The Brent and WTI prices of crude oil fell by 60% between June 2014 and January 2015, marking one of the fastest and largest declines in oil history. Several potential factors (related to oil supply and demand) which could have influenced this oil price decline were discussed in an extensive World Bank policy research note by Baffes, Kose, Ohnsorge, and Stocker (2015). However, Tokic (2015) and a Bank of International Settlements report (Domanski, Kearns, Lombardi, and Shin, 2015) showed that production and consumption alone are not sufficient for a fully satisfactory explanation of the collapse in oil prices. Particularly, Domanski, Kearns, Lombardi, and Shin (2015) suggested the idea that “if financial constraints keep production levels high and result in increased hedging of future production, the addition to oil sales would magnify price declines. In the extreme, a downward-sloping supply response of increased current and future sales of oil could amplify the initial decline in the oil price and force further deleveraging”.

Given this background, I proposed a potential explanation for the part of the oil price decline which could not be explained using supply and demand alone, suggesting that there was a negative financial bubble which decreased oil prices beyond the level justified by economic fundamentals. A negative financial bubble is a situation where the increasing pessimism fuelled by short positions lead investors to run away from the market, which spirals downwards in a self-fulfilling process, see Yan, Woodard, and Sornette (2012) for more details.

I employed two sets of bubble detection strategies to corroborate this proposition.[1] Despite the differences between these bubble detection methods, they provided the same result: the oil price experienced a statistically significant negative financial bubble in the last months of 2014 and at the beginning of 2015. Moreover, a set of robustness checks confirmed these results also with different tests, model set-ups and alternative datasets, thus giving additional support to the idea put forward by Domanski, Kearns, Lombardi, and Shin (2015) and Tokic (2015) that the oil price collapse cannot be explained only by supply and demand.

The previous results have two policy implications: first, the enhanced regulations imposed after the 2008 oil bubble (see Collins (2010) and Cosgrove (2009)) were not able to ensure the oil price efficiency. Tokic (2015) and Domanski, Kearns, Lombardi, and Shin (2015) highlighted that the oil price collapse 2014/2015 was probably caused by the increased leverage of oil firms (the debt of oil and gas sector increased from $1 trillion in 2006 to $2.5 trillion in 2014): the need to keep high production levels and to hedge future production to satisfy financial constraints managed to amplify the initial price decline due to economic fundamentals. An important lesson is that a more effective regulatory framework should include not only oil traders/speculators, but also oil producers.

A second implication is that market regulators should be concerned not only about positive price bubbles, but also about negative bubbles. This is particularly important considering the boom and bust cycles in oil prices and production, which is a well-known characteristic of the oil market, see Maugeri (2010) for details. In this regard, it is important to point out that extremely low prices are not necessarily beneficial, even for countries which are (mainly) oil consumers:  Kilian (2008) showed that the large fall in investment in the oil and gas industry following the oil price crash in 1985/1986 was one of the main reasons why real consumption in the US did not grow as expected. More specifically, there is a large literature which examined if and why economic activity responds asymmetrically to oil price shocks, i.e. high oil prices decrease economic activity much more than low oil prices stimulate it. I refer the interested reader to the Macroeconomic Dynamics special issue on “Oil Price Shocks” published in 2011 for more details. Besides, several authors investigated the linkages between the oil market and other markets, focusing particularly on the volatility transmission across financial markets: Diebold and Yilmaz (2012) found that cross-market volatility spillovers across US stock, bond, foreign exchange and commodities markets were quite limited until 2007, but have increased since then; particularly, they found that the commodity market was a net recipient of small levels of volatility shocks from the other markets till 2007, but it has become a net transmitter after the beginning of the global financial crisis. Similar evidence was found by Ji and Fan (2012) who found that the crude oil market has significant volatility spillover effects on non-energy commodity markets and they have strengthened after the crisis. A similar result was also reported by Creti, Joets, and Mignon (2013) who showed increased links between stock and commodity markets, and by Gomes and Chaibi (2014) who highlighted that shock and volatility spillovers tend to go more often from oil to stock markets than vice-versa, see my paper Fantazzini (2016) and references therein for more details.

Given this increased influence of the oil market on the other markets, regulators should consider a regulatory framework able to mitigate an oil price crash due to panic selling and/or market manipulation: a potential starting point could be the model developed by Dutt and Harris (2005), which can be used to set position limits for cash-settled derivative contracts.

REFERENCES

Baffes, J., M. Kose, F. Ohnsorge, and M. Stocker (2015): The great plunge in oil prices: Causes, consequences, and policy responses. World Bank Group, Development Economics.

Collins, D. (2010): “CFTC steps up on limits. Futures: news, analysis & strategies for futures,” Option Derivatives Traders, 39(2), 14.

Creti, A., M. Jo¨ets, and V. Mignon (2013): “On the links between stock and commodity markets’ volatility,” Energy Economics, 37, 16–28.

Cosgrove, M. (2009): “Banishing energy speculators is not the solution,” FOW: Global Derivatives Management, 454, 39.

Diebold, F. X., and K. Yilmaz (2012): “Better to give than to receive: Predictive directional measurement of volatility spillovers,” International Journal of Forecasting, 28(1), 57–66.

Domanski, D., J. Kearns, M. J. Lombardi, and H. S. Shin (2015): “Oil and Debt,” BIS Quarterly Review, March, 55–65.

Fantazzini (2016): The oil price crash in 2014/15: Was there a (negative) financial bubble?, Energy Policy, 96, 383-396,

Gomes, M., and A. Chaibi (2014): “Volatility Spillovers Between Oil Prices And Stock Returns: A Focus On Frontier Markets,” Journal of Applied Business Research, 30(2), 509–526.

Ji, Q., and Y. Fan (2012): “How does oil price volatility affect non-energy commodity markets?,” Applied Energy, 89(1), 273–280.

Johansen, A., D. Sornette, and O. Ledoit (1999): “Predicting Financial Crashes Using Discrete Scale Invariance,” Journal of Risk, 1(4), 5–32.

Johansen, A., O. Ledoit, and D. Sornette (2000): “Crashes as critical points,” International Journal of Theoretical and Applied Finance, 3(2), 219–255.

Kilian, L. (2008): “The Economic Effects of Energy Price Shocks,” Journal of Economic Literature, 46(4), 871–909.

Maugeri, L. (2010): Beyond the age of oil: the myths, realities, and future of fossil fuels and their alternatives. Praeger.

Phillips, P., S. Shi, and J. Yu  “Testing for Multiple Bubbles: Historical Episodes of Exuberance and Collapse in the SP500,” International Economic Review, 56 (4), 1043-1078.

Phillips, P., and S. Shi (2014): “Financial bubble implosion,” Discussion paper.

Sornette, D., O. Ledoit, and A. Johansen (1999): “Critical crashes,” Risk Magazine, 12(1), 91–95.

Tokic (2015): “The 2014 oil bust: Causes and consequences,” Energy Policy, 85, 162–169.

Yan, W., R. Woodard, and D. Sornette (2012): “Diagnosis and prediction of rebounds in financial markets,” Physica A: Statistical Mechanics and its Applications, 391(4), 1361–1380.

ENDNOTES

[1] First, I used tests for financial bubbles proposed by Phillips, Shi, and Yu (2015) and Phillips and Shi (2014). These tests are based on recursive and rolling right-tailed Augmented Dickey-Fuller unit root test, wherein the null hypothesis is of a unit root and the alternative is of a mildly explosive process. They can identify periods of statistically significant explosive price behavior and date-stamp their occurrence.  Second, I used the log-periodic power law (LPPL) model for negative financial bubbles developed by Yan, Woodard, and Sornette (2012). This model adapts the the Johansen-Ledoit-Sornette (JLS) model of rational expectation bubbles developed by Sornette, Ledoit, and Johansen (1999), Johansen, Sornette, and Ledoit (1999) and Johansen, Ledoit, and Sornette (2000) to the case of a price fall occurring during a transient negative bubble.

The preceding post comes to us from Dean Fantazzini, Associate Professor at the Moscow School of Economics at the Moscow State University.  The post is based on his recent article, which is entitled “The oil price crash in 2014/15: Was there a (negative) financial bubble?” and available here.