The relationship between European Brent crude oil price development and US macroeconomy
Abnormal volatility has a damaging effect on the macroeconomy and is seen as a measure of risk in asset and commodity markets. This investigation had the aim to analyze the supposed transatlantic volatility inducing effect of the most prominent scheduled macroeconomic news announcements from the United States (US) on Brent Blend crude oil price intraday volatility over a period of seven years from 2012 to 2018. The objective was to generate a ranking list of scheduled US macroeconomic news that forecast high intraday volatility episodes at precise points in time. A total of 38 US news was analyzed using a data mining workflow. Data modeling was conducted using a simple ordinary least squares regression model and performed with programming language Python. A one hour window of rolling standard deviation based on one minute high-frequency closing prices were applied. As a result, 20 scheduled US macroeconomic news was successfully identified to significantly impact Brent crude oil price volatility. The model strongly supports the forecast of high price fluctuations and provides an opportunity for market players to adjust their risk management strategies right in time.
Auer, B., Rottman, H. (2015). Einführung in die Ökonometrie. In: Statistik und Ökonometrie für Wirtschaftswissenschaftler: Eine anwendungsorientierte Einführung (3rd ed., pp. 417-615 , Springer Gabler. Retrieved from https://doi.org/10.1007/978-3-658-06439-6.
Andersen, T.G., Bollerslev, T., Diebold, F.X., & Ebens, H. (2001). The Distribution of Realized Stock Return Volatility. Journal of Financial Econometrics, 61(1), 43-76. Retrieved from https://doi.org/10.1016/S0304-405X(01)00055-1
Bakas, D., & Triantafyllou, A. (2018). The impact of uncertainty shocks on the volatility of commodity prices. Journal of International Money and Finance, 87, 96–111. Retrieved from https://doi.org/10.1016/j.jimonfin.2018.06.001
Bakas, D., & Triantafyllou, A. (2019). Volatility forecasting in commodity markets using macro uncertainty. Energy Economics, 81, 79–94. Retrieved from https://doi.org/10.1016/j.eneco.2019.03.016
Baumohl, B. (2013). The Secrets of Economic Indicators (3rd ed.). Upper Saddle River, New Jersey 07458, Pearson Education Inc.
Belgacem, A., Creti, A., Guesmi, K., & Lahiani, A. (2014). Volatility spillovers and macroeconomic announcements: Evidence from crude oil markets. Taylor&Francis Online. Retrieved from https://doi.org/10.1080/00036846.2015.1011316
Bernanke, B.S. (1980). Irreversibility, uncertainty, and cyclical investment. Quarterly Journal of Economics, 98(1), 85–106. Retrieved from https://doi.org/10.3386/w0502
Bredin, D., Elder, J., & Fountas, S. (2010). The effects of uncertainty about oil prices in g-7. Geary Institute, University College Dublin, Working Papers 200840, no. 2010-01. Retrieved from Https://ideas.repec.org/p/ucd/wpaper/200840.html
Chen, X., Ye Y., Williams, G, & Xu, X. (2007). A survey of open source data mining systems. In: Washio T. et al. (eds) Emerging Technologies in Knowledge Discovery and Data Mining. PAKDD 2007. Lecture Notes in Computer Science, vol 4019. Springer, Berlin, Heidelberg. Retrieved from https://doi.org/10.1007/978-3-540-77018-3_2
Cooper, J.C.B. (2003). Price elasticity of demand for crude oil: estimates for 23 countries. OPEC Review, 27(1), 1–8. Retrieved from https://doi.org/10.1111/1468-0076.00121
Diakopoulous, N. (2019). Interactive: The top programming languages 2018. IEEE Spectrum. Retrieved from https://spectrum.ieee.org/static/interactive-the-top-programming-languages-2019
EIA. (2015). Ranking oil consumption up to 2014. EIA, US Energy Information Administration. Retrieved from https:www.eia.gov/beta/international/
Ebrahim, Z., Inderwildi, O.R., & King, D.A. (2014). Macroeconomic impacts of oil price volatility: mitigation and resilience. Frontiers in Energy, 8, 9-24. Retrieved from https://doUpper Saddle River, New Jersey 07458. https://doi.org/10.1007/s11708-014-0300-3
Elder, J., & Serletis, A. (2010). Oil price uncertainty. Journal of Money, Credit, and Banking, 42(6), 1137–1159. Retrieved from https://doi.org/10.1111/j.1538-4616.2010.00323.x
Elder, J. (2018). Oil price volatility: industrial production and special aggregates. Macroeconomic Dynamics, 22(3), 640–653. Retrieved from https://doi.org/10.1017/S136510051600047X
Faseli, O., & Zamani, M. (2016). Application of Open Web API: The Impact of Crude Oil Stocks Change Announcements on Crude Oil Price Volatility, International Journal of Engineering Development and Research, (4)1, 63-69. Retrieved from https://www.researchgate.net/publication/336832303
Faseli, O. (2019). Ranking of US Macroeconomic News Impacting WTI Crude Oil Volatility Risk. Int. J. of Research in Business and Social Science, 8(6) (2019), pp. 49-57. Retrieved from https://doi.org/10.20525/ijrbs.v8i6.515
Faseli, O. (2020). What drives crude oil market movements? Dissertation under review. TU Wien, Vienna University of Technology, Austria.
Fernandez-Perez, A., Frijns, B., & Tourani-Rad A. (2017). When no news is good news - the decrease in investor fear after the fomc announcement. Journal of Empirical Finance, 41, 187–199. https://doi.org/10.1016/j.jempfin.2016.07.013
Geyer, A. (2019, December 4). Basic Financial Econometrics. Vienna University of Economics and Business. Retrieved from https://www.wu.ac.at/fileadmin/wu/d/i/ifr/Basic_Financial_Econometrics. pdf.
Guo, H., & Kliesen, K.L. (2005). Oil price volatility and u.s. macroeconomic activity. Federal Reserve Bank of St. Louis Review, 87(6), 669–683. Retrieved from https://doi.org/10.20955/r87.699-84
Han, J., Kamber, M, & Pei, J. (2012). Why Data Mining? In: Data Mining - Concepts and Techniques, 3rd ed., Elsevier Inc., pp. 1-34. Doi:10.1016/B978-0-12-381479-1.00001-0
Henriques, I., & Sadorsky, P. (2011). The effect of oil price volatility on strategic investment. Energy Economics, 33(1), 79–87. Retrieved from https://doi.org./10.1016/j.eneco.2010.09.001
Horan, S.M., Peterson, J.H., & Mahar, J. (2004). Implied volatilitye of oil futures options surrounding opec meetings. The Energy Journal, 25(3), 103–125. https://www.jstor.org/stable/41323044
Hui, B. (2014). Effect of inventory announcements on crude oil price volatility. Energy Economics, Elsevier. 46, 485–494. Doi:10.1016/j.eneco.2014.05.015.
Jo, S. (2012). The effects of oil price uncertainty on the macroeconomy. Bank of Canada. Staff working paper 2012-40.Retrieved from http://www.bankofcanada.ca/wp-content/uploads/2012/12/wp2012-40.pdf
JODI, (2012). Report on jodi related activities. Joint Organisation Data Initiative. Retrieved from http://www.jodidata.org/resources/papers.aspx
Kilian, L. (2009). Not all oil price shocks are alike: disentangling demand and supply shocks in the crude oil market. The American Economic Review, 99(3), 1053–1069. Retrieved from https://www.jstor.org/stable/25592494
Kilian, L. (2010). Oil price volatility: Origin and effects. WTO Staff Working Papers, ERSD-2010-02. Economic Research and Statistics Division, Geneva. Retrieved from https://doi.org/10.30875/4445c521-en
Kilian, L., & Vega, C. (2011). Do energy prices respond to u.s. macroeconomic news? A Test of the Hypothesis of Predetermined Energy Prices. The Review of Economics and Statistics, 93(2), 660–671. Retrieved from https://www.jstor.org/stable/23015961
Klimisch, H.J., Andreae, M., & Tillman, U. (1997). Robust summary of information on crude oil, in a systematic approach for evaluating the quality of experimental toxicological and ecotoxicological data. Regulatory Toxicology and Pharmacology, 25(1-5), 2–3. Retrieved from https://doi.org/10.1006/rtph.1996.1076
Libo Wu, Jing Li, & ZhongXiang Zhang. (2011). Inflationary effect of oil price shocks in an imperfect marktet: a partial transmission input-output analysis. Economic Series, East-West Center Working Papers, no. 115, 1–12. Retrieved from https://doi.org/10.1016/j.jpolmod.2012.01.008
Lipsky, J. (2009). Economic shifts and oil price volatility. A 4th OPEC International Seminar, Vienna. http://www.imf.org/en/News/Articles/2015/09/28/24/04/53/sp03180
Liu, J., & Kemp, A. (2019). Forecasting the sign of u.s. oil and gas industry stock index excess returns employing macroeconomic variables. Energy Economics, 81, 672–686. Retrieved from https://doi.org/10.1016/j.eneco.2019.04.023
Lopez, R. (2018). The behaviour of energy-related volatility indices around scheduled news announcements: Implications for variance swap investments. Energy Economics, 72, 356–364. Retrieved from https://doi.org/10.1016/j.eneco.2018.04.040
Mork, K.A. (1989). Oil and the macroeconomy when prices go up and down: An extension of hamilton’s results. J. of Political Economy, 97(3), 740–744.
Newey, W.K., & West, K.D. (1987). A simple, positive semi-definite heteroskedasticity and autocorrelation consistent covariance matrix. Econometrica, 55, 703-708.
Nguyen, D.K., & Walther, T. (2018). Modeling and forecasting commodity market volatility with longterm economic and financial variables. University of St. Gallen, School of Finance Research Paper No. 2018/24. DOI:10.1002/for2617
Park, J., & Ratti, R.A. (2008). Oil price shocks and stock markets in the U.S. and 13 european countries. Energy Economics, 30(5), 2587–260. Retrieved from https://doi.org/10.1016/j.eneco.2008.04.003
Piatetsky Shapiro, G. (2018). Top data science and machine learning methods used in 2018. Retrieved from https://www.kdnuggets.com/2018/05/poll-tools-analytics-data-science-machine-learning-results.html
Pindyck, R.S. (1991). Irreversibility, uncertainty, and investment. National Bureau of Economic Research, Working Paper no. 3307. Journal of Economic Literature, 29, 1110-1148. DOI:10.3386/w3307
Plante, M., & Traum, N. (2012). Time varying oil price volatility and macroeconomic aggregates. Center for Applied Economics and Policy Research, Working papers, no. 2012-002. Retrieved from http://dx.doi.oig/10.2139/ssrn.2005312
Sadorsky, P. (1999). Oil price shocks and market activity. Energy Economics, 25(5), 449–469. https://doi.org/10.1016/S0140-9883(99)00020-1
Schmidbauer, H., & Rösch, A. (2012). OPEC news announcements: Effects on oil price expectation and volatility. Energy Economics, 34, 1656–1663. Retrieved from https://doi.org/10.1016/j.eneco.2012.01.006
Su, Z., Lu, M., & Yin, L. (2018). Oil prices and news-based uncertainty: novel evidence. Energy Econ., 72, 331–340. Retrieved from https://doi.org/10.1016/j.eneco.2018.04.021
Triki, T., & Affes, Y. (2011). Managing commodity price volatility in africa. Africa Economic Brief., 2(12), 1-7.
Xiao, L., & Aydemir, A. (2007). Volatility modeling and forecasting in finance. In Forecasting Volatility in the Financial Markets, 3rd ed., Elsevier, pp. 1-45. Retrieved from https://doi.org/10.1016/B978-075066942-9.50003-0
Zhang, Xun., Lai, K.K., & Wang, Shou-Yang. (2017). A new approach for crude oil price analysis based on empirical mode decomposition. Energy Economics, 30(3), 905–918. Retrieved from https://doi.org/10.1016/j.eneco.2007.02.012
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