Ranking of US macroeconomic news impacting WTI crude oil volatility risk
This study had the purpose to investigate the impact of 38 scheduled major United States (US) macroeconomic news on WTI crude oil intraday volatility for the period 2012-2018. It was the aim to provide a news ranking that indicates upcoming high volatility episodes at a specific point in time. The West Texas Intermediate (WTI) light crude oil represents a benchmark since it has a signal effect on market players. High crude oil price volatility is a measure of risk and known to increase inflation, to affect producers, consumers, and investors and to destabilize economic growth. In this research approach one-minute high-frequency bid close prices provided the basis for a 1h window rolling standard deviation. Data modeling was performed using simple and multiple robust ordinary least squares (OLS) regression performed with programming language Python. The model successfully identified 21 significant news announcements in both, the simple and multiple regression models, however, simple OLS-regression appears to be more sensitive. It also provided a ranking of US news impacting WTI volatility risk. The results support the prediction of approaching high price volatility and thus, display an opportunity for market participants and decision-makers to minimize risk.
Auer, B., & Rottman, H. (2015). In: Statistik und Ökonometrie für Wirtschaftswissenschaftler: Eine anwendungsorientierte Einführung, Lehrbuch, 3. überarbeitete und aktualisierte Auflage, Springer Gabler. Doi: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, 43-76.
Bakas, D., & Triantafyllou, A. (2018). The impact of uncertainty shocks on the volatility of commodity prices. J. Int. Money Financ., 87, 96–111.
Bakas, D., & Triantafyllou, A. (2019). Volatility forecasting in commodity markets using macro uncertainty. Energy Econ., 81, 79–94.
Baumohl, B. (2013). In: The Secrets of Economic Indicators, Pearson Education Inc., pp 6-12.
Belgacem, A., Creti, Anna, Guesmi, K., & Lahiani, A. (2014). Volatility spillovers and macroeconomic announcements: Evidence from crude oil markets. IPAG Business School, Working Paper Series, no. 2014-050, 1–8.
Bernanke, B.S. (1983). Irreversibility, uncertainty, and cyclical investment. Quarterly Journal of Economics, 98(1), 85–106.
Bredin, D., Elder, J., & Fountas, S. (2010). The effects of uncertainty about oil prices in g-7. Geary Institute, University College Dublin, Working Papers, no. 2010-01.
Chen, X., Ye Y., Williams, G, & Xu, X. (2007). A survey of open source data mining systems. In: Emerging Technologies in Knowledge Discovery and Data Mining. PAKDD, International Workshops Nanjing, May 22-25, China, pp 3-14.
Cooper, J.C.B. (2003). Price elasticity of demand for crude oil: estimates for 23 countries. OPEC Review, 27(1), 1–8.
Diakopoulous, N. (2019). Interactive: The top programming languages 2018. IEEE Spectrum. https://spectrum.ieee.org
EIA. (2015). Ranking oil consumption up to 2014. EIA, Energy Information Agency. https:www.eia.gov/beta/international/
Ebrahim, Z., Inderwildi, O.R., & King, D.A. (2014). Macroeconomic impacts of oil price volatility: mitigation and resilience. Front. Energy, Higher Education Press, Springer Verlag Berlin Heidelberg, 10, 1-16. Doi:10.1007/s11708-014-0300-3.
Elder, J., & Serletis, A. (2010). Oil price uncertainty. Journal of Money, Credit and Banking,42(6),1137–1159. https://doi.org/10.1111/j.1538-4616.2010.00323.x
Elder, J. (2018). Oil price volatility: industrial production and special aggregates. Macroecon. Dyn., 22(3), 640–653.
Faseli, O., & Zamani, M. (2016). Application of Open Web API: The Impact of Crude Oil Stocks Change Announcements on Crude Oil Price Volatility, IJEDR, (4)1. ISSN:2321-9939.
Faseli, O. (2019). What drives crude oil market movements? (Working Title). 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.
Geyer, A. (2008). In: Basic Financial Econometrics, preliminary and incomplete version.
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.
Han, J., Kamber, M, & Pei, J. (2012). Why Data Mining? In: Data Mining - Concepts and Techniques, 3rd ed, Elsevier Inc., 1, 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.
Horan, S.M., Peterson, J.H., & Mahar, J. (2004). Implied volatility of oil futures options surrounding opec meetings. Energy J., 25(3), 103–125.
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. http://www.bankofcanada.ca/wp-content/ uploads/2012/12/wp2012-40.pdf
JODI, (2012). Report on jodi related activities. Joint Organisation Data Initiative.
Kilian, L. (2009). Not all oil price shocks are alike: disentangling demand and supply shocks in the crude oil market. American Economic Review, 99(3), 1053–69.
Kilian, L. (2010). Oil price volatility: Origin and effects. World Trade Organization, Economic Research and Statistics Division, Geneva. Paper ERSD-2010-02. http://www.wto.org
Kilian, L., & Vega, C. (2011). Do energy prices respond to u.s. macroeconomic news? Review of Economics and Statistics, 93(2), 660–671.
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.
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.
Lipsky, J. (2009). Economic shifts and oil price volatility. A 4th OPEC International Seminar, Vienna.
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.
Lopez, R. (2018). The behaviour of energy-related volatility indices around scheduled news announcements: Implications for variance swap investments. Energy Economics, 72, 356–364.
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.
Namboodiri, P K S. (1983). Politics and economics of oil prices. Strategic Analysis, 7(4), 339-342.
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. MPRA, University Library of Munich, Paper no. 84464.
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.
Piatetsky Shapiro, G. (2018). Top data science and machine learning methods used in 2018
Pindyck, R.S. (1991). Irreversibility, uncertainty, and investment. National Bureau of EconomiResearch, Working Paper no. 3307.
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.
Sadorsky, P. (1999). Oil price shocks and market activity. Energy Economics, 25(5), 449–469.
Schmidbauer, H., & Rösch, A. (2012). OPEC news announcements: Effects on oil price expectaion and volatility. Energy Economics, 34, 1656–1663.
Su, Z., Lu, M., & Yin, L. (2018). Oil prices and news-based uncertainty: novel evidence. Energy Econ., 72, 331–340. 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.
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. Doi:10.1016/j.eneco.2007.02.012
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