmarket, VaR has autoregressive effect and is affected by oil price fluctuation, and theinfluences of oil price’s reducing are stronger than oil price’s increasing. In oil futuresmarket, the left tail quantile of return is affected by oil price fluctuation, but the righttail quantile is only affected by oil price’s reducing.
Finally, existing researches can not provide the effective model selection strategyof oil futures hedge ratio estimation. Considering the interaction of oil spot and futuresreturns and the change of model forecasting ability affected by subprime crisis, thisdissertation analyzes the estimation bias of OLS hedge ratio and develops structuralBEKK model. And this dissertation discusses the model selection of oil futures hedgeratio estimation. It is shown that, due to feedback effect of spot return to futures return,the estimation bias of OLS hedge ratio exists. The return interaction and volatilityspillover effect between oil spot and futures markets can be described by structuralBEKK model. The estimation of oil futures hedge ratio should consider oil pricetendency and the influence of subprime crisis, and select the proper model between OLSmodel and structural BEKK model.
引文
[1] R. F. Engle, C. W. J. Granger. Co-integration and error correction: Representation, estimation,and testing. Econometrica,1987,55(2):251-276
[2] S. Johansen, K. Juselius. Maximum likelihood estimation and inference on cointegration-Withapplications to the demand for money. Oxford Bulletin of Economics and Statistics,1990,52(2):169-210
[3] S. Johansen. Likelihood-based inference in cointegrated vector autoregressive models. NewYork: Oxford University Press,1995
[4] S. Johansen. Estimation and hypothesis testing of cointegration vectors in Gaussian vectorautoregressive models. Econometrica,1991,59(6):1551-1580
[5]张喜彬,孙青华,张世英.非线性协整关系及其检验方法研究.系统工程学报,1999,14(1):57-68
[6]孙青华,张喜彬,张世英.非线性协整关系的存在性研究.管理科学学报,2000,3(3):65-74
[7]杨宝臣,张世英.变结构协整建模方法研究.系统工程学报,2002,17(1):26-31
[8]樊智,张世英.非线性协整建模研究及沪深股市实证分析.管理科学学报,2005,8(1):73-77
[9] A. W. Gregory, B. E. Hansen. Residual-based tests for cointegration in models with regimeshifts. Journal of Econometrics,1996,70(1):99-126
[10] J. P. Indjehagopian, F. Lantz, V. Simon. Dynamics of heating oil market prices in Europe.Energy Economics,2000,22(2):225-252
[11] O. M. Villanueva. Spot-forward cointegration, structural breaks and FX market unbiasedness.Journal of International Financial Markets, Institutions and Money,2007,17(1):58-78
[12]项后军,潘锡泉.人民币汇率购买力平价问题的重新研究—基于结构突变检验与变结构协整的视角.数量经济技术经济研究,2010,27(4):48-61
[13] D. Kenourgios, A. Samitas. Equity market integration in emerging Balkan markets. Researchin International Business and Finance,2011,25(3):296-307
[14] H. Mohammadi. Market integration and price transmission in the U.S. natural gas market:From the wellhead to end use markets. Energy Economics,2011,33(2):227-235
[15] B. E. Hansen, B. Seo. Testing for two-regime threshold cointegration in vectorerror-correction models. Journal of Econometrics,2002,110(2):293-318
[16] V. J. Gabriel, Z. Psaradakis, M. Sola. A simple method of testing for cointegration subject tomultiple regime changes. Economic Letters,2002,76(2):213-221
[17] A. Davies. Testing for international equity market integration using regime switchingcointegration technique. Review of Financial Economics,2006,15(4):305-321
[18] M. Chiang. Price discovery and changes in regimes for stock index futures. Global FinanceJournal,2003,14(3):287-301
[19] P. M. Migiakis, F. V. Bekiris. Regime switches between dividend and bond yields.International Review of Financial Analysis,2009,18(4):198-204
[20]苟小菊,王世雷.通货膨胀率和股票收益率的相关性的实证研究—基于马尔可夫转换模型.北京理工大学学报(社会科学版),2009,11(4):50-53
[21] D. Harris, B. McCabe, S. Leybourne. Stochastic cointegration: Estimation and inference.Journal of Econometrics,2002,111(2):363-384
[22] B. McCabe, S. Leybourne, D. Harris. A residual-based test for stochastic cointegration.Economic Theory,2006,22(3):429-456
[23] Z. Xiao. Quantile cointegrating regression. Journal of Econometrics,2009,150(2):248-260
[24] H. J. Bierens. Testing the unit root with drift hypothesis against nonlinear trend stationarity,with an application to the US price level and interest rate. Journal of Econometrics,1997a,81(1):29-64
[25] H. J. Bierens. Nonparametric cointegration analysis. Journal of Econometrics,1997b,77(2):379-404
[26] J. Breitung. Nonparametric tests for unit roots and cointegration. Journal of Econometrics,2002,108(2):343-363
[27] J. Breitung, A. M. R. Taylor. Corrigendum to Nonparametric tests for unit roots andcointegration. Journal of Econometrics,2003,117(2):401-404
[28] S. Maslyuk, R. Smyth. Cointegration between oil spot and future prices of the same anddifferent grades in the presence of structural change. Energy Policy,2009,37(5):1687-1693
[29] C. Lee, J. Zeng. Revisiting the relationship between spot and futures oil prices: Evidence fromquantile cointegrating regression. Energy Economics,2011,33(5):924-935
[30] B. T. Ewing, S. M. Hammoudeh, M. A. Thompson. Examining asymmetric behavior in USpetroleum futures and spot prices. Energy Journal,2006,27(3):9-23
[31] B. Huang, C. Yang, M. Hwang. The dynamics of a nonlinear relationship between crude oilspot and futures prices: A multivariate threshold regression approach. Energy Economics,2009,31(1):91-98
[32] J. B. Lin, C. C. Liang. Testing for threshold cointegration and error correction: Evidence in thepetroleum futures market. Applied Economics,2010,42(22):2897-2907
[33] E. Mamatzakis, P. Remoundos. Testing for adjustment costs and regime shifts in BRENTcrude futures market. Economic Modelling,2011,28(3):1000-1008
[34] A. Serletis, J. Herbert. The message in North American energy prices. Energy Economics,1999,21(5):471-483
[35] A. Serletis, R. Rangel-Ruiz. Testing for common features in North American energy markets.Energy Economics,2004,26(3):401-414
[36] L. J. Bachmeier, J. M. Griffin. Testing for market integration: crude oil, coal, and natural gas.Energy Journal,2006,27(2):55-71
[37] P. R. Hartley, III. KB. Medlock, J. E. Rosthal. The relationship of natural gas to oil prices.Energy Journal,2008,29(3):47-66
[38] I. A. Onour. Natural gas markets: How sensitive are they to crude oil price changes. OPECEnergy Review,2009,33(2):111-124
[39] F. Asche, P. Osmunddsen, M. Sandssmark. The UK market for natural gas, oil and electricity:are the prices decoupled. Energy Journal,2006,27(2):27-40
[40] T. Panagiotidis, E. Rutledge. Oil and gas markets in the UK: Evidence from a cointegratingapproach. Energy Economics,2007,29(2):329-347
[41] C. M. Jones, G. Kaul. Oil and the stock markets. Journal of Finance,1996,51(2):463-491
[42] D. W. Jones, P. N. Leiby, I. K. Paik. Oil price shocks and the macroeconomy: what has beenlearned since1996. Energy Journal,2004,25(2):1-32
[43] N. F. Chen, R. Roll, S. A. Ross. Economic forces and the stock market. Journal of Business,1986,59(3):383-403
[44] Y. Hamao. An empirical examination of the Arbitrage Pricing Theory: Using Japanese data.Japan and the World Economy,1988,1(1):45-61
[45] T. Kaneko, B. S. Lee. Relative importance of economic factors in the US and Japanese stockmarkets. Journal of the Japanese and International Economies,1995,9(3):290-307
[46] W. E. Ferson, C. R. Harvey. Sources of risk and expected returns in global equity markets.Journal of Banking and Finance,1994,18(4):775-803
[47] S. A. Basher, P. Sadorsky. Oil price risk and emerging stock markets. Global Finance Journal,2006,17(2):224-251
[48] P. Sadorsky. Oil price shocks and stock market activity. Energy Economics,1999,21(5):449-469
[49] E. Papapertrou. Oil price shocks, stock market, economic activity and employment in Greece.Energy Economics,2001,23(5):511-532
[50] B. N. Huang, M. J. Hwang, H. P. Peng. The asymmetry of the impact of oil price shocks oneconomic activity: an application of the multivariate threshold model. Energy Economics,2005,27(3):455-476
[51]金洪飞,金荦.石油价格与股票市场的溢出效应—基于中美数据的比较分析.金融研究,2008,(2):83-97
[52] J. Park, R. A. Ratti. Oil price shocks and stock markets in the U.S. and13European countries.Energy Economics,2008,30(5):2587-2608
[53] R. Faff, T. Brailsford. Oil price risk and the Australian stock market. Journal of EnergyFinance and Development,1999,4(1):69-87
[54] P. Sadorsky. Risk factors in stock returns of Canadian oil and gas companies. EnergyEconomics,2001,23(1):17-28
[55] M. M. Boyer, D. Filion. Common and fundamental factors in stock returns of Canadian oiland gas cmpanies. Energy Economics,2007,29(3):428-453
[56] I. El-Sharif, D. Brown, B. Burton, et al. Evidence on the nature and extent of the relationshipbetween oil price and equity values in the UK. Energy Economics,2005,27(6):819-830
[57] M. Nandha, R. Faff. Does oil move equity prices? A global view. Energy Economics,2008,30(3):986-997
[58] S. S. Golub. Oil prices and exchange rates. Economic Journal,1983,93(371):576-593
[59] J. Caprio, P. B. Clark. Oil price shocks in a portfolio-balance model. Journal of Economicsand Business,1983,35(2):221-234
[60] S. Zhou. The response of real exchange rates to various economic shocks. Southern EconomicJournal,1995,61(4):936-954
[61] K. Chaudhuri, B. C. Daniel. Long-run equilibrium real exchange rates and oil prices.Economics Letters,1998,58(2):231-238
[62] R. A. Amano, S. Norden. Oil prices and the rise and fall of the US real exchange rate. Journalof International Money and Finance,1998a,17(2):299-316
[63] R. A. Lizardo, A. V. Mollick. Oil price fluctuations and U.S. dollar exchange rates. EnergyEconomics,2010,32(2):399-408
[64] R. A. Amano, S. Norden. Exchange rates and oil prices. Review of International Economics,1998b,6(4):683-694
[65] M. Camarero, C. Tamarit. Oil prices and Spanish competitiveness: A cointegrated panelanalysis. Journal of Policy Modeling,2002,24(6):591-605
[66] S. Chen, H. Chen. Oil prices and real exchange rates. Energy Economics,2007,29(3):390-404
[67] P. Sadorsky. The empirical relationship between energy futures prices and exchange rates.Energy Economics,2000,22(2):253-266
[68] A. Yousefi, T. S. Wirjanto. The empirical role of the exchange rate on the crude-oil priceformation. Energy Economics,2004,26(5):783-799
[69]佘升翔,陆强,马超群.石油—美元机制及其互动特征的实证研究.系统工程,2010,28(6):36-39
[70] Y. Zhang, Y. Fan, H. Tsai, et al. Spillover effect of US dollar exchange rate on oil prices.Journal of Policy Modeling,2008,30(6):973-991
[71] J. H. Stock, M. W. Watson. Testing for common trends. Journal of the American StatisticalAssociation,1988,83(404):1097-1107
[72] K. D. Garbade, W. L. Silber. Price movements and price discovery in futures and cash markets.Review of Economics and Statistics,1983,65(2):289-297
[73] J. Gonzalo, C. Granger. Estimation of common long-memory components in co-integratedsystems. Journal of Business and Economic Statistics,1995,13(1):27-35
[74] J. Hasbrouck. One security, many markets: Determining the contributions to price discovery.Journal of Finance,1995,50(4):1175-1201
[75] S. L. Green, K. A. Mork. Toward efficiency in the crude-oil market. Journal of AppliedEconometrics,1991,6(1):45-66
[76] J. Quan. Two step testing procedure for price discovery role of future prices. Journal ofFutures Markets,1992,12(2):139-149
[77] I. A. Moosa, N. E. Al-Loughani. Unbiasedness and time varying risk premia in the crude oilfutures market. Energy Economics,1994,16(2):99-105
[78]李海英,马卫锋,罗婷.上海燃料油期货价格发现功能研究—基于GS模型的实证分析.财贸研究,2007,18(2):104-108
[79] A. Serletis, D. Banack. Market efficiency and cointegration: An application to PetroleumMarkets. Review of Futures Markets,1990,9(2):372-385
[80] T. V. Schwarz, A. C. Szakmary. Price discovery in Petroleum Markets: Arbitrage,cointegration, and the time interval of analysis. Journal of Futures Markets,1994,14(2):147-167
[81]王群勇,张晓桐.原油期货市场的价格发现功能—基于信息份额模型的分析.统计与决策,2005,(12):77-79
[82]宋玉华,林治乾.国际石油期货价格与现货价格动态关系的实证研究.中国石油大学学报(社会科学版),2007,23(5):1-5
[83]陈明华,陈蔚.国际石油期货市场价格发现功能研究—基于WTI的实证分析.世界经济与政治论坛,2010,(4):47-61
[84] S. G. Gulen. Efficiency in the crude oil futures market. Journal of Energy Finance andDevelopment,1998,3(1):13-21
[85] W. J. Crowder, A. Hamed. A cointegration test for oil futures market efficiency. Journal ofFutures Markets,1993,13(8):933-941
[86] E. Peroni, R. McNown. Noninformative and informative tests of efficiency in three energyfutures markets. Journal of Futures Markets,1998,18(8):939-964
[87]赵茜,王书平.上海燃料油期货市场价格发现功能的实证研究.运筹与管理,2007,16(2):98-101
[88] R. F. Engle, S. Mangaelli. CAViaR: Conditional autoregressive value at risk by regressionquantiles. Journal of Business and Economic Statistics,2004,22(4):367-381
[89]陈功,程希骏,马利军.基于CAViaR的DCC模型及其对中国股市的实证研究.数学的实践与认识,2009,39(4):75-81
[90]王新宇,宋学锋,吴瑞明.基于AAVS-CAViaR模型的股市风险测量研究.系统工程学报,2010,25(3):326-333
[91] S. Manganelli, R. F. Engle. A comparison of Value-at-Risk models in finance. In: Szego G(Ed.). Risk measures for the21st century. New York: John Wiley&Sons Inc,2004:123-144
[92] Y. Bao, T. Lee, B. Salto lu. Evaluating the predictive performance of Value-at-Risk models inemerging markets: A reality check. Journal of Forecasting,2006,25(2):101-128
[93] R. Fuss, Z. Adams, D. G. Kaiser. The predictive power of Value-at-Risk models in commodityfutures markets. Journal of Asset Management,2010,11(4):261-285
[94] J. D. Cabedo, I. Moya. Estimating oil price ‘Value at Risk’ using the historical simulationapproach. Energy Economics,2003,25(3):239-253
[95] M. Sadeghi, S. Shavvalpour. Energy risk management and value at risk modeling. EnergyPolicy,2006,34(18):3367-3373
[96]张意翔,胥朝阳,成金华.基于VaR方法的中国石油企业跨国并购的价格风险评价.管理学报,2010,7(3):440-444
[97] P. Giot, S. Laurent. Market risk in commodity markets: A VaR approach. Energy Economics,2003,25(5):435-457
[98] A. Costello, E. Asem, E. Gardner. Comparison of historically simulated VaR: Evidence fromoil prices. Energy Economics,2008,30(5):2154-2166
[99] J. C. Hung, M. C. Lee, H. C. Liu. Estimation of value-at-risk for energy commodities viafat-tailed GARCH models. Energy Economics,2008,30(3):1173-1191
[100] Y. Fan, Y. J. Zhang, H. T. Tsai, et al. Estimating ‘Value at Risk’ of crude oil price and itsspillover effect using the GED-GARCH approach Energy Economics,2008,30(6):3156-3171
[101]冯春山,吴家春,蒋馥.应用半参数法计算石油市场风险价值.湖北大学学报(自然科学版),2004,26(3):213-217
[102]潘慧峰,张金水.用VaR度量石油市场的极端风险.运筹与管理,2006,15(5):94-98
[103]孙琳.基于VaR-GARCH模型的石油市场风险管理研究:[硕士学位论文].厦门:厦门大学,2008
[104]沈沛龙,邢通政.基于GARCH模型的WTI和Brent原油价格风险分析.哈尔滨工业大学学报(社会科学版),2010,12(3):88-93
[105]沈沛龙,邢通政.国际油价波动与中国成品油价格风险研究.重庆大学学报(社会科学版),2011,17(1):35-41
[106] T. Krehbiel, L. C. Adkins. Price risk in the NYMEX energy complex: An extreme valueapproach. Journal of Futures Markets,2005,25(4):309-337
[107] V. Marimoutou, B. Raggad, A. Trabelsi. Extreme value theory and value at risk: Application tooil market. Energy Economics,2009,31(4):519-530
[108]刘存柱.石油市场风险管理理论与方法研究:[博士学位论文].天津:天津大学,2004
[109]余炜彬,范英,魏一鸣.基于极值理论的原油市场价格风险VaR的研究.系统工程理论与实践,2007,27(8):12-20
[110] D. Huang, B. Yu, F. Fabozzi, et al. CAViaR-based forecast for oil price risk. EnergyEconomics,2009,31(4):511-518
[111] D. Lien, Y. K. Tse. Some recent developments in futures hedging. Journal of EconomicSurveys,2002,16(3):357-396
[112]华仁海,仲伟俊.期货市场套期保值理论述评.经济学动态,2002,(11):66-69
[113] S. S. Chen, C. F. Lee, K. Shrestha. Futures hedge ratios: A review. Quarterly Review ofEconomics and Finance,2003,43(4):433-465
[114] L. Johnson. The theory of hedging and speculation in commodity futures. Review ofEconomic Studies,1960,27(3):139-151
[115] C. S. Cheung, C. C. Y. Kwan, P. C. Y. Yip. The hedging effectiveness of options and futures: Amean-Gini approach. Journal of Futures Markets,1990,10(1):61-74
[116] A. De Jong, E. De Roon, C. Veld. Out-of-sample hedging effectiveness of currency futures foralternative models and hedging strategies. Journal of Futures Markets,1997,17(7):817-837
[117] L. Bauwens, S. Laurent, J. V. K. Rombouts. Multivariate GARCH model: A survey. Journal ofApplied Econometrics,2006,21(1):79-109
[118]李文君,尹康.多元GARCH模型研究述评.数量经济技术经济研究,2009,(10):138-147
[119]刘志东.多元GARCH模型结构特征、参数估计与假设检验研究综述.数量经济技术经济研究,2010,(9):147-161
[120] T. Bollerslev, R. F. Engle, J. M. Wooldridge. A capital-asset pricing model with time-varyingcovariances. Journal of Political Economy,1988,96(1):116-131
[121] T. Bollerslev. Modeling the coherence in short-term nominal exchange rates: A multivariategeneralized ARCH approach. Review of Economics and Statistics,1990,72(3):498-505
[122] R. F. Engle. Dynamic Conditional Correlation: A simple class of multivariate GARCH models.Journal of Business and Economics Statistics,2002,20(1):339-350
[123] R. F. Engle, K. F. Kroner. Multivariate simultaneous generalized ARCH. Econometric Theory,1995,11(1):122-150
[124] S. Ling, M. McAleer. Asymptotic theory for a vector ARMA-GARCH model. EconometricTheory,2003,19(2):280-310
[125] L. H. Ederington. The hedging performance of the new futures markets. Journal of Finance,1979,34(1):157-170
[126] D. Lien, Y. K. Tse, A. K. C. Tsui. Evaluating the hedging performance of the constantcorrelation GARCH model. Applied Financial Economics,2002,12(11):791-798
[127] M. S. Haigh, M. T. Holt. Crack spread hedging: Accounting for time-varying spillovers in theenergy futures markets. Journal of Applied Econometrics,2002,17(3):269-289
[128] L. Switzer, M. El-khoury. Extreme volatility, speculative efficiency, and the hedgingeffectiveness of the oil futures markets. Journal of Futures Markets,2007,27(1):61-84
[129] Q. Ji, Y. Fan. A dynamic hedging approach for refineries in multiproduct oil markets. Energy,2011,36(2):881-887
[130] A. H. Alizadeh, M. G. Kavussanos, D. A. Menachof. Hedging against bunker pricefluctuations using petroleum futures contracts: Constant versus time-varying hedge ratios.Applied Economics,2004,36(12):1337-1353
[131]冯春山,蒋馥,吴家春.石油期货套期保值套期比选取的研究.系统工程理论方法应用,2005,14(2):190-192
[132]方虹,陈勇.石油期货最优套期保值比率及套期保值绩效的实证研究.中国软科学,2008,(1):125-130
[133]马超群,路文金,李双飞.基于MV-GARCH的石油期货时变套期保值比率研究.统计与决策,2009,(17):15-17
[134] C. L. Chang, M. McAleer, R. Tansuchat. Crude oil hedging strategies using dynamicmultivariate GARCH. Energy Economics,2011,33(5):912-923
[135] A. R. Jalali-Naini, M. K. Manesh. Price volatility, hedging and variable risk premium in thecrude oil market. OPEC Review,2006,30(2):55-70
[136] R. Ripple, I. Moosa. Hedging effectiveness and futures contract maturity: the case of NYMEXcrude oil futures. Applied Financial Economics,2007,17(9):683-689
[137]潘慧峰,吴卫星.基于动态条件相关系数模型的石油市场套期保值比估计.数学的实践与认识,2008,38(6):52-60
[138] A. H. Alizadeh, N. K. Nomikos, P. K. Pouliasis. A Markov regime switching approach forhedging energy commodities. Journal of Banking and Finance,2008,32(9):1970-1983
[139] H. Lee. Regime switching correlation hedging. Journal of Banking and Finance,2010,34(11),2728-2741
[140] J. Hung, Y. Wang, M. Chang, et al. Minimum variance hedging with bivariateregime-switching model for WTI crude oil. Energy,2011,36(5):3050-3057
[141] C. Chang, J. Lai, I. Chuang. Futures hedging effectiveness under the segmentation of bear/bullenergy markets. Energy Economics,2010,32(2):442-449
[142] F. Allen, A. Babus, E. Carletti. Financial crises: theory and evidence. Annual Review ofFinancial Economics,2009,1(1):97-116
[143] F. Allen, E. Carletti. An overview of the crisis: Causes, consequences and solutions.International Review of Finance,2010,10(1):1-26
[144] D. Sornette, R. Woodard, W. Zhou. The2006-2008oil bubble: Evidence of speculation, andprediction. Physica A: Statistical Mechanics and its Applications,2009,388(8):1571-1576
[145] R. K. Kaufmann. The role of market fundamentals and speculation in recent price changes forcrude oil. Energy Policy,2011,39(1):105-115
[146] R. S. Pindyck. The long-run evolution of energy prices. Energy Journal,1999,20(2):1-27
[147] B. Ewing, C. Harter. Co-movements of Alaska North Slope and UK Brent crude oil prices.Applied Economics Letters,2000,7(8):553-558
[148] A. Serletis. Unit root behavior in energy futures prices. Energy Journal,1992,13(2):119-128
[149] J. Lee, J. A. List, M. C. Strazicich. Non-renewable resource prices: deterministic or stochastictrends. Journal of Environmental Economics and Management,2006,51(3):354-370
[150] F. A. Postali, P. Picchetti. Geometric Brownian motion and structural breaks in oil prices: aquantitative analysis. Energy Economics,2006,28(4):506-522
[151] S. Maslyuk, R. Smyth. Unit root properties of crude oil spot and futures prices. Energy Policy,2008,36(7):2591-2600
[152] A. Ghoshray, B. Johnson. Trends in world energy prices. Energy Economics,2010,32(5):1147-1156
[153] C. W. J. Granger, N. R. Swanson. An introduction to stochastic unit-root processes. Journal ofEconometrics,1997,80(1):35-62
[154] B. McCabe, A. Tremayne. Testing a time series for difference stationarity. Annals of Statistics,1995,23(3):1015-1028
[155] S. Leybourne, B. McCabe, A. Tremayne. Can economic time series be differenced tostationarity. Journal of Business and Economic Statistics,1996,14(4):435-446
[156] J. L. Wu, S. L. Chen. Can nominal exchange rates be differenced to stationarity. EconomicsLetters,1997,55(3):397-402
[157] M. F. Bleaney, S. J. Leybourne, P. Mizen. Mean reversion of real exchange rates inhigh-inflation countries. Southern Economic Journal,1999,65(4):839-854
[158] R. Sollis, S. J. Leybourne, P. Newbold. Stochastic unit root modelling of stock price indices.Applied Financial Economics,2000,10(3):311-315
[159] G. Yoon. Stochastic unit roots in the capital asset pricing model. Bulletin of EconomicResearch,2005,57(4):369-389
[160] C. J. Kim, C. R. Nelson. State-Space models with Regime-Switching: Classical andGibbs-Sampling approaches with applications. Cambridge: MIT Press,1999
[161] R. B. Davies. Hypothesis testing when a nuisance parameter is present only under thealternative. Biometrika,1987,74(1):33-43
[162] R. Rigobon. Identification through heteroskedasticity. Review of Economics and Statistics,2003,85(4):777-792
[163] R. Rigobon, B. Sack. Measuring the reaction of monetary policy to the stock market.Quarterly Journal of Economics,2003,118(2):639-669
[164] R. Rigobon, B. Sack. The impact of monetary policy on asset prices. Journal of MonetaryEconomics,2004,51(8):1553-1575
[165] G. M. Caporale, A. Cipollini, P. O. Demetriades. Monetary policy and the exchange rateduring the Asian Crisis: identification through heteroskedasticity. Journal of InternationalMoney and Finance,2005,24(1):39-53
[166] M. T. Bohl, P. L. Siklos, T. Werner. Do central banks react to the stock market? The case of theBundesbank. Journal of Banking and Finance,2007,31(3):719-733
[167] R. Rigobon, B. Sack. The effect of war risk on US financial markets. Journal of Banking andFinance,2005,29(7):1769-1789
[168] W. H. Greene. Econometric analysis. New Jersey: Prentice Hall,2003
[169] Y. Hong, Y. Liu, S. Wang. Granger causality in risk and detection of extreme risk spilloverbetween financial markets. Journal of Econometrics,2009,150(2):271-287
[170]洪永淼,成思危,刘艳辉,汪寿阳.中国股市与世界其他股市之间的大风险溢出效应.经济学(季刊),2004,3(3):703-726
[171]潘慧峰,张金水.国内外石油市场的极端风险溢出检验.中国管理科学,2007,15(3):25-30
[172]周翔,蒋翔林.全球股指现货和期货市场极端风险溢出检验.统计与决策,2009,(17):129-132
[173] R. T. Baillie, G. G. Booth, Y. Tse, et al. Price discovery and common factor models. Journal ofFinancial Markets,2002,5(3):309-321
[174]黄大山,卢祖帝.中国股市风险CAViaR建模的稳定性分析.管理评论,2004,16(5):9-16
[175]丁军军.基于CAViaR方法的我国股票市场风险度量及波动性研究:[硕士学位论文].厦门:厦门大学,2007
[176] J. W. Taylor. Using exponentially weighted quantile regression to estimate Value at Risk andexpected shortfall. Journal of Financial Econometrics,2008,6(3):382-406
[177] D. Huang, B. Yu, Z. Lu, et al. Index-Exciting CAViaR: A new empirical time-varying riskmodel. Studies in Nonlinear Dynamics and Econometrics,2010,14(2): Article1
[178] K. Yu, R. A. Moyeed. Bayesian quantile regression. Statistics and Probability Letters,2001,54(4):437-447
[179] E. G. Tsionas. Bayesian quantile inference. Journal of Statistical Computation and Simulation,2003,73(9):659-674
[180] C. Reed, K. Yu. A partially collapsed Gibbs sampler for Bayesian quantile regression. WorkingPaper, Brunel University,2009
[181]王新宇,宋学锋.间接TARCH-CAViaR模型及其MCMC参数估计与应用.系统工程理论与实践,2008,28(9):46-51
[182]王新宇,宋学锋.基于贝叶斯分位数回归的市场风险测度模型与应用.系统管理学报,2009,18(1):40-48
[183] R. Gerlach, C. Chen, N. Chan. Bayesian time-varying quantile forecasting for Value-at-Risk infinancial markets. Journal of Business and Economic Statistics,2010,29(4):481-492
[184] K. Yu, J. Zhang. A Three-Parameter Asymmetric Laplace distribution and its extension.Communications in Statistics-Theory and Methods,2005,34(9-10):1867-1879
[185] I. Ntzoufras. Bayesian modeling using WinBUGS. New York: John Wiley&Sons Inc,2009
[186] R. Kass, A. Raftery. Bayes factors. Journal of American Statistical Association,1995,90(430):773-795
[187] J. Berkowitz, P. Christoffersen, D. Pelletier. Evaluating Value-at-Risk models with desk-leveldata. Management Science,2009, Articles in Advance:1-15
[188] D. Hendricks. Evaluation of Value-at-Risk models using historical data. Economic PolicyReview,1996,2(1):39-69
[189] R. Koenker, G. Bassett. Regression quantiles. Econometrica,1978,46(1):33-50
[190] R. Koenker. Quantile regression. Cambridge: Cambridge University Press,2005
[191] R. Koenker, Z. Xiao. Quantile autoregression. Journal of American Statistical Association,2006,101(475):980-990
[192] W. M. Fong, K. H. See. A Markov switching model of the conditional volatility of crude oilfutures prices. Energy Economics,2002,24(1):71-95
[193] P. Sadorsky. Modeling and forecasting petroleum futures volatility. Energy Economics,2006,28(4):467-488
[194] C. W. Cheong. Modeling and forecasting crude oil markets using ARCH-type models. EnergyPolicy,2009,37(6):2346-2355
[195] S. H. Kang, S. M. Kang, S. M. Yoon. Forecasting volatility of crude oil markets. EnergyEconomics,2009,31(1):119-125
[196] P. Agnolucci. Volatility in crude oil futures: A comparison of the predictive ability of GARCHand implied volatility models. Energy Economics,2009,31(2):316-321
[197] Y. Wei, Y. Wang, D. Huang. Forecasting crude oil market volatility: Further evidence usingGARCH-class models. Energy Economics,2010,32(6):1477-1484
[198] J. Hill, T. Schneeweis. A note on the hedging effectiveness of foreign currency futures. Journalof Futures Markets,1981,1(4):659-664
[199] T. Grammatikos, A. Saunders. Stability and the hedging performance of foreign currencyfutures. Journal of Futures Markets,1983,3(3):295-305
[200] D. E. Bell, W. S. Krasker. Estimating hedge ratios. Financial Management,1986,15(2):34-39
[201] R. J. Myers, S. R. Thompson. Generalized optimal hedge ratio estimation. American Journalof Agricultural Economics,1989,71(4):858-868
[202] P. Sercu, X. Wu. Cross-and Delta-Hedges: Regression-versus price-based hedge ratios.Journal of Banking and Finance,2000,24(5):735-757
[203] J. Miffre. Conditional OLS minimum variance hedge ratios. Journal of Futures Markets,2004,24(10):945-964
[204] R. Rigobon. The curse of non-investment grade countries. Journal of Development Economics,2002,69(2):423-449
[2] S. Johansen, K. Juselius. Maximum likelihood estimation and inference on cointegration-Withapplications to the demand for money. Oxford Bulletin of Economics and Statistics,1990,52(2):169-210
[3] S. Johansen. Likelihood-based inference in cointegrated vector autoregressive models. NewYork: Oxford University Press,1995
[4] S. Johansen. Estimation and hypothesis testing of cointegration vectors in Gaussian vectorautoregressive models. Econometrica,1991,59(6):1551-1580
[5]张喜彬,孙青华,张世英.非线性协整关系及其检验方法研究.系统工程学报,1999,14(1):57-68
[6]孙青华,张喜彬,张世英.非线性协整关系的存在性研究.管理科学学报,2000,3(3):65-74
[7]杨宝臣,张世英.变结构协整建模方法研究.系统工程学报,2002,17(1):26-31
[8]樊智,张世英.非线性协整建模研究及沪深股市实证分析.管理科学学报,2005,8(1):73-77
[9] A. W. Gregory, B. E. Hansen. Residual-based tests for cointegration in models with regimeshifts. Journal of Econometrics,1996,70(1):99-126
[10] J. P. Indjehagopian, F. Lantz, V. Simon. Dynamics of heating oil market prices in Europe.Energy Economics,2000,22(2):225-252
[11] O. M. Villanueva. Spot-forward cointegration, structural breaks and FX market unbiasedness.Journal of International Financial Markets, Institutions and Money,2007,17(1):58-78
[12]项后军,潘锡泉.人民币汇率购买力平价问题的重新研究—基于结构突变检验与变结构协整的视角.数量经济技术经济研究,2010,27(4):48-61
[13] D. Kenourgios, A. Samitas. Equity market integration in emerging Balkan markets. Researchin International Business and Finance,2011,25(3):296-307
[14] H. Mohammadi. Market integration and price transmission in the U.S. natural gas market:From the wellhead to end use markets. Energy Economics,2011,33(2):227-235
[15] B. E. Hansen, B. Seo. Testing for two-regime threshold cointegration in vectorerror-correction models. Journal of Econometrics,2002,110(2):293-318
[16] V. J. Gabriel, Z. Psaradakis, M. Sola. A simple method of testing for cointegration subject tomultiple regime changes. Economic Letters,2002,76(2):213-221
[17] A. Davies. Testing for international equity market integration using regime switchingcointegration technique. Review of Financial Economics,2006,15(4):305-321
[18] M. Chiang. Price discovery and changes in regimes for stock index futures. Global FinanceJournal,2003,14(3):287-301
[19] P. M. Migiakis, F. V. Bekiris. Regime switches between dividend and bond yields.International Review of Financial Analysis,2009,18(4):198-204
[20]苟小菊,王世雷.通货膨胀率和股票收益率的相关性的实证研究—基于马尔可夫转换模型.北京理工大学学报(社会科学版),2009,11(4):50-53
[21] D. Harris, B. McCabe, S. Leybourne. Stochastic cointegration: Estimation and inference.Journal of Econometrics,2002,111(2):363-384
[22] B. McCabe, S. Leybourne, D. Harris. A residual-based test for stochastic cointegration.Economic Theory,2006,22(3):429-456
[23] Z. Xiao. Quantile cointegrating regression. Journal of Econometrics,2009,150(2):248-260
[24] H. J. Bierens. Testing the unit root with drift hypothesis against nonlinear trend stationarity,with an application to the US price level and interest rate. Journal of Econometrics,1997a,81(1):29-64
[25] H. J. Bierens. Nonparametric cointegration analysis. Journal of Econometrics,1997b,77(2):379-404
[26] J. Breitung. Nonparametric tests for unit roots and cointegration. Journal of Econometrics,2002,108(2):343-363
[27] J. Breitung, A. M. R. Taylor. Corrigendum to Nonparametric tests for unit roots andcointegration. Journal of Econometrics,2003,117(2):401-404
[28] S. Maslyuk, R. Smyth. Cointegration between oil spot and future prices of the same anddifferent grades in the presence of structural change. Energy Policy,2009,37(5):1687-1693
[29] C. Lee, J. Zeng. Revisiting the relationship between spot and futures oil prices: Evidence fromquantile cointegrating regression. Energy Economics,2011,33(5):924-935
[30] B. T. Ewing, S. M. Hammoudeh, M. A. Thompson. Examining asymmetric behavior in USpetroleum futures and spot prices. Energy Journal,2006,27(3):9-23
[31] B. Huang, C. Yang, M. Hwang. The dynamics of a nonlinear relationship between crude oilspot and futures prices: A multivariate threshold regression approach. Energy Economics,2009,31(1):91-98
[32] J. B. Lin, C. C. Liang. Testing for threshold cointegration and error correction: Evidence in thepetroleum futures market. Applied Economics,2010,42(22):2897-2907
[33] E. Mamatzakis, P. Remoundos. Testing for adjustment costs and regime shifts in BRENTcrude futures market. Economic Modelling,2011,28(3):1000-1008
[34] A. Serletis, J. Herbert. The message in North American energy prices. Energy Economics,1999,21(5):471-483
[35] A. Serletis, R. Rangel-Ruiz. Testing for common features in North American energy markets.Energy Economics,2004,26(3):401-414
[36] L. J. Bachmeier, J. M. Griffin. Testing for market integration: crude oil, coal, and natural gas.Energy Journal,2006,27(2):55-71
[37] P. R. Hartley, III. KB. Medlock, J. E. Rosthal. The relationship of natural gas to oil prices.Energy Journal,2008,29(3):47-66
[38] I. A. Onour. Natural gas markets: How sensitive are they to crude oil price changes. OPECEnergy Review,2009,33(2):111-124
[39] F. Asche, P. Osmunddsen, M. Sandssmark. The UK market for natural gas, oil and electricity:are the prices decoupled. Energy Journal,2006,27(2):27-40
[40] T. Panagiotidis, E. Rutledge. Oil and gas markets in the UK: Evidence from a cointegratingapproach. Energy Economics,2007,29(2):329-347
[41] C. M. Jones, G. Kaul. Oil and the stock markets. Journal of Finance,1996,51(2):463-491
[42] D. W. Jones, P. N. Leiby, I. K. Paik. Oil price shocks and the macroeconomy: what has beenlearned since1996. Energy Journal,2004,25(2):1-32
[43] N. F. Chen, R. Roll, S. A. Ross. Economic forces and the stock market. Journal of Business,1986,59(3):383-403
[44] Y. Hamao. An empirical examination of the Arbitrage Pricing Theory: Using Japanese data.Japan and the World Economy,1988,1(1):45-61
[45] T. Kaneko, B. S. Lee. Relative importance of economic factors in the US and Japanese stockmarkets. Journal of the Japanese and International Economies,1995,9(3):290-307
[46] W. E. Ferson, C. R. Harvey. Sources of risk and expected returns in global equity markets.Journal of Banking and Finance,1994,18(4):775-803
[47] S. A. Basher, P. Sadorsky. Oil price risk and emerging stock markets. Global Finance Journal,2006,17(2):224-251
[48] P. Sadorsky. Oil price shocks and stock market activity. Energy Economics,1999,21(5):449-469
[49] E. Papapertrou. Oil price shocks, stock market, economic activity and employment in Greece.Energy Economics,2001,23(5):511-532
[50] B. N. Huang, M. J. Hwang, H. P. Peng. The asymmetry of the impact of oil price shocks oneconomic activity: an application of the multivariate threshold model. Energy Economics,2005,27(3):455-476
[51]金洪飞,金荦.石油价格与股票市场的溢出效应—基于中美数据的比较分析.金融研究,2008,(2):83-97
[52] J. Park, R. A. Ratti. Oil price shocks and stock markets in the U.S. and13European countries.Energy Economics,2008,30(5):2587-2608
[53] R. Faff, T. Brailsford. Oil price risk and the Australian stock market. Journal of EnergyFinance and Development,1999,4(1):69-87
[54] P. Sadorsky. Risk factors in stock returns of Canadian oil and gas companies. EnergyEconomics,2001,23(1):17-28
[55] M. M. Boyer, D. Filion. Common and fundamental factors in stock returns of Canadian oiland gas cmpanies. Energy Economics,2007,29(3):428-453
[56] I. El-Sharif, D. Brown, B. Burton, et al. Evidence on the nature and extent of the relationshipbetween oil price and equity values in the UK. Energy Economics,2005,27(6):819-830
[57] M. Nandha, R. Faff. Does oil move equity prices? A global view. Energy Economics,2008,30(3):986-997
[58] S. S. Golub. Oil prices and exchange rates. Economic Journal,1983,93(371):576-593
[59] J. Caprio, P. B. Clark. Oil price shocks in a portfolio-balance model. Journal of Economicsand Business,1983,35(2):221-234
[60] S. Zhou. The response of real exchange rates to various economic shocks. Southern EconomicJournal,1995,61(4):936-954
[61] K. Chaudhuri, B. C. Daniel. Long-run equilibrium real exchange rates and oil prices.Economics Letters,1998,58(2):231-238
[62] R. A. Amano, S. Norden. Oil prices and the rise and fall of the US real exchange rate. Journalof International Money and Finance,1998a,17(2):299-316
[63] R. A. Lizardo, A. V. Mollick. Oil price fluctuations and U.S. dollar exchange rates. EnergyEconomics,2010,32(2):399-408
[64] R. A. Amano, S. Norden. Exchange rates and oil prices. Review of International Economics,1998b,6(4):683-694
[65] M. Camarero, C. Tamarit. Oil prices and Spanish competitiveness: A cointegrated panelanalysis. Journal of Policy Modeling,2002,24(6):591-605
[66] S. Chen, H. Chen. Oil prices and real exchange rates. Energy Economics,2007,29(3):390-404
[67] P. Sadorsky. The empirical relationship between energy futures prices and exchange rates.Energy Economics,2000,22(2):253-266
[68] A. Yousefi, T. S. Wirjanto. The empirical role of the exchange rate on the crude-oil priceformation. Energy Economics,2004,26(5):783-799
[69]佘升翔,陆强,马超群.石油—美元机制及其互动特征的实证研究.系统工程,2010,28(6):36-39
[70] Y. Zhang, Y. Fan, H. Tsai, et al. Spillover effect of US dollar exchange rate on oil prices.Journal of Policy Modeling,2008,30(6):973-991
[71] J. H. Stock, M. W. Watson. Testing for common trends. Journal of the American StatisticalAssociation,1988,83(404):1097-1107
[72] K. D. Garbade, W. L. Silber. Price movements and price discovery in futures and cash markets.Review of Economics and Statistics,1983,65(2):289-297
[73] J. Gonzalo, C. Granger. Estimation of common long-memory components in co-integratedsystems. Journal of Business and Economic Statistics,1995,13(1):27-35
[74] J. Hasbrouck. One security, many markets: Determining the contributions to price discovery.Journal of Finance,1995,50(4):1175-1201
[75] S. L. Green, K. A. Mork. Toward efficiency in the crude-oil market. Journal of AppliedEconometrics,1991,6(1):45-66
[76] J. Quan. Two step testing procedure for price discovery role of future prices. Journal ofFutures Markets,1992,12(2):139-149
[77] I. A. Moosa, N. E. Al-Loughani. Unbiasedness and time varying risk premia in the crude oilfutures market. Energy Economics,1994,16(2):99-105
[78]李海英,马卫锋,罗婷.上海燃料油期货价格发现功能研究—基于GS模型的实证分析.财贸研究,2007,18(2):104-108
[79] A. Serletis, D. Banack. Market efficiency and cointegration: An application to PetroleumMarkets. Review of Futures Markets,1990,9(2):372-385
[80] T. V. Schwarz, A. C. Szakmary. Price discovery in Petroleum Markets: Arbitrage,cointegration, and the time interval of analysis. Journal of Futures Markets,1994,14(2):147-167
[81]王群勇,张晓桐.原油期货市场的价格发现功能—基于信息份额模型的分析.统计与决策,2005,(12):77-79
[82]宋玉华,林治乾.国际石油期货价格与现货价格动态关系的实证研究.中国石油大学学报(社会科学版),2007,23(5):1-5
[83]陈明华,陈蔚.国际石油期货市场价格发现功能研究—基于WTI的实证分析.世界经济与政治论坛,2010,(4):47-61
[84] S. G. Gulen. Efficiency in the crude oil futures market. Journal of Energy Finance andDevelopment,1998,3(1):13-21
[85] W. J. Crowder, A. Hamed. A cointegration test for oil futures market efficiency. Journal ofFutures Markets,1993,13(8):933-941
[86] E. Peroni, R. McNown. Noninformative and informative tests of efficiency in three energyfutures markets. Journal of Futures Markets,1998,18(8):939-964
[87]赵茜,王书平.上海燃料油期货市场价格发现功能的实证研究.运筹与管理,2007,16(2):98-101
[88] R. F. Engle, S. Mangaelli. CAViaR: Conditional autoregressive value at risk by regressionquantiles. Journal of Business and Economic Statistics,2004,22(4):367-381
[89]陈功,程希骏,马利军.基于CAViaR的DCC模型及其对中国股市的实证研究.数学的实践与认识,2009,39(4):75-81
[90]王新宇,宋学锋,吴瑞明.基于AAVS-CAViaR模型的股市风险测量研究.系统工程学报,2010,25(3):326-333
[91] S. Manganelli, R. F. Engle. A comparison of Value-at-Risk models in finance. In: Szego G(Ed.). Risk measures for the21st century. New York: John Wiley&Sons Inc,2004:123-144
[92] Y. Bao, T. Lee, B. Salto lu. Evaluating the predictive performance of Value-at-Risk models inemerging markets: A reality check. Journal of Forecasting,2006,25(2):101-128
[93] R. Fuss, Z. Adams, D. G. Kaiser. The predictive power of Value-at-Risk models in commodityfutures markets. Journal of Asset Management,2010,11(4):261-285
[94] J. D. Cabedo, I. Moya. Estimating oil price ‘Value at Risk’ using the historical simulationapproach. Energy Economics,2003,25(3):239-253
[95] M. Sadeghi, S. Shavvalpour. Energy risk management and value at risk modeling. EnergyPolicy,2006,34(18):3367-3373
[96]张意翔,胥朝阳,成金华.基于VaR方法的中国石油企业跨国并购的价格风险评价.管理学报,2010,7(3):440-444
[97] P. Giot, S. Laurent. Market risk in commodity markets: A VaR approach. Energy Economics,2003,25(5):435-457
[98] A. Costello, E. Asem, E. Gardner. Comparison of historically simulated VaR: Evidence fromoil prices. Energy Economics,2008,30(5):2154-2166
[99] J. C. Hung, M. C. Lee, H. C. Liu. Estimation of value-at-risk for energy commodities viafat-tailed GARCH models. Energy Economics,2008,30(3):1173-1191
[100] Y. Fan, Y. J. Zhang, H. T. Tsai, et al. Estimating ‘Value at Risk’ of crude oil price and itsspillover effect using the GED-GARCH approach Energy Economics,2008,30(6):3156-3171
[101]冯春山,吴家春,蒋馥.应用半参数法计算石油市场风险价值.湖北大学学报(自然科学版),2004,26(3):213-217
[102]潘慧峰,张金水.用VaR度量石油市场的极端风险.运筹与管理,2006,15(5):94-98
[103]孙琳.基于VaR-GARCH模型的石油市场风险管理研究:[硕士学位论文].厦门:厦门大学,2008
[104]沈沛龙,邢通政.基于GARCH模型的WTI和Brent原油价格风险分析.哈尔滨工业大学学报(社会科学版),2010,12(3):88-93
[105]沈沛龙,邢通政.国际油价波动与中国成品油价格风险研究.重庆大学学报(社会科学版),2011,17(1):35-41
[106] T. Krehbiel, L. C. Adkins. Price risk in the NYMEX energy complex: An extreme valueapproach. Journal of Futures Markets,2005,25(4):309-337
[107] V. Marimoutou, B. Raggad, A. Trabelsi. Extreme value theory and value at risk: Application tooil market. Energy Economics,2009,31(4):519-530
[108]刘存柱.石油市场风险管理理论与方法研究:[博士学位论文].天津:天津大学,2004
[109]余炜彬,范英,魏一鸣.基于极值理论的原油市场价格风险VaR的研究.系统工程理论与实践,2007,27(8):12-20
[110] D. Huang, B. Yu, F. Fabozzi, et al. CAViaR-based forecast for oil price risk. EnergyEconomics,2009,31(4):511-518
[111] D. Lien, Y. K. Tse. Some recent developments in futures hedging. Journal of EconomicSurveys,2002,16(3):357-396
[112]华仁海,仲伟俊.期货市场套期保值理论述评.经济学动态,2002,(11):66-69
[113] S. S. Chen, C. F. Lee, K. Shrestha. Futures hedge ratios: A review. Quarterly Review ofEconomics and Finance,2003,43(4):433-465
[114] L. Johnson. The theory of hedging and speculation in commodity futures. Review ofEconomic Studies,1960,27(3):139-151
[115] C. S. Cheung, C. C. Y. Kwan, P. C. Y. Yip. The hedging effectiveness of options and futures: Amean-Gini approach. Journal of Futures Markets,1990,10(1):61-74
[116] A. De Jong, E. De Roon, C. Veld. Out-of-sample hedging effectiveness of currency futures foralternative models and hedging strategies. Journal of Futures Markets,1997,17(7):817-837
[117] L. Bauwens, S. Laurent, J. V. K. Rombouts. Multivariate GARCH model: A survey. Journal ofApplied Econometrics,2006,21(1):79-109
[118]李文君,尹康.多元GARCH模型研究述评.数量经济技术经济研究,2009,(10):138-147
[119]刘志东.多元GARCH模型结构特征、参数估计与假设检验研究综述.数量经济技术经济研究,2010,(9):147-161
[120] T. Bollerslev, R. F. Engle, J. M. Wooldridge. A capital-asset pricing model with time-varyingcovariances. Journal of Political Economy,1988,96(1):116-131
[121] T. Bollerslev. Modeling the coherence in short-term nominal exchange rates: A multivariategeneralized ARCH approach. Review of Economics and Statistics,1990,72(3):498-505
[122] R. F. Engle. Dynamic Conditional Correlation: A simple class of multivariate GARCH models.Journal of Business and Economics Statistics,2002,20(1):339-350
[123] R. F. Engle, K. F. Kroner. Multivariate simultaneous generalized ARCH. Econometric Theory,1995,11(1):122-150
[124] S. Ling, M. McAleer. Asymptotic theory for a vector ARMA-GARCH model. EconometricTheory,2003,19(2):280-310
[125] L. H. Ederington. The hedging performance of the new futures markets. Journal of Finance,1979,34(1):157-170
[126] D. Lien, Y. K. Tse, A. K. C. Tsui. Evaluating the hedging performance of the constantcorrelation GARCH model. Applied Financial Economics,2002,12(11):791-798
[127] M. S. Haigh, M. T. Holt. Crack spread hedging: Accounting for time-varying spillovers in theenergy futures markets. Journal of Applied Econometrics,2002,17(3):269-289
[128] L. Switzer, M. El-khoury. Extreme volatility, speculative efficiency, and the hedgingeffectiveness of the oil futures markets. Journal of Futures Markets,2007,27(1):61-84
[129] Q. Ji, Y. Fan. A dynamic hedging approach for refineries in multiproduct oil markets. Energy,2011,36(2):881-887
[130] A. H. Alizadeh, M. G. Kavussanos, D. A. Menachof. Hedging against bunker pricefluctuations using petroleum futures contracts: Constant versus time-varying hedge ratios.Applied Economics,2004,36(12):1337-1353
[131]冯春山,蒋馥,吴家春.石油期货套期保值套期比选取的研究.系统工程理论方法应用,2005,14(2):190-192
[132]方虹,陈勇.石油期货最优套期保值比率及套期保值绩效的实证研究.中国软科学,2008,(1):125-130
[133]马超群,路文金,李双飞.基于MV-GARCH的石油期货时变套期保值比率研究.统计与决策,2009,(17):15-17
[134] C. L. Chang, M. McAleer, R. Tansuchat. Crude oil hedging strategies using dynamicmultivariate GARCH. Energy Economics,2011,33(5):912-923
[135] A. R. Jalali-Naini, M. K. Manesh. Price volatility, hedging and variable risk premium in thecrude oil market. OPEC Review,2006,30(2):55-70
[136] R. Ripple, I. Moosa. Hedging effectiveness and futures contract maturity: the case of NYMEXcrude oil futures. Applied Financial Economics,2007,17(9):683-689
[137]潘慧峰,吴卫星.基于动态条件相关系数模型的石油市场套期保值比估计.数学的实践与认识,2008,38(6):52-60
[138] A. H. Alizadeh, N. K. Nomikos, P. K. Pouliasis. A Markov regime switching approach forhedging energy commodities. Journal of Banking and Finance,2008,32(9):1970-1983
[139] H. Lee. Regime switching correlation hedging. Journal of Banking and Finance,2010,34(11),2728-2741
[140] J. Hung, Y. Wang, M. Chang, et al. Minimum variance hedging with bivariateregime-switching model for WTI crude oil. Energy,2011,36(5):3050-3057
[141] C. Chang, J. Lai, I. Chuang. Futures hedging effectiveness under the segmentation of bear/bullenergy markets. Energy Economics,2010,32(2):442-449
[142] F. Allen, A. Babus, E. Carletti. Financial crises: theory and evidence. Annual Review ofFinancial Economics,2009,1(1):97-116
[143] F. Allen, E. Carletti. An overview of the crisis: Causes, consequences and solutions.International Review of Finance,2010,10(1):1-26
[144] D. Sornette, R. Woodard, W. Zhou. The2006-2008oil bubble: Evidence of speculation, andprediction. Physica A: Statistical Mechanics and its Applications,2009,388(8):1571-1576
[145] R. K. Kaufmann. The role of market fundamentals and speculation in recent price changes forcrude oil. Energy Policy,2011,39(1):105-115
[146] R. S. Pindyck. The long-run evolution of energy prices. Energy Journal,1999,20(2):1-27
[147] B. Ewing, C. Harter. Co-movements of Alaska North Slope and UK Brent crude oil prices.Applied Economics Letters,2000,7(8):553-558
[148] A. Serletis. Unit root behavior in energy futures prices. Energy Journal,1992,13(2):119-128
[149] J. Lee, J. A. List, M. C. Strazicich. Non-renewable resource prices: deterministic or stochastictrends. Journal of Environmental Economics and Management,2006,51(3):354-370
[150] F. A. Postali, P. Picchetti. Geometric Brownian motion and structural breaks in oil prices: aquantitative analysis. Energy Economics,2006,28(4):506-522
[151] S. Maslyuk, R. Smyth. Unit root properties of crude oil spot and futures prices. Energy Policy,2008,36(7):2591-2600
[152] A. Ghoshray, B. Johnson. Trends in world energy prices. Energy Economics,2010,32(5):1147-1156
[153] C. W. J. Granger, N. R. Swanson. An introduction to stochastic unit-root processes. Journal ofEconometrics,1997,80(1):35-62
[154] B. McCabe, A. Tremayne. Testing a time series for difference stationarity. Annals of Statistics,1995,23(3):1015-1028
[155] S. Leybourne, B. McCabe, A. Tremayne. Can economic time series be differenced tostationarity. Journal of Business and Economic Statistics,1996,14(4):435-446
[156] J. L. Wu, S. L. Chen. Can nominal exchange rates be differenced to stationarity. EconomicsLetters,1997,55(3):397-402
[157] M. F. Bleaney, S. J. Leybourne, P. Mizen. Mean reversion of real exchange rates inhigh-inflation countries. Southern Economic Journal,1999,65(4):839-854
[158] R. Sollis, S. J. Leybourne, P. Newbold. Stochastic unit root modelling of stock price indices.Applied Financial Economics,2000,10(3):311-315
[159] G. Yoon. Stochastic unit roots in the capital asset pricing model. Bulletin of EconomicResearch,2005,57(4):369-389
[160] C. J. Kim, C. R. Nelson. State-Space models with Regime-Switching: Classical andGibbs-Sampling approaches with applications. Cambridge: MIT Press,1999
[161] R. B. Davies. Hypothesis testing when a nuisance parameter is present only under thealternative. Biometrika,1987,74(1):33-43
[162] R. Rigobon. Identification through heteroskedasticity. Review of Economics and Statistics,2003,85(4):777-792
[163] R. Rigobon, B. Sack. Measuring the reaction of monetary policy to the stock market.Quarterly Journal of Economics,2003,118(2):639-669
[164] R. Rigobon, B. Sack. The impact of monetary policy on asset prices. Journal of MonetaryEconomics,2004,51(8):1553-1575
[165] G. M. Caporale, A. Cipollini, P. O. Demetriades. Monetary policy and the exchange rateduring the Asian Crisis: identification through heteroskedasticity. Journal of InternationalMoney and Finance,2005,24(1):39-53
[166] M. T. Bohl, P. L. Siklos, T. Werner. Do central banks react to the stock market? The case of theBundesbank. Journal of Banking and Finance,2007,31(3):719-733
[167] R. Rigobon, B. Sack. The effect of war risk on US financial markets. Journal of Banking andFinance,2005,29(7):1769-1789
[168] W. H. Greene. Econometric analysis. New Jersey: Prentice Hall,2003
[169] Y. Hong, Y. Liu, S. Wang. Granger causality in risk and detection of extreme risk spilloverbetween financial markets. Journal of Econometrics,2009,150(2):271-287
[170]洪永淼,成思危,刘艳辉,汪寿阳.中国股市与世界其他股市之间的大风险溢出效应.经济学(季刊),2004,3(3):703-726
[171]潘慧峰,张金水.国内外石油市场的极端风险溢出检验.中国管理科学,2007,15(3):25-30
[172]周翔,蒋翔林.全球股指现货和期货市场极端风险溢出检验.统计与决策,2009,(17):129-132
[173] R. T. Baillie, G. G. Booth, Y. Tse, et al. Price discovery and common factor models. Journal ofFinancial Markets,2002,5(3):309-321
[174]黄大山,卢祖帝.中国股市风险CAViaR建模的稳定性分析.管理评论,2004,16(5):9-16
[175]丁军军.基于CAViaR方法的我国股票市场风险度量及波动性研究:[硕士学位论文].厦门:厦门大学,2007
[176] J. W. Taylor. Using exponentially weighted quantile regression to estimate Value at Risk andexpected shortfall. Journal of Financial Econometrics,2008,6(3):382-406
[177] D. Huang, B. Yu, Z. Lu, et al. Index-Exciting CAViaR: A new empirical time-varying riskmodel. Studies in Nonlinear Dynamics and Econometrics,2010,14(2): Article1
[178] K. Yu, R. A. Moyeed. Bayesian quantile regression. Statistics and Probability Letters,2001,54(4):437-447
[179] E. G. Tsionas. Bayesian quantile inference. Journal of Statistical Computation and Simulation,2003,73(9):659-674
[180] C. Reed, K. Yu. A partially collapsed Gibbs sampler for Bayesian quantile regression. WorkingPaper, Brunel University,2009
[181]王新宇,宋学锋.间接TARCH-CAViaR模型及其MCMC参数估计与应用.系统工程理论与实践,2008,28(9):46-51
[182]王新宇,宋学锋.基于贝叶斯分位数回归的市场风险测度模型与应用.系统管理学报,2009,18(1):40-48
[183] R. Gerlach, C. Chen, N. Chan. Bayesian time-varying quantile forecasting for Value-at-Risk infinancial markets. Journal of Business and Economic Statistics,2010,29(4):481-492
[184] K. Yu, J. Zhang. A Three-Parameter Asymmetric Laplace distribution and its extension.Communications in Statistics-Theory and Methods,2005,34(9-10):1867-1879
[185] I. Ntzoufras. Bayesian modeling using WinBUGS. New York: John Wiley&Sons Inc,2009
[186] R. Kass, A. Raftery. Bayes factors. Journal of American Statistical Association,1995,90(430):773-795
[187] J. Berkowitz, P. Christoffersen, D. Pelletier. Evaluating Value-at-Risk models with desk-leveldata. Management Science,2009, Articles in Advance:1-15
[188] D. Hendricks. Evaluation of Value-at-Risk models using historical data. Economic PolicyReview,1996,2(1):39-69
[189] R. Koenker, G. Bassett. Regression quantiles. Econometrica,1978,46(1):33-50
[190] R. Koenker. Quantile regression. Cambridge: Cambridge University Press,2005
[191] R. Koenker, Z. Xiao. Quantile autoregression. Journal of American Statistical Association,2006,101(475):980-990
[192] W. M. Fong, K. H. See. A Markov switching model of the conditional volatility of crude oilfutures prices. Energy Economics,2002,24(1):71-95
[193] P. Sadorsky. Modeling and forecasting petroleum futures volatility. Energy Economics,2006,28(4):467-488
[194] C. W. Cheong. Modeling and forecasting crude oil markets using ARCH-type models. EnergyPolicy,2009,37(6):2346-2355
[195] S. H. Kang, S. M. Kang, S. M. Yoon. Forecasting volatility of crude oil markets. EnergyEconomics,2009,31(1):119-125
[196] P. Agnolucci. Volatility in crude oil futures: A comparison of the predictive ability of GARCHand implied volatility models. Energy Economics,2009,31(2):316-321
[197] Y. Wei, Y. Wang, D. Huang. Forecasting crude oil market volatility: Further evidence usingGARCH-class models. Energy Economics,2010,32(6):1477-1484
[198] J. Hill, T. Schneeweis. A note on the hedging effectiveness of foreign currency futures. Journalof Futures Markets,1981,1(4):659-664
[199] T. Grammatikos, A. Saunders. Stability and the hedging performance of foreign currencyfutures. Journal of Futures Markets,1983,3(3):295-305
[200] D. E. Bell, W. S. Krasker. Estimating hedge ratios. Financial Management,1986,15(2):34-39
[201] R. J. Myers, S. R. Thompson. Generalized optimal hedge ratio estimation. American Journalof Agricultural Economics,1989,71(4):858-868
[202] P. Sercu, X. Wu. Cross-and Delta-Hedges: Regression-versus price-based hedge ratios.Journal of Banking and Finance,2000,24(5):735-757
[203] J. Miffre. Conditional OLS minimum variance hedge ratios. Journal of Futures Markets,2004,24(10):945-964
[204] R. Rigobon. The curse of non-investment grade countries. Journal of Development Economics,2002,69(2):423-449