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The Oil Price Crash in 2014/15: Was There a (Negative) Financial Bubble?

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  • Fantazzini, Dean

Abstract

This paper suggests that there was a negative bubble in oil prices in 2014/15, which decreased them beyond the level justified by economic fundamentals. This proposition is corroborated by two sets of bubble detection strategies: the first set consists of tests for financial bubbles, while the second set consists of the log-periodic power law (LPPL) model for negative financial bubbles. Despite the methodological differences between these detection methods, they provided the same outcome: the oil price experienced a statistically significant negative financial bubble in the last months of 2014 and at the beginning of 2015. These results also hold after several robustness checks which consider the effect of conditional heteroskedasticity, model set-ups with additional restrictions, longer data samples, tests with lower frequency data and with an alternative proxy variable to measure the fundamental value of oil.

Suggested Citation

  • Fantazzini, Dean, 2016. "The Oil Price Crash in 2014/15: Was There a (Negative) Financial Bubble?," MPRA Paper 72094, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:72094
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    9. Yonghong Jiang & Gengyu Tian & Bin Mo, 2020. "Spillover and quantile linkage between oil price shocks and stock returns: new evidence from G7 countries," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 6(1), pages 1-26, December.
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    11. Chen, Shyh-Wei & Wu, An-Chi, 2018. "Is there a bubble component in government debt? New international evidence," International Review of Economics & Finance, Elsevier, vol. 58(C), pages 467-486.
    12. Gharib, Cheima & Mefteh-Wali, Salma & Jabeur, Sami Ben, 2021. "The bubble contagion effect of COVID-19 outbreak: Evidence from crude oil and gold markets," Finance Research Letters, Elsevier, vol. 38(C).
    13. Ma, Richie Ruchuan & Xiong, Tao, 2021. "Price explosiveness in nonferrous metal futures markets," Economic Modelling, Elsevier, vol. 94(C), pages 75-90.
    14. Gomis-Porqueras, Pedro & Shi, Shuping & Tan, David, 2020. "Gold as a Financial Instrument," MPRA Paper 102782, University Library of Munich, Germany.
    15. Ma, Yan-Ran & Zhang, Dayong & Ji, Qiang & Pan, Jiaofeng, 2019. "Spillovers between oil and stock returns in the US energy sector: Does idiosyncratic information matter?," Energy Economics, Elsevier, vol. 81(C), pages 536-544.
    16. Khan, Muhammad Imran & Yasmeen, Tabassam & Shakoor, Abdul & Khan, Niaz Bahadur & Muhammad, Riaz, 2017. "2014 oil plunge: Causes and impacts on renewable energy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P1), pages 609-622.
    17. Cheng, Fangzheng & Fan, Tijun & Fan, Dandan & Li, Shanling, 2018. "The prediction of oil price turning points with log-periodic power law and multi-population genetic algorithm," Energy Economics, Elsevier, vol. 72(C), pages 341-355.
    18. Henseler, Martin & Maisonnave, Helene, 2018. "Low world oil prices: A chance to reform fuel subsidies and promote public transport? A case study for South Africa," Transportation Research Part A: Policy and Practice, Elsevier, vol. 108(C), pages 45-62.
    19. Berk, Istemi & Çam, Eren, 2020. "The shift in global crude oil market structure: A model-based analysis of the period 2013–2017," Energy Policy, Elsevier, vol. 142(C).
    20. Zhou, Wei & Huang, Yang & Chen, Jin, 2018. "The bubble and anti-bubble risk resistance analysis on the metal futures in China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 947-957.
    21. Pham, Linh, 2019. "Do all clean energy stocks respond homogeneously to oil price?," Energy Economics, Elsevier, vol. 81(C), pages 355-379.
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    More about this item

    Keywords

    Oil; WTI; Brent; Generalized sup ADF test; LPPL; Bubble;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • O13 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Agriculture; Natural Resources; Environment; Other Primary Products
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

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