IDEAS home Printed from https://ideas.repec.org/a/rbs/ijbrss/v9y2020i7p123-129.html
   My bibliography  Save this article

Screening for light crude oil and market comovements

Author

Listed:
  • Omid Faseli

    (Institute of Information Systems Engineering Vienna University of Technology)

Abstract

This study aimed to perform a screening for economic interrelationships among market participants from the stock market, global stock indices, and commodities from fossil energy, agricultural, and the metals sector. Particular focus was put on the comovements of the light crude oil benchmarks West Texas Intermediate (WTI) and Brent crude oil. In finance research and the crude oil markets, identifying novel groupings and interactions is a fundamental requirement due to the extended impact of crude oil price fluctuations on economic growth and inflation. Thus, it is of high interest for investors to identify market players and interactions that appear sensitive to crude oil price volatility triggers. The price development of 14 stocks, 25 leading global indices, and 13 commodity prices, including WTI and Brent, were analyzed via data mining applying the hierarchical correlation cluster mapping technique. All price data comprised the period from January 2012 – December 2018 and were based on daily returns. The technique identifies and visualizes existing hierarchical clusters and correlation patterns emphasizing comovements that indicate positively correlated processes. The method successfully identified clustering patterns and a series of relevant and partly unexpected novel comovements in all investigated economic sectors. Although additional research is required to reveal the causative factors, the study offers an insight into in-depth market interrelationships. Key Words: Data mining, pattern recognition, correlation cluster-map, clustering, light crude oil, WTI, Brent

Suggested Citation

  • Omid Faseli, 2020. "Screening for light crude oil and market comovements," International Journal of Research in Business and Social Science (2147-4478), Center for the Strategic Studies in Business and Finance, vol. 9(7), pages 123-129, December.
  • Handle: RePEc:rbs:ijbrss:v:9:y:2020:i:7:p:123-129
    DOI: 10.20525/ijrbs.v9i7.949
    as

    Download full text from publisher

    File URL: http://ssbfnet.com/ojs/index.php/ijrbs/article/view/949/725
    Download Restriction: no

    File URL: https://doi.org/10.20525/ijrbs.v9i7.949
    Download Restriction: no

    File URL: https://libkey.io/10.20525/ijrbs.v9i7.949?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Bakas, Dimitrios & Triantafyllou, Athanasios, 2019. "Volatility forecasting in commodity markets using macro uncertainty," Energy Economics, Elsevier, vol. 81(C), pages 79-94.
    2. Bakas, Dimitrios & Triantafyllou, Athanasios, 2018. "The impact of uncertainty shocks on the volatility of commodity prices," Journal of International Money and Finance, Elsevier, vol. 87(C), pages 96-111.
    3. Zhang, Xun & Lai, K.K. & Wang, Shou-Yang, 2008. "A new approach for crude oil price analysis based on Empirical Mode Decomposition," Energy Economics, Elsevier, vol. 30(3), pages 905-918, May.
    4. Su, Zhi & Lu, Man & Yin, Libo, 2018. "Oil prices and news-based uncertainty: Novel evidence," Energy Economics, Elsevier, vol. 72(C), pages 331-340.
    5. Fionn Murtagh & Pierre Legendre, 2014. "Ward’s Hierarchical Agglomerative Clustering Method: Which Algorithms Implement Ward’s Criterion?," Journal of Classification, Springer;The Classification Society, vol. 31(3), pages 274-295, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Omid Faseli, 2020. "The relationship between European Brent crude oil price development and the US macroeconomy," International Journal of Research in Business and Social Science (2147-4478), Center for the Strategic Studies in Business and Finance, vol. 9(1), pages 80-87, January.
    2. Song, Lu & Tian, Gengyu & Jiang, Yonghong, 2022. "Connectedness of commodity, exchange rate and categorical economic policy uncertainties — Evidence from China," The North American Journal of Economics and Finance, Elsevier, vol. 60(C).
    3. Scarcioffolo, Alexandre R. & Etienne, Xiaoli L., 2021. "Regime-switching energy price volatility: The role of economic policy uncertainty," International Review of Economics & Finance, Elsevier, vol. 76(C), pages 336-356.
    4. Lyu, Yongjian & Wei, Yu & Hu, Yingyi & Yang, Mo, 2021. "Good volatility, bad volatility and economic uncertainty: Evidence from the crude oil futures market," Energy, Elsevier, vol. 222(C).
    5. Dutta, Anupam & Bouri, Elie & Saeed, Tareq, 2021. "News-based equity market uncertainty and crude oil volatility," Energy, Elsevier, vol. 222(C).
    6. Chen, Louisa & Verousis, Thanos & Wang, Kai & Zhou, Zhiping, 2023. "Financial stress and commodity price volatility," Energy Economics, Elsevier, vol. 125(C).
    7. Syeda Beena Zaidi & Abidullah Khan & Shabeer Khan & Mohd Ziaur Rehman & Wadi B. Alonazi & Abul Ala Noman, 2023. "Connectedness between Pakistan’s Stock Markets with Global Factors: An Application of Quantile VAR Network Model," Mathematics, MDPI, vol. 11(19), pages 1-17, October.
    8. Hedi Ben Haddad & Imed Mezghani & Abdessalem Gouider, 2021. "The Dynamic Spillover Effects of Macroeconomic and Financial Uncertainty on Commodity Markets Uncertainties," Economies, MDPI, vol. 9(2), pages 1-22, June.
    9. Ding, Qian & Huang, Jianbai & Gao, Wang & Zhang, Hongwei, 2022. "Does political risk matter for gold market fluctuations? A structural VAR analysis," Research in International Business and Finance, Elsevier, vol. 60(C).
    10. Tunc, Ahmet & Kocoglu, Mustafa & Aslan, Alper, 2022. "Time-varying characteristics of the simultaneous interactions between economic uncertainty, international oil prices and GDP: A novel approach for Germany," Resources Policy, Elsevier, vol. 77(C).
    11. Huang, Jianbai & Li, Yingli & Zhang, Hongwei & Chen, Jinyu, 2021. "The effects of uncertainty measures on commodity prices from a time-varying perspective," International Review of Economics & Finance, Elsevier, vol. 71(C), pages 100-114.
    12. Qadan, Mahmoud & Idilbi, Yasmeen, 2022. "Presidential honeymoons, political cycles and the commodity market," Resources Policy, Elsevier, vol. 77(C).
    13. Lyu, Yongjian & Tuo, Siwei & Wei, Yu & Yang, Mo, 2021. "Time-varying effects of global economic policy uncertainty shocks on crude oil price volatility:New evidence," Resources Policy, Elsevier, vol. 70(C).
    14. Xiao, Jihong & Wang, Yudong, 2022. "Macroeconomic uncertainty, speculation, and energy futures returns: Evidence from a quantile regression," Energy, Elsevier, vol. 241(C).
    15. Maurizio Vichi & Carlo Cavicchia & Patrick J. F. Groenen, 2022. "Hierarchical Means Clustering," Journal of Classification, Springer;The Classification Society, vol. 39(3), pages 553-577, November.
    16. Bakas, Dimitrios & Triantafyllou, Athanasios, 2020. "Commodity price volatility and the economic uncertainty of pandemics," Economics Letters, Elsevier, vol. 193(C).
    17. Tao XIONG & Chongguang LI & Yukun BAO, 2017. "An improved EEMD-based hybrid approach for the short-term forecasting of hog price in China," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 63(3), pages 136-148.
    18. Kaijian He & Rui Zha & Jun Wu & Kin Keung Lai, 2016. "Multivariate EMD-Based Modeling and Forecasting of Crude Oil Price," Sustainability, MDPI, vol. 8(4), pages 1-11, April.
    19. Grossmann, Axel & Kim, Jintae, 2022. "The impact of U.S. dollar movements and U.S. dollar states on non-perishable commodity prices," Research in International Business and Finance, Elsevier, vol. 61(C).
    20. Jiao Jieying & Hu Guanyu & Yan Jun, 2021. "A Bayesian marked spatial point processes model for basketball shot chart," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 17(2), pages 77-90, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:rbs:ijbrss:v:9:y:2020:i:7:p:123-129. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Umit Hacioglu (email available below). General contact details of provider: https://edirc.repec.org/data/ssbffea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.