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A systematic review in crude oil markets: Embarking on the oil price

  • Crude oil plays an important role in economic activities, with both commodity attributes and financial characteristics. Through comprehensive review of the literature on crude oil prices, the following phenomena are presented. First, the forecasts and risk management of crude oil prices are still important topics when researchers conduct studies, however, the uncertainty of economic activity has aggravated the fluctuation of crude oil prices. Second, factors from supply side and demand side are main drivers of movements of crude oil prices, and investor sentiment gradually becomes an important factor affecting the expected level of crude oil prices. Third, economic activities and financial stability are influenced by shocks of crude oil prices, meanwhile, many studies confirm the asymmetric effects. However, due to changes in the external environment, more complex nonlinear time-varying features are exhibited. In addition, the advent of text mining technology and artificial intelligence technology provides new and effective methods for forecasting the trend of crude oil prices and conducting risk measurement in crude oil market.

    Citation: Yuhang Zheng, Ziqing Du. A systematic review in crude oil markets: Embarking on the oil price[J]. Green Finance, 2019, 1(3): 328-345. doi: 10.3934/GF.2019.3.328

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  • Crude oil plays an important role in economic activities, with both commodity attributes and financial characteristics. Through comprehensive review of the literature on crude oil prices, the following phenomena are presented. First, the forecasts and risk management of crude oil prices are still important topics when researchers conduct studies, however, the uncertainty of economic activity has aggravated the fluctuation of crude oil prices. Second, factors from supply side and demand side are main drivers of movements of crude oil prices, and investor sentiment gradually becomes an important factor affecting the expected level of crude oil prices. Third, economic activities and financial stability are influenced by shocks of crude oil prices, meanwhile, many studies confirm the asymmetric effects. However, due to changes in the external environment, more complex nonlinear time-varying features are exhibited. In addition, the advent of text mining technology and artificial intelligence technology provides new and effective methods for forecasting the trend of crude oil prices and conducting risk measurement in crude oil market.


    1. Introduction

    Nucleosomes contain histone octamers around which DNA is wrapped [1]. Neighboring nucleosomes are separated by unwrapped linker DNA. Generally, a nucleosome's position with respect to the gene promoter plays an important role in yeast gene expression [2,3,4,5]. Nucleosome arrangement is also specific to an organism [6].

    Trichostatin A (TSA) is a histone deacetylase inhibitor that promotes histone acetylation, which induces hyperacetylation of histones [7]. TSA influences nucleosome structure via histone acetylation. In addition, TSA influences nucleosome positions in the filamentous ascomycete Aspergillus fumigatus [8]. The acetylation and deacetylation of histones play an important role in the regulation of transcription [9]. Our previous study showed that TSA influences gene expression and nucleosome position in the archiascomycete Saitoella complicata [10]. Our study identified a total of 154 genes upregulated in a concentration-dependent manner in response to TSA treatment, whereas 131 genes were identified to be increasingly downregulated with increasing TSA concentration [10]. Most of nucleosome positions did not change after TSA treatment [10]. The anamorphic and saprobic budding yeast S. complicata, which is classified under Taphrinomycotina, represents the earliest ascomycetous lineage [11,12]. The fission yeast Schizosaccharomyces is also classified under Taphrinomycotina [12].

    In the previous study, we compared the nucleosome positions in 0 and 3 μg/mL TSA [10]. Thus, it was uncertain whether nucleosome position changed in a TSA concentration-dependent manner or not. If nucleosome position did not change in a TSA concentration-dependent manner, at which concentration did the position change? In this study, we investigated whether genes that are known to be regulated in response to TSA treatment also exhibit changes in nucleosome formation at the gene promoters in a TSA concentration-dependent manner.

    In addition, the ascomycetous yeast Saccharomyces cerevisiae spheroplast was reported to enlarge using zymolyase [13,14]. Enlarged spheroplast cells contain multiple nuclei [13]. It was uncertain how the multiple nuclei were maintained. Do nucleosome positions differ in between single nucleus and multiple nuclei? In bacterial enlarged spheroplasts, DNA was replicated and stress response genes were upregulated [15]. We found that S. complicata cells enlarge when grown in minimal SD broth (Takara, Japan) after zymolyase treatment. Thus, we measured the extent of nucleosome formation at the gene promoters in enlarged S. complicatacells and compared them with nucleosome formation levels in TSA-treated cells.


    2. Materials and Method


    2.1. Saitoella complicata culture

    Saitoella complicata NBRC 10748 (= JCM 7358, = IAM 12963; type strain) was cultivated in YM broth (yeast extract, 3 g/L; malt extract, 3 g/L; peptone, 5 g/L; dextrose, 10 g/L) at 25 ℃ for 24 h as a control sample. Afterwards, TSA (1, 2, and 3 μg/mL) was added to the S. complicata culture; cells were subsequently incubated at 25 ℃ for 24 h. For the enlarged spheroplast generation, S. complicata was grown in minimal SD broth (Takara, Japan) at 25 ℃ for 30 h. Harvested cells were centrifuged for 5 min at 3000 rpm and suspended in buffer containing 0.8 M sorbitol and 25 mM phosphate at 25 ℃ for 20 min. Zymolyase 20T (Seikagaku corporation, Japan) was added to the cell suspension; the cells were incubated at 37 ℃ for 60 min. S. complicata cells were harvested, centrifuged for 5 min at 3000 rpm, and cultured in minimal SD broth (adjusted to pH 7.5) at 25 ℃ for 4-7 days.


    2.2. Nucleosomal DNA fragment isolation

    Equal volumes of S. complicata culture and 2% formaldehyde were mixed and incubated for 10 min. Next, 5 mL of 1.25 M glycine was added to the resulting solution. S. complicata cells were collected, washed with 50 mM Tris-EDTA buffer (pH 8), and then suspended in zymolyase buffer (1 M sorbitol, 10 mM DTT, and 50 mM Tris-HCl, pH 8.0). Zymolyase (Seikagaku corporation, Japan) (50 U) was added to the cell suspension, and the resulting solution was incubated at 37 ℃ for 1 h. Cells were collected by centrifugation and suspended in 2.5 mL of zymolyase buffer, after which 1 U of MNase (Takara, Japan) was added. The resulting digestion solution was incubated at 37 ℃ for 30 min, and the reaction was stopped by adding sodium dodecyl sulfate to a final concentration of 1% and EDTA to a final concentration of 10 mM. Proteinase K solution (5 μL) was added to the solution, and the mixture was incubated at 56 ℃ for 1 h. DNA was phenol/chloroform-extracted, ethanol-precipitated, and treated with RNase (Nippon Gene, Japan). Nucleosomal DNA fragments were isolated via electrophoresis on 2% agarose gel. The mononucleosomal DNA band was excised and purified using the QIAquick Gel Extraction Kit (Qiagen, Germany).


    2.3. Quantitative PCR

    In this study, we selected six nucleosome positions in the gene promoters (300 nucleotides upstream of the translational start site) of the following four locus tags: G7K_2351-t1, G7K_2810-t1, G7K_3456-t1, and G7K_5676-t1. G7K_2351-t1 and G7K_2810-t1 encode homologs to 19S proteasome regulatory subunit Rpn3 and 20S proteasome-component α6 subunit Pre5, respectively, and are known to be increasingly downregulated upon treatment with increasing concentrations of TSA [10]. G7K_3456-t1 encodes a homolog to anaphase promoting complex subunit Apc11, whereas the G7K_5676-t1 gene is not homologous to any Schizosaccharomyces pombe protein. G7K_3456-t1 and G7K_5676-t1 are genes that are both upregulated in response to TSA treatment in a concentration-dependent manner [10]. Table 1 and Supplementary Figure 1 list the primers used in this study. We selected the position 5676_0 as an internal control, which showed the same nucleosome formation level between the cells treated with 0 μg/mL and 3 μg/mL TSA (Supplementary Figure 1) [10]. PCR was performed using the following cycling conditions: 1 cycle of 95 ℃ for 600 s and 45 cycles of denaturation (95 ℃ for 10 s), annealing (55 ℃ for 10 s), and extension (72 ℃ for 15 s). After the extension, a melting curve cycle was performed from 60 ℃ to 95 ℃ at 0.1 ℃/s to confirm the absence of non-specific bands. The quantification cycle (Cq) values were obtained using LightCycler Nano Software (Roche, Basel). We calculated the nucleosome formation level using the following formula: 2(Cq value at the position 5676_0 − Cq value at each position).

    Table 1. Primers used in this study.
    Target position Forward (5' to 3') Reverse (5' to 3') Product size (bp)
    2351 ggcaggcagtccaatagagt gagatcaagaggggttcacg 103
    2810_1 gcagtttaacgacgagaaggtt cgcctcggtaataggtattcat 110
    2810_2 ggacaagctcctggtcttcc cccttcaaagcacctcaatc 110
    3456 gagaagctaaccgagcaacttt tggccaattgaacaaacgat 109
    5676_1 tcagcgattccccaagttat gatgagggcgtcgagttc 110
    5676_2 gttcacgaggacagatcagg ggagttcgaaccatctttataacttg 109
    5676_0 (control) gagcgggatgtctttgtgat ctaggcagtcactgggatcg 99
     | Show Table
    DownLoad: CSV
    Figure 1. Phase contrast micrographs of Saitoella complicata. (A) Normal budding cells in minimal SD broth before zymolyase treatment. (B) Enlarged spheroplasts after 112 h of culture in minimal SD broth after zymolyase treatment. Phase contrast microscopy images were obtained using Olympus CKX41; bar = 50 μm.

    We performed analysis of variance (ANOVA) and a pairwise t test with Holm's adjustment using R statistical software (http://www.r-project.org/).


    3. Results and Discussion

    The typical diameter of a Saitoella complicata cell is approximately 5 μm, whereas that of an enlarged spheroplast cultured in minimal SD broth after zymolyase treatment was measured to be approximately 15 μm (Figure 1).

    ANOVA results showed that nucleosome formation levels were not significantly (p > 0.05) different at position 2351 but significantly different (p < 0.05) at the five other positions, namely, 2810_1, 2810_2, 3456, 5676_1, and 5676_2 (Figure 2).

    Figure 2. Comparison of nucleosome formation levels. The degree of nucleosome formation at position 5676_0 (control) is 1. We calculated the degree of nucleosome formation using the following formula: 2(Cq value at the position 5676_0-Cq value at each position). Star indicates p < 0.05 in a pairwise t test with Holm's adjustment.

    Among the five positions, analysis using pairwise ttest with Holm's adjustment showed no significant differences in terms of the degree of nucleosome formation at position 5676_1 (p > 0.05) between normal budding cells (0 μg/mL TSA) and enlarged cells (culture in minimal SD broth). However, significant differences (p < 0.05) in nucleosome formation levels were observed in the four other positions (2810_1, 2810_2, 3456, and 5676_2) (Figure 2). In addition, no significant differences in nucleosome formation were observed between enlarged cells and TSA-treated cells (2 and 3 μg/mL) at positions 2810_1, 2810_2, and 5676_2 (Figure 2). The above results strongly suggest that TSA-treatment and culture in minimal SD broth after zymolyase treatment exert similar effects on nucleosome formation at positions 2810_1, 2810_2, and 5676_2. Further research is necessary to confirm whether enlarged cells exhibit different histone acetylation patterns. Changes in nucleosome formation at the gene promoters can represent a stress response mechanism in cells subjected to spheroplast (zymolyase treatment) and TSA treatment. On the other hand, the degree of nucleosome formation at position 5676_1 was observed to be significantly different between enlarged cells and TSA-treated cells (Figure 2). However, nucleosome formation at this position was not significantly different between normal budding cells and enlarged cells. Thus, the observed nucleosome formation at position 5676_1 is specific to TSA-treated cells.

    Changes in the degree of nucleosome formation appeared to occur in a TSA concentration-dependent manner at positions 3456 (decreasing) and 5676_1 (increasing) (Figure 2). However, no significant differences in nucleosome formation levels were observed between cells treated with 1 and 2 μg/mL TSA and between cells treated with 2 and 3 μg/mL TSA (Figure 2).

    Nucleosome formation at position 5676_1 increased after TSA-treatment (Figure 2). On the other hand, nucleosome formation levels decreased after TSA-treatment at the neighboring position 5676_2 (Figure 2), which strongly suggests that a histone octamer can move from position 5676_2 to 5676_1. Based on the calculated nucleosome formation levels and p values, cells treated with 1 μg/mL TSA evidently showed nucleosome movement (Figure 2). Interestingly, changes in nucleosome position did not occur in enlarged cells, since nucleosome formation was observed only at position 5676_2 (Figure 2).

    In positions 2810_1 and 2810_2 (neighboring regions), nucleosome formation levels decreased as a result of TSA-treatment (Figure 2). This suggests that two histone octamers may be absent at these two positions. The observed nucleosome depletion at position 2810_2 is inconsistent with the results of the previous study (Supplementary Figure 1) and suggests that the nucleosome occupancy at this position is unstable.

    Except for position 2351, nucleosome formation levels in all other positions were significantly different between cells treated with 0 and 1 μg/mL TSA. However, no significant differences were observed between cells treated with 2 and 3 μg/mL TSA. The above results indicate that changes in the nucleosome formation occurred mainly in cells treated with 1 μg/mL TSA but not in cells treated with 2 and 3 μg/mL TSA.


    4. Conclusion

    We demonstrated that although TSA-treatment and zymolyase-treatment are completely different stimulus, TSA-treated cells and enlarged spheroplasts ofSaitoella complicata showed similar changes in nucleosome formation in five out of six gene promoter positions examined in the present study. These results strongly suggest that changes in nucleosome formation could serve as a stress response mechanism of S. complicata cells. Different stressors (TSA and zymolyase treatments) induce similar changes in the patterns of nucleosome formation in gene promoters in S. complicata.


    Acknowledgments

    This work was supported by JSPS KAKENHI grant no. 25440188 and a grant from The Cannon Foundation.


    Conflict of Interest

    The authors declare that there is no conflict of interest regarding the publication of this paper.




    [1] Aastveit KA (2014) Oil price shocks in a data-rich environment. Energy Econ 45: 268-279. doi: 10.1016/j.eneco.2014.07.006
    [2] Aastveit KA, Bjørnland HC, Thorsrud LA (2015). What drives oil prices? emerging versus developed economies. J Appl Econometrics 30.
    [3] Agnolucci P (2009) Volatility of crude oil futures: a comparison of forecasts from garch and implied volatility models. Energy Econ 31: 316-321. doi: 10.1016/j.eneco.2008.11.001
    [4] Ahmad AH, Hernandez RM (2013) Asymmetric adjustment between oil prices and exchange rates: empirical evidence from major oil producers and consumers. J Int Financ Mark Inst Money 27: 306-317. doi: 10.1016/j.intfin.2013.10.002
    [5] Alizadeh AH, Nomikos NK, Pouliasis PK (2008) A markov regime switching approach for hedging energy commodities. J Bank Financ 32: 1970-1983. doi: 10.1016/j.jbankfin.2007.12.020
    [6] Aloui C, Jammazi R (2015) Dependence and risk assessment for oil prices and exchange rate portfolios: a wavelet based approach. Phys A 436: 62-86. doi: 10.1016/j.physa.2015.05.036
    [7] Aloui R, Aïssa MSB, Nguyen DK, et al. (2013) Conditional dependence structure between oil prices and exchange rates: a copula-garch approach. J Int Money Financ 32: 719-738. doi: 10.1016/j.jimonfin.2012.06.006
    [8] Alquist R, Kilian L, Vigfusson R (2011) Forecasting the price of oil. Ssrn Electron J 2: 427-507.
    [9] Antonakakis N, Chatziantoniou I, Filis G (2014) Dynamic spillovers of oil price shocks and economic policy uncertainty. Energy Econ 44: 433-447. doi: 10.1016/j.eneco.2014.05.007
    [10] Asafu-Adjaye J (2000) The relationship between energy consumption, energy prices and economic growth: time series evidence from Asian developing countries. Energy Econ 22: 615-625. doi: 10.1016/S0140-9883(00)00050-5
    [11] Atems B, Kapper D, Lam E (2015) Do exchange rates respond asymmetrically to shocks in the crude oil market? Energy Econ 49: 227-238. doi: 10.1016/j.eneco.2015.01.027
    [12] Baker S, Bloom N, Davis S (2013) Measuring economic policy uncertainty. Chicago Booth Research Paper, 13-02.
    [13] Balcilar M, Bekiros S, Gupta R (2017) The role of news-based uncertainty indices in predicting oil markets: a hybrid nonparametric quantile causality method. Empir Econ 53: 879-889. doi: 10.1007/s00181-016-1150-0
    [14] Balcilar M, Gungor H, Hammoudeh S (2015) The time-varying causality between spot and futures crude oil prices: a regime switching approach. Int Rev Econ Financ 40: 51-71. doi: 10.1016/j.iref.2015.02.008
    [15] Bampinas G, Panagiotidis T (2015) On the relationship between oil and gold before and after financial crisis: linear, nonlinear and time-varying causality testing. Stud Nonlinear Dyn Econometrics 19: 657-668.
    [16] Bank M, Larch M, Peter G (2011) Google search volume and its influence on liquidity and returns of German stocks. Financ Mark Portf Manage 25: 239-264. doi: 10.1007/s11408-011-0165-y
    [17] Barsky RB, Kilian L (2004) Oil and the macroeconomy since the 1970s. J Econ Perspect 18: 115-134. doi: 10.1257/0895330042632708
    [18] Basher SA, Sadorsky P (2006) Oil price risk and emerging stock markets. Global Financ J 17: 224-251. doi: 10.1016/j.gfj.2006.04.001
    [19] Basher SA, Haug AA, Sadorsky P (2010) Oil prices, exchange rates and emerging stock markets. Energy Econ 34: 227-240. doi: 10.1016/j.eneco.2011.10.005
    [20] Baumeister C, Kilian L (2014) Real-time analysis of oil price risks using forecast scenarios. Imf Econ Rev 62: 119-145. doi: 10.1057/imfer.2014.1
    [21] Baumeister C, Kilian L (2017) Lower oil prices and the u.s. economy: is this time different? Social Sci Electron Publishing 2016: 287-357.
    [22] Baumeister C, Peersman G (2013) Time-varying effects of oil supply shocks on the us economy. Am Econ J Macroecon 5: 1-28. doi: 10.1257/mac.5.4.1
    [23] Baumeister C, Kilian L, Lee TK (2014). Are there gains from pooling real-time oil price forecasts? Energy Econ 46: S33-S43. doi: 10.1016/j.eneco.2014.08.008
    [24] Baumeister C, Kilian L, Lee TK (2017) Inside the crystal ball: new approaches to predicting the gasoline price at the pump. Social Sci Electron Publishing.
    [25] Bekiros SD, Diks CGH (2008) The relationship between crude oil spot and futures prices: cointegration, linear and nonlinear causality. Energy Econ 30: 2673-2685. doi: 10.1016/j.eneco.2008.03.006
    [26] Bekiros S, Gupta R, Paccagnini A (2015) Oil price forecastability and economic uncertainty. Econ Lett 132: 125-128. doi: 10.1016/j.econlet.2015.04.023
    [27] Bentzen J (2007) Does opec influence crude oil prices? testing for co-movements and causality between regional crude oil prices. Appl Econ 39: 1375-1385.
    [28] Blair BF, Rezek JP (2008) The effects of hurricane katrina on price pass-through for gulf coast gasoline. Econ Lett 98: 229-234. doi: 10.1016/j.econlet.2007.02.028
    [29] Bloch H, Rafiq S, Salim R (2015) Economic growth with coal, oil and renewable energy consumption in china: prospects for fuel substitution. Econ Model 44: 104-115. doi: 10.1016/j.econmod.2014.09.017
    [30] Bodenstein M, Guerrieri L, Kilian L (2012) Monetary policy responses to oil price fluctuations. Imf Econ Rev 60: 470-504. doi: 10.1057/imfer.2012.19
    [31] Brahmasrene T, Huang JC, Sissoko Y (2014) Crude oil prices and exchange rates: causality, variance decomposition and impulse response. Energy Econ 44: 407-412. doi: 10.1016/j.eneco.2014.05.011
    [32] Breitenfellner A, Cuaresma JC, Mayer P (2014) Energy inflation and house price corrections. Energy Econ 48: 109-116. doi: 10.1016/j.eneco.2014.08.023
    [33] Brémond V, Hache E, Mignon V (2011) Does opec still exist as a cartel? an empirical investigation. Energy Econ 34: 125-131.
    [34] Cabedo JD, Moya I (2003) Estimating oil price 'value at risk' using the historical simulation approach. Energy Econ 25: 239-253. doi: 10.1016/S0140-9883(02)00111-1
    [35] Caner M, Hansen BE (2001) Threshold autoregression with a unit root. Econometrica 69: 1555-1596. doi: 10.1111/1468-0262.00257
    [36] Cheong CW (2009) Modeling and forecasting crude oil markets using arch-type models. Energy Policy 37: 2346-2355. doi: 10.1016/j.enpol.2009.02.026
    [37] Colgan JD (2014) Oil, domestic politics, and international conflict. Energy Res Social Sci 1: 198-205. doi: 10.1016/j.erss.2014.03.005
    [38] Costello A, Asem E, Gardner E (2008) Comparison of historically simulated var: evidence from oil prices. Energy Econ 30: 2154-2166. doi: 10.1016/j.eneco.2008.01.011
    [39] Da Z, Engelberg J, Gao P (2011) In search of attention.J Financ 66: 1461-1499. doi: 10.1111/j.1540-6261.2011.01679.x
    [40] Dahl C, Yücel M (1991) Testing alternative hypotheses of oil producer behavior. Energy J 12: 117-138. doi: 10.5547/ISSN0195-6574-EJ-Vol12-No4-8
    [41] Dai YH, Xie WJ, Jiang ZQ, et al. (2016) Correlation structure and principal components in the global crude oil market. Empir Econ 51: 1501-1519. doi: 10.1007/s00181-015-1057-1
    [42] Demirer R, Kutan AM (2010) The behavior of crude oil spot and futures prices around opec and spr announcements: an event study perspective. Energy Econ 32: 1467-1476. doi: 10.1016/j.eneco.2010.06.006
    [43] Ding L, Vo M (2012) Exchange rates and oil prices: A multivariate stochastic volatility analysis. Q Rev Econ Financ 52: 15-37. doi: 10.1016/j.qref.2012.01.003
    [44] Dong H, Liu Y, Chang J (2019) The heterogeneous linkage of economic policy uncertainty and oil return risks. Green Financ 1: 46-66. doi: 10.3934/GF.2019.1.46
    [45] Draper DW (1984) The behavior of event-related returns on oil futures contracts. J Futures Mark 4: 125-132. doi: 10.1002/fut.3990040203
    [46] Driesprong G, Jacobsen B, Maat B (2003) Striking oil: another puzzle? Erim Rep 89: 307-327.
    [47] Dvir E, Rogoff K (2014) Demand effects and speculation in oil markets: theory and evidence. J Int Money Finan 42: 113-128. doi: 10.1016/j.jimonfin.2013.08.007
    [48] Edelstein P, Kilian L (2008) The response of business fixed investment to changes in energy prices: a test of some hypotheses about the transmission of energy price shocks. J Macroecon 7.
    [49] Fan Y, Xu JH (2011) What has driven oil prices since 2000? a structural change perspective. Energy Econ 33: 1082-1094.
    [50] Fan Y, Zhang YJ, Tsai HT, et al. (2008) Estimating 'value at risk' of crude oil price and its spillover effect using the ged-garch approach. Energy Econ 30: 3156-3171. doi: 10.1016/j.eneco.2008.04.002
    [51] Fattouh B (2007) OPEC pricing power: the need for a new perspective, Oxford Institute for Energy Studies,WPM 31.
    [52] Fattouh B (2010) The dynamics of crude oil price differentials. Energy Econ 2: 334-342. doi: 10.1016/j.eneco.2009.06.007
    [53] Fattouh B, Mahadeva L (2013) OPEC: what difference has it made? Annu Rev Resour Econ 5: 427-443. doi: 10.1146/annurev-resource-091912-151901
    [54] Fesharaki F, Hoffman SL (1985) OPEC and the structural changes in the oil market: the outlook after the counter-revolution. Energy 10: 505-516. doi: 10.1016/0360-5442(85)90065-9
    [55] Filis G (2010) Macro economy, stock market and oil prices: do meaningful relationships exist among their cyclical fluctuations? Energy Econ 32: 877-886. doi: 10.1016/j.eneco.2010.03.010
    [56] Gisser M, Goodwin TH (1986) Crude oil and the macroeconomy: tests of some popular notions: a note. J Money Credit Bank 18: 95. doi: 10.2307/1992323
    [57] Gjerde O, Sættem F (1999) Causal relations among stock returns and macroeconomic variables in a small, open economy. J Int Financ Mark Inst Money 9: 61-74. doi: 10.1016/S1042-4431(98)00036-5
    [58] Golub SS (1983) Oil prices and exchange rates. Econ J 93: 576-593. doi: 10.2307/2232396
    [59] Gülen SG (1996) Is opec a cartel? evidence from cointegration and causality tests. Energy J 17: 43-57.
    [60] Gülen SG (1997) Regionalization in the world crude oil market. Energy J 18: 109-126.
    [61] Gülen SG (1998) Efficiency in the crude oil futures market. J Energy Financ Dev 3: 0-21.
    [62] Guo JF, Ji Q (2013) How does market concern derived from the internet affect oil prices? Appl Energy 112: 1536-1543. doi: 10.1016/j.apenergy.2013.03.027
    [63] Hamilton JD (1983) Oil and the macroeconomy since world war ii. J Political Econ 91: 228-248. doi: 10.1086/261140
    [64] Hamilton JD (2008) Oil and the macroeconomy. New Palgrave Dictionary Econ Edition Palgrave Macmillan 18: 115-134.
    [65] Hamilton JD (2012) Oil prices, exhaustible resources, and economic growth. Nber Working Papers. doi: 10.1596/1813-9450-6117
    [66] Hamilton JD (2009) Causes and consequences of the oil shock of 2007-08. Brookings Pap Econ Activity, 215-259. doi: 10.1353/eca.0.0047
    [67] Hamilton JD (2011) Nonlinearities and the macroeconomic effects of oil prices. Macroecon Dyn 15: 364-378. doi: 10.1017/S1365100511000307
    [68] Hammoudeh SM, Ewing BT, Thompson MA (2008) Threshold cointegration analysis of crude oil benchmarks. Energy J 29: 79-95. doi: 10.5547/ISSN0195-6574-EJ-Vol29-No4-4
    [69] Hayat A, Narayan PK (2010) The oil stock fluctuations in the united states. Appl Energy 87: 178-184. doi: 10.1016/j.apenergy.2009.07.010
    [70] Henriques I, Sadorsky P (2008) Oil prices and the stock prices of alternative energy companies. Energy Econ 30: 998-1010. doi: 10.1016/j.eneco.2007.11.001
    [71] Hicks B, Kilian L (2013) Did unexpectedly strong economic growth cause the oil price shock of 2003-2008?. J Forecasting 32: 385-394. doi: 10.1002/for.2243
    [72] Hooker MA (1996) What happened to the oil price-macroeconomy relationship? J Monetary Econ 38: 215-220. doi: 10.1016/S0304-3932(96)01282-2
    [73] Horan SM, Mahar PJ (2004) Implied volatility of oil futures options surrounding opec meetings. Energy J 25: 103-125. doi: 10.5547/ISSN0195-6574-EJ-Vol25-No3-6
    [74] Hosseini SH, Hamed SG (2016) A study on the future of unconventional oil development under different oil price scenarios: a system dynamics approach. Energy Policy 91: 64-74. doi: 10.1016/j.enpol.2015.12.027
    [75] Huang CJ, Wang HC, Chen MG, et al. (2009) The impact of international oil prices on consumer prices: evidence from a var model. Comput Sci Inf Eng Wri World Congr 04: 531-534.
    [76] Huang D, Yu B, Fabozzi FJ, et al. (2009) Caviar-based forecast for oil price risk. Energy Econ 31: 511-518. doi: 10.1016/j.eneco.2008.12.006
    [77] Jammazi R, Lahiani A, Nguyen DK (2015) A wavelet-based nonlinear ardl model for assessing the exchange rate pass-through to crude oil prices. J Int Financ Mark Inst Money 34: 173-187. doi: 10.1016/j.intfin.2014.11.011
    [78] Ji Q, Guo JF (2015) Oil price volatility and oil-related events: an internet concern study perspective. Appl Energy 137: 256-264. doi: 10.1016/j.apenergy.2014.10.002
    [79] Jia X, An H, Wei F, et al. (2015) How do correlations of crude oil prices co-move? a grey correlation-based wavelet perspective. Energy Econ 49, 588-598.
    [80] Jiménez-Rodríguez R (2009) Oil price shocks and real GDP growth: testing for non-linearity. Energy J 30: 1-23.
    [81] Jiménez-Rodríguez R, Sánchez M (2005) Oil price shocks and real GDP growth: empirical evidence for some OECD countries. Appl Econ 37: 201-228. doi: 10.1080/0003684042000281561
    [82] Jones CM, Kaul G (1996) Oil and the stock markets. J Financ 51: 463-491. doi: 10.1111/j.1540-6261.1996.tb02691.x
    [83] Kang SH, Kang SM, Yoon SM (2009) Forecasting volatility of crude oil markets. Energy Econ 31: 119-125. doi: 10.1016/j.eneco.2008.09.006
    [84] Kang W, Ratti RA (2013) Oil shocks, policy uncertainty and stock market return. J Int Financ Mark Inst Money 26: 305-318. doi: 10.1016/j.intfin.2013.07.001
    [85] Kang W, Ratti RA (2013) Structural oil price shocks and policy uncertainty. Econ Model 35: 314-319. doi: 10.1016/j.econmod.2013.07.025
    [86] Kanjilal K, Ghosh S (2017) Dynamics of crude oil and gold price post 2008 global financial crisis - new evidence from threshold vector error-correction model. Resour Policy 52: 358-365. doi: 10.1016/j.resourpol.2017.04.001
    [87] Kaufmann RK, Dees S, Karadeloglou P, et al. (2004) Does opec matter? an econometric analysis of oil prices. Energy J 5: 67-90.
    [88] Kilian L, Vigfusson RJ (2011) Nonlinearities in the oil price-output relationship. Macroecon Dyn 5: 337-363. doi: 10.1017/S1365100511000186
    [89] Kilian L (2009) Not all oil price shocks are alike: disentangling demand and supply shocks in the crude oil market. Am Econ Rev 99: 1053-1069. doi: 10.1257/aer.99.3.1053
    [90] Kilian L, Lee TK (2014) Quantifying the speculative component in the real price of oil: the role of global oil inventories. J Int Money Financ 42: 71-87. doi: 10.1016/j.jimonfin.2013.08.005
    [91] Kilian L, Murphy DP (2012) Why agnostic sign restrictions are not enough: understanding the dynamics of oil market var models. J Eur Econ Assoc 10: 1166-1188. doi: 10.1111/j.1542-4774.2012.01080.x
    [92] Kilian L, Murphy DP (2014) The role of inventories and speculative trading in the global market for crude oil. J Appl Econometrics 29: 454-478. doi: 10.1002/jae.2322
    [93] Kisswani KM (2016) Does opec act as a cartel? empirical investigation of coordination behavior. Energy Policy 97: 171-180.
    [94] Kling JL (1985). Oil price shocks and stock market behavior. J Portf Manage 12: 34-39. doi: 10.3905/jpm.1985.409034
    [95] Krehbiel T, Adkins LC (2005) Price risk in the nymex energy complex: an extreme value approach. J Futures Mark 25: 309-337. doi: 10.1002/fut.20150
    [96] Krugman P (1980) Vehicle currencies and the structure of international exchange. Nber Working Papers, 12: 513-526.
    [97] Lammerding M, Stephan P, Trede M, et al. (2013) Speculative bubbles in recent oil price dynamics: evidence from a bayesian markov-switching state-space approach. Energy Econ 36: 491-502. doi: 10.1016/j.eneco.2012.10.006
    [98] Li J, Lu X, ZhouY (2016) Cross-correlations between crude oil and exchange markets for selected oil rich economies. Phys A 453: 131-143. doi: 10.1016/j.physa.2016.02.039
    [99] Li L, Ma J, Wang SY, et al. (2015) How does google search affect trader positions and crude oil prices? Econ Model 49: 162-171. doi: 10.1016/j.econmod.2015.04.005
    [100] Li SF, Zhang H, Yuan D (2019) Investor attention and crude oil prices: Evidence from nonlinear Granger causality tests. Energy Econ: [ In Press].
    [101] Liao G, Li Z, Du Z (2019) The Heterogeneous Interconnections between Supply or Demand Side and Oil Risks. Energies 12: 2226. doi: 10.3390/en12112226
    [102] Liu Y, Dong H, Failler P (2019) The oil market reactions to OPEC's announcements. Energies 12: 3238. doi: 10.3390/en12173238
    [103] Lin SX, Tamvakis M (2010) OPEC announcements and their effects on crude oil prices. Energ Policy 38: 1010-1016. doi: 10.1016/j.enpol.2009.10.053
    [104] Lippi F, Nobili A (2012) Oil and the macroeconomy: a quantitative structural analysis. J Eur Econ Assoc 10: 1059-1083. doi: 10.1111/j.1542-4774.2012.01079.x
    [105] Lizardo RA, Mollick AV (2010) Oil price fluctuations and U.S. dollar exchange rates. Energy Econ 32: 399-408.
    [106] Lombardi MJ, Van Robays I (2011) Do financial investors destabilize the oil price? SSRN Electron J.
    [107] Loutia A, Mellios C, Andriosopoulos K (2016) Do OPEC announcements influence oil prices?. Energy Policy 90: 262-272. doi: 10.1016/j.enpol.2015.11.025
    [108] Lux T, Segnon M, Gupta R (2016) Forecasting crude oil price volatility and value-at-risk: evidence from historical and recent data. Energy Econ 56: 117-133. doi: 10.1016/j.eneco.2016.03.008
    [109] Marimoutou V, Raggad B, Trabelsi A (2009) Extreme value theory and value at risk: application to oil market. Energy Econ 31: 519-530. doi: 10.1016/j.eneco.2009.02.005
    [110] Melvin M, Sultan J (2010) South African political unrest, oil prices, and the time varying risk premium in the gold futures market. J Futures Mark 10: 103-111. doi: 10.1002/fut.3990100202
    [111] Mensi W, Hammoudeh S, Yoon SM (2014) Structural breaks and long memory in modeling and forecasting volatility of foreign exchange markets of oil exporters: the importance of scheduled and unscheduled news announcements. Int Rev Econ Financ 30: 101-119. doi: 10.1016/j.iref.2013.10.004
    [112] Mohammadi H, Su L (2010) International evidence on crude oil price dynamics: applications of arima-garch models. Energy Econ 32: 1001-1008. doi: 10.1016/j.eneco.2010.04.009
    [113] Mollick AV, Assefa TA (2013) U.S. stock returns and oil prices: the tale from daily data and the 2008-2009 financial crisis. Energy Econ 36: 1-18.
    [114] Moosa IA, Al-Loughani NE (1994) Unbiasedness and time varying risk premia in the crude oil futures market. Energy Econ 16: 99-105. doi: 10.1016/0140-9883(94)90003-5
    [115] Morana C (2001) A semiparametric approach to short-term oil price forecasting. Energy Econ 23: 325-338. doi: 10.1016/S0140-9883(00)00075-X
    [116] Narayan PK, Narayan S, Prasad A (2008) Understanding the oil price-exchange rate nexus for the Fiji islands. Energy Econ 30: 2686-2696. doi: 10.1016/j.eneco.2008.03.003
    [117] Nomikos NK, Pouliasis PK (2011). Forecasting petroleum futures markets volatility: the role of regimes and market conditions. Energy Econ 33: 321-337. doi: 10.1016/j.eneco.2010.11.013
    [118] Papapetrou E (2001) Bivariate and multivariate tests of the inflation-productivity granger-temporal causal relationship: evidence from Greece. J Econ Stud 28: 213-226. doi: 10.1108/EUM0000000005470
    [119] Park J, Ratti RA (2008) Oil price shocks and stock markets in the US and 13 European countries. Energy Econ 30: 2587-2608. doi: 10.1016/j.eneco.2008.04.003
    [120] Peersman G, Robays IV (2012) Cross-country differences in the effects of oil shocks. Energy Econ 34: 1532-1547. doi: 10.1016/j.eneco.2011.11.010
    [121] Qadan M, Nama H (2018) Investor sentiment and the price of oil. Energy Econ 69: S0140988317303766. doi: 10.1016/j.eneco.2017.10.035
    [122] Rafiq S, Salim R (2011) The linkage between energy consumption and income in six emerging economies of Asia. Int J Emerging Mark 6: 50-73. doi: 10.1108/17468801111104377
    [123] Rahman S, Serletis A (2012) Oil price uncertainty and the Canadian economy: evidence from a varma, garch-in-mean, asymmetric BEKK model. Energy Econ 34: 603-610. doi: 10.1016/j.eneco.2011.08.014
    [124] Rao T, Srivastava S (2013) Modeling movements in oil, gold, forex and market indices using search volume index and Twitter sentiments. Acm Web Sci Conf.
    [125] Reboredo JC (2011) How do crude oil prices co-move?: a copula approach. Energy Econ 33: 948-955. doi: 10.1016/j.eneco.2011.04.006
    [126] Sadorsky P (1999) Oil price shocks and stock market activity. Energy Econ 21: 449-469. doi: 10.1016/S0140-9883(99)00020-1
    [127] Saiz A, Simonsohn U (2013) Proxying for unobservable variables with internet document-frequency. J Eur Econ Assoc 11: 137-165. doi: 10.1111/j.1542-4774.2012.01110.x
    [128] Sauer DG (1994) Measuring economic markets for imported crude oil. Energy J 15: 107-123. doi: 10.5547/ISSN0195-6574-EJ-Vol15-No2-6
    [129] Schmidbauer H, Rösch A (2012) Opec news announcements: effects on oil price expectation and volatility. Energy Econ 34: 1656-1663. doi: 10.1016/j.eneco.2012.01.006
    [130] Schwarz TV, Szakmary AC (2010) Price discovery in petroleum markets: arbitrage, cointegration, and the time interval of analysis. J Futures Mark 14: 147-167. doi: 10.1002/fut.3990140204
    [131] Seyyedi S (2017) Analysis of the interactive linkages between gold prices, oil prices, and exchange rate in india. Global Econ Rev 46: 65-79. doi: 10.1080/1226508X.2017.1278712
    [132] Shao YH, Yang YH, Shao HL, et al. (2019) Time-varying lead-lag structure between the crude oil spot and futures markets. Phys A 523: 723-733. doi: 10.1016/j.physa.2019.03.002
    [133] Shrestha K (2014) Price discovery in energy markets. Energy Econ 45: 229-233. doi: 10.1016/j.eneco.2014.06.007
    [134] Silvapulle P, Moosa IA (2015) The relationship between spot and futures prices: evidence from the crude oil market. J Futures Mark 19: 175-193. doi: 10.1002/(SICI)1096-9934(199904)19:2<175::AID-FUT3>3.0.CO;2-H
    [135] Silvapulle P, Smyth R, Zhang X, et al. (2017) Nonparametric panel data model for crude oil and stock market prices in net oil importing countries. Energy Econ 67: 255-267. doi: 10.1016/j.eneco.2017.08.017
    [136] Singh VK, Nishant S, Kumar P (2018) Dynamic and directional network connectedness of crude oil and currencies: evidence from implied volatility. Energy Econ 76: 48-63. doi: 10.1016/j.eneco.2018.09.018
    [137] Singleton KJ (2014) Investor flows and the 2008 boom/bust in oil prices. Manage Sci 60: 300-318. doi: 10.1287/mnsc.2013.1756
    [138] Smith DJ (2005) New release: oil, blood and money: culture and power in Nigeria. Anthropological Q 8: 725-740. doi: 10.1353/anq.2005.0042
    [139] Tang K, Xiong W (2012) Index investment and the financialization of commodities. Social Sci Electron Publishing 68: 54-74.
    [140] Tushar R, Saket S (2013) Modeling movements in oil, gold, forex and market indices using search volume index and twitter sentiments. WebSci'13 Proceedings of the 5th Annual ACM Web Science Conference, New York, USA, 336-345.
    [141] Vlastakis N, Markellos RN (2012) Information demand and stock market volatility. Social Sci Electron Publishing 36: 1808-1821.
    [142] Wang H, Huang JZ, Qu Y, et al. (2004) Web services: problems and future directions. J Web Semantics 1: 309-320. doi: 10.1016/j.websem.2004.02.001
    [143] Wang J, Wang J (2016) Forecasting energy market indices with recurrent neural networks: case study of crude oil price fluctuations. Energy 102: 365-374. doi: 10.1016/j.energy.2016.02.098
    [144] Wang SP, Hu AM, Wu ZX (2012) The impact of oil price volatility on china's economy: an empirical investigation based on var model. Adv Mater Res 524: 3211-3215. doi: 10.4028/www.scientific.net/AMR.524-527.3211
    [145] Wang Y, Wu C, Li Y (2016) Forecasting crude oil market volatility: a markov switching multifractal volatility approach. Int J Forecasting 32: 1-9. doi: 10.1016/j.ijforecast.2015.02.006
    [146] Wei Y, Wang Y, Huang D (2010) Forecasting crude oil market volatility: Further evidence using GARCH-class models. Energy Econ 32: 1477-1484. doi: 10.1016/j.eneco.2010.07.009
    [147] Weiner RJ (1991) Is the world oil market "one great pool"?. Energy J 12: 95-107. doi: 10.5547/ISSN0195-6574-EJ-Vol12-No3-7
    [148] Wirl F, Kujundzic A (2004) The impact of OPECconference outcomes on world oil prices 1984-2001. Energy J 25: 45-62. doi: 10.5547/ISSN0195-6574-EJ-Vol25-No1-3
    [149] Wu CC, Chung H, Chang YH (2012) The economic value of co-movement between oil price and exchange rate using copula-based Garch models. Energy Econ 34: 270-282. doi: 10.1016/j.eneco.2011.07.007
    [150] Xie MQ, Jiang H, Huang YL, et al. (2006) New Class Recognition Based on Support Vector Data Description. Int Conf Machine Learning Cybernetics.
    [151] Yin L (2016) Does oil price respond to macroeconomic uncertainty? new evidence. Empir Econ 51: 921-938. doi: 10.1007/s00181-015-1027-7
    [152] Youssef M, Belkacem L, Mokni K (2015) Value-at-risk estimation of energy commodities: a long-memory garch-evt approach. Energy Econ 51: 99-110. doi: 10.1016/j.eneco.2015.06.010
    [153] Zhang X, Yu L, Wang S, et al. (2009) Estimating the impact of extreme events on crude oil price: an emd-based event analysis method. Energy Econ 31: 768-778. doi: 10.1016/j.eneco.2009.04.003
    [154] Zhang YJ, Wang J (2015) Exploring the WTI crude oil price bubble process using the markov regime switching model. Phys A 421: 377-387. doi: 10.1016/j.physa.2014.11.051
    [155] Zhang YJ, Wei YM (2010) The crude oil market and the gold market: evidence for cointegration, causality and price discovery. Resour Policy 35: 168-177. doi: 10.1016/j.resourpol.2010.05.003
    [156] Zhang YJ, Yao T (2016) Interpreting the movement of oil prices: driven by fundamentals or bubbles? Econ Model 55: 226-240. doi: 10.1016/j.econmod.2016.02.016
    [157] Zhang YJ, Fan Y, Tsai HT, et al. (2008) Spillover effect of us dollar exchange rate on oil prices. J Policy Model 30: 973-991. doi: 10.1016/j.jpolmod.2008.02.002
    [158] Zhao X, Xi Z (2009) Estimation of Value-at-Risk for Energy Commodities via CAViaR Model. Commun Comput Inf Sci 35: 429-437.
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