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The relationship between European Brent crude oil price development and US macroeconomy

Omid Faseli

International Journal of Research in Business and Social Science · 2020 · Vol. 9 / Issue 1 · 80-87

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Overview

Abstract

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.

Keywords: {'name': 'Data Science'}, {'name': 'Event Study'}, {'name': 'Intraday Crude Oil Volatility'}, {'name': 'Macroeconomic News Announcements'}, {'name': 'Brent Crude Oil'}

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Omid Faseli
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DOI: 10.1007/978-3-658-06439-6

DOI: 10.1016/S0304-405X(01)00055-1

DOI: 10.1016/j.jimonfin.2018.06.001

DOI: 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.

DOI: 10.1080/00036846.2015.1011316

DOI: 10.2307/1885568

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

DOI: 10.1007/978-3-540-77018-3_2

DOI: 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/

DOI: 10.1007/s11708-014-0303-0

DOI: 10.1111/j.1538-4616.2010.00323.x

DOI: 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

DOI: 10.20525/ijrbs.v8i6.515

DOI: 10.20525/ijrbs.v8i6.515

Faseli, O. (2020). What drives crude oil market movements? Dissertation under review. TU Wien, Vienna University of Technology, Austria.

DOI: 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.

DOI: 10.20955/r.87.669-84

DOI: 10.1016/B978-0-12-381479-1.00001-0

DOI: 10.1016/j.eneco.2010.09.001

DOI: 10.5547/ISSN0195-6574-EJ-Vol25-No3-6

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

DOI: 10.1257/aer.99.3.1053

DOI: 10.30875/4445c521-en

DOI: 10.1162/REST_a_00086

DOI: 10.1006/rtph.1996.1076

DOI: 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

DOI: 10.1016/j.eneco.2019.04.023

DOI: 10.1016/j.eneco.2018.04.040

DOI: 10.1086/261625

DOI: 10.2307/1913610

DOI: 10.2139/ssrn.3294967

DOI: 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

DOI: 10.3386/w3307

DOI: 10.2139/ssrn.2005312

DOI: 10.1016/S0140-9883(99)00020-1

DOI: 10.1016/j.eneco.2012.01.006

DOI: 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.

DOI: 10.1016/B978-075066942-9.50003-0

DOI: 10.1016/j.eneco.2007.02.012