The oil price crash in 2014/15: Was there a (negative) financial bubble?

Fantazzini, Dean

doi:10.1016/j.enpol.2016.06.020

Cited by 95 publications

(52 citation statements)

References 65 publications

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“…Ambiguous results have been found regarding the influence of financial speculation: While Husain et al (2015) reject this factor specifically, Fantazzini (2016) find evidence for the presence of a negative financial bubble. The appreciation of the US-Dollar might have been another factor (Tokic, 2015); although some studies (Alquist et al, 2013;© 2017.…”

Section: Background 21 the Falling Price Puzzlementioning

confidence: 86%

OPEC, Saudi Arabia, and the shale revolution: Insights from equilibrium modelling and oil politics

Ansari

2017

Energy Policy

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Why did OPEC not cut oil production in the wake of 2014's price fall? This study aims at aiding the mostly qualitative discussion with quantitative evidence from computing quarterly partial market equilibria Q4 2011 -Q4 2015 under present short-term profit maximisation and different competition setups. Although the model performs reasonably well in explaining pre-2014 prices, all setups fail to capture low prices, which fall even beyond perfect competition outcomes. This result is robust with respect to large variations in cost parameters. Rejecting present short-term profit maximisation, as well as a qualitative discussion of Saudi Arabian politics and the shale oil revolution, lead to the conclusion that the price drop of 2014-16 was most plausibly the result of an attempt to defend market shares and to test for shale oil resilience, besides being fuelled by other factors such as rising competitiveness of alternative technologies. Although shale oil might have increased competition permanently (as supported by model results), the agreement of December 2016 should not be misunderstood as an OPEC defeat.

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Section: Background 21 the Falling Price Puzzlementioning

confidence: 86%

OPEC, Saudi Arabia, and the shale revolution: Insights from equilibrium modelling and oil politics

Ansari

2017

Energy Policy

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“…The speed of information processing in contemporary markets is systematically increasing and therefore in order to better capture the underlying dynamics the present analysis is performed for higher frequency intraday 5 min recordings in the period between January 02, 2012 and December 29 2017. This period is particularly interesting because of the positive bubble on the US dollar and the negative bubble on the oil market as reported by Tokic (2015), Fantazzini (2016), Fomin et al (2016) and Watorek et al (2016). In the present study the MFCCA proposed by Oświȩcimka et al (2014) and the q-dependent detrended cross-correlation coefficient ρ q proposed by Kwapień et al (2015) are employed to analyse the cross-correlations between WTI Crude Oil futures (CL) and thirteen most important financial instruments: E-mini S&P500 futures (ES) as an appropriate representation of the world stock market, gold futures (GC) and 11 currencies expressed in the US dollar.…”

Section: Introductionmentioning

confidence: 98%

Multifractal cross-correlations between the world oil and other financial markets in 2012–2017

et al. 2019

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Statistical and multiscaling characteristics of WTI Crude Oil futures prices expressed in US dollar in relation to the most traded currencies as well as to gold futures and to the E-mini S&P500 futures prices on 5 min intra-day recordings in the period January 2012 -December 2017 are studied. It is shown that in most of the cases the tails of return distributions of the considered financial instruments follow the inverse cubic power law. The only exception is the Russian ruble for which the distribution tail is heavier and scales with the exponent close to 2. From the perspective of multiscaling the analysed time series reveal the multifractal organization with the left-sided asymmetry of the corresponding singularity spectra. Even more, all the considered financial instruments appear to be multifractally crosscorrelated with oil, especially on the level of medium-size fluctuations, as the multifractal cross-correlation analysis carried out by means of the multifractal cross-correlation analysis (MFCCA) and detrended cross-correlation coefficient ρ q show. The degree of such cross-correlations is however varying among the financial instruments. The strongest ties to the oil characterize currencies of the oil extracting countries. Strength of this multifractal coupling appears to depend also on the oil market trend. In the analysed time period the level of cross-correlations systematically increases during the bear phase on the oil market and it saturates after the trend reversal in 1st half of * Corresponding author Email address: marcin.watorek@ifj.edu.pl (Marcin Watorek) 2016. The same methodology is also applied to identify possible causal relations between considered observables. Searching for some related asymmetry in the information flow mediating cross-correlations indicates that it was the oil price that led the Russian ruble over the time period here considered rather than vice versa.

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“…The crash detection capability of the PSY procedure has been noted in several recent empirical articles by the authors (PSY (2015a); PS (2018a); Shi (2017); Deng et al (2017)) and by many other researchers considering stock prices, exchange rates and other financial time series where abrupt crashes and sustained collapses have occurred. See, for example, Yiu and Jin (2013), Fantazzini (2016), and Hu et al (2017. This capability of PSY is further illustrated in the present paper with an application of the procedure to the S&P500 stock market over the period of January 2005 to March 2009.…”

Section: Introductionmentioning

confidence: 99%

Detecting Financial Collapse and Ballooning Sovereign Risk

Phillips

2017

SSRN Journal

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This paper proposes a new model for capturing discontinuities in the underlying financial environment that can lead to abrupt falls, but not necessarily sustained monotonic falls, in asset prices. This notion of price dynamics is consistent with existing understanding of market crashes, which allows for a mix of market responses that are not universally negative. The model may be interpreted as a martingale composed with a randomized drift process that is designed to capture various asymmetric drivers of market sentiment. In particular, the model is capable of generating realistic patterns of price meltdowns and bond yield inflations that constitute major market reversals while not necessarily being always monotonic in form. The recursive and moving window methods developed in Phillips, Shi and Yu (2015, PSY), which were designed to detect exuberance in financial and economic data, are shown to have detective capacity for such meltdowns and expansions. This characteristic of the PSY tests has been noted in earlier empirical studies by the present authors and other researchers but no analytic reasoning has yet been given to explain why methods intended to capture the expansionary phase of a bubble may also detect abrupt and broadly sustained collapses. The model and asymptotic theory developed in the present paper together explain this property of the PSY procedures. The methods are applied to analyze S&P500 stock prices and sovereign risk in European Union countries over 2001-2016 using government bond yields and credit default swap premia. A pseudo real-time empirical analysis of these data shows the effectiveness of the monitoring strategy in capturing key events and turning points in market risk assessment.

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The oil price crash in 2014/15: Was there a (negative) financial bubble?

Cited by 95 publications

References 65 publications

OPEC, Saudi Arabia, and the shale revolution: Insights from equilibrium modelling and oil politics

OPEC, Saudi Arabia, and the shale revolution: Insights from equilibrium modelling and oil politics

Multifractal cross-correlations between the world oil and other financial markets in 2012–2017

Detecting Financial Collapse and Ballooning Sovereign Risk

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