Summary

• In this contribution, I suggest a simple and novel way to predict a global energy crisis and how this could be transformed into a global economic/financial crisis.
• Since uncertainty is a major component of a crisis, and the standard deviation is a measure of uncertainty, then this statistic may also be a measure of a crisis.
• In an energy world highly dominated by oil, the standard deviation of oil prices could therefore be taken as a measure of a global energy crisis.
• But the standard deviation may not pick up in an accurate way uncertainty along such volatile variables as oil prices.
• Hence a new measure of oil price uncertainty is advanced which is found to be useful to forecast both the occurrence of a global economic/financial crisis and its magnitude.

This contribution has two objectives. One, to suggest a simple way to predict a global energy crisis, and two, to analyze the tenuous dividing line between a global energy crisis and a global economic and/or financial downturn.

Following a recent article published on a leading specialized journal. an energy crisis is a society-wide economic problem caused by a constricted supply of energy leading to diminished availability and increased price to consumers. This definition precludes the possibility of a crisis from a demand perspective translated into a persistent downfall of oil prices. Hence it appears to have difficulties to explain the current energy crisis.

Indeed the latest oil price collapse seems to be a bit different from at least four other energy crises occurred since the late 1980s but somewhat similar to that taken place beginning in 2004. In effect, whereas all those four previous energy crises were characterized by a supply restraint followed by an extraordinary and temporary rise in oil prices, the present one seems to be in general a demand generated and permanent crunch.

This latter characteristic is precisely what the prevailing plight shares in common with the paradigmatic change I observed back in 2009 and 2010 while envisaging the inauguration of the lithium era in the world. Just as since 2004 there has been an enduring upsurge in oil prices to levels never experienced before, we can now talk as well about a permanent rather than a circumstantial oil price downfall, a point I have first made in another article .

It should be however clarified that this time the falling oil prices are not likely to lead us to a situation pre-2004. Thus, regardless of the specific reasons why that might be the case which are, incidentally, part of a forthcoming piece of mine, in this contribution I will first suggest a simple way to predict (based on oil prices) not only a global energy crisis but also its magnitude or relative importance.

Needless to say, here by a global energy crisis I mean either a sudden and astounding oil price downfall or a sudden and astounding oil price upsurge on the understanding that oil has nowadays become the key energy resource in the world.

Since uncertainty is a major component of a crisis and the standard deviation of a variable is a measure of uncertainty, then as a first approximation, it might be reasonable to assume that the standard deviation of oil prices could be taken as a measure of a global energy crisis.

One might think that the higher the standard deviation of oil prices, the higher the probability of an energy crisis. However, this indicator may not pick up in an accurate way uncertainty along such volatile variables as oil prices.

Using official data on daily oil prices from the Energy Information Administration (EIA), in Table 1 and Charts 1-2, I show yearly oil price averages and standard deviations for the period 1986-2014 (WTI) and the period 1987-2014 (Brent). As it can be seen, three sets of results are noticeable.

First, the highest standard deviations on both data sets either coincide with or precede oil price peaks (in 1987, 1990, 2000, 2008) or an oil price downfall (in 2014, albeit this is not yet reflected in the annual data).

Second, except for two exceptions, one in 2010 (for WTI) and another in 2013 (for Brent), beginning in 2004, yearly standard deviations are higher than the total average standard deviation for the periods under consideration while yearly oil prices are seen to have always superseded total average oil prices. In a series of previous articles (the last of which is dated in 2011) I have suggested that not only yearly oil prices but also their volatility (as measured by yearly standard deviations) in relation to average values of both variables for a given period of years may affect adoption of lithium batteries paving the way towards electrification of the global automotive industry. This could be indeed a Permanent Paradigmatic Shift just as the one I envisioned in a blog published as far back as 2008.

And third, within the sub-period 2004-2014, highest standard deviations for 2007-2009 also happened together with the highest yearly oil price ever, followed by one of the most notorious yearly oil price downfall in the whole time frame under observation (both for WTI and Brent oil prices).

Interestingly enough, these three trends reflect the most important global energy crises that the world has faced in the last three decades or so.

Table 1 — International Oil Prices: Yearly Averages and Standard Deviations

Chart 1

Source: EIA . This chart is an updated and expanded version of another one originally published here in 2010.

Chart 2

Source: EIA . This chart is an updated and expanded version of another one originally published here in 2010.

One problem with this approach is that it doesn’t allow us to reasonably ascertain the magnitude and therefore the relative importance of each crisis. In fact, by simply looking at standard deviations of oil prices over the period 1986-2014, one may get a wrong impression of the relative severity of the different crises. This would force us to believe, for instance, that because the standard deviation in 2008 was about 4 times larger than that of 1990, the derived energy crisis in 2009 was also approximately 4 times graver than that in 1990. But we may indeed not have sufficient elements to conclude such a thing.

To overcome this difficulty, in this study I suggest using coefficients of variation instead of standard deviations. In general, the coefficient of variation is the standard deviation of a variable weighted against its mean. Specifically, the yearly coefficient of variation is defined here as the yearly standard deviation divided by the yearly average oil prices multiplied by 100. In Table 2 the coefficients of variation (both for WTI and Brent data) are presented.

As shown in Charts 3 and 4, the highest coefficients of variation help us identify reasonably well the timing of the most significant global energy crises since 1986.

In fact, 7 out of 9 and 6 out of 8 (Brent) yearly coefficients of variation — which appear to be above the total average coefficient of variation for the whole period — either coincide with or precede the 1987 Stock Crash, the 1990 Gulf War, the early 2000s Recession, the 2009 Great Recession, and the current crisis.

As stimulating as it might be, this finding by and large resembles our previous results using standard deviations. Unlike standard deviations, however, coefficients of variation now give us an indication as to the magnitude or relative importance of each crisis. Unfortunately, this is not completely straightforward because in some cases the results seem to vary depending on which oil price data set is utilized.

Table 2

Chart 3

In effect, leaving aside the Stock Market Crash for which we can rely on WTI data only, and the Early 2000s Recession as well as the current crisis in which both oil price data sets assign similar measures of magnitude, Brent oil prices are found to place more weight on the Gulf War rather than on the Great Recession. A question arises at this point as to which oil price data set is more relevant to get out of this conundrum.

According to a recent analysis. WTI might be a benchmark at which oil produced in much of the United States trades, whereas Brent would be a leading international benchmark.

Based on this reasoning, we would be tempted to argue that the Gulf War was indeed a worse global energy crisis than the Great Recession. But that may be at best an a priori statement. Why? Well, because up to this point we haven’t said a word about the real impact of these global energy crises on the world economy which could give us a clearer indication of the magnitude or relative importance of them. And this takes us directly to the second objective of this investigation, namely to analyze the tenuous dividing line between a global energy crisis and a global economic and/or financial downturn.

In what follows, we will assume that low and/or negative U.S. GDP growth rates can be thought of as a proxy for global economic and financial crises. Beware that the results of this study were not significantly different when using world GDP growth rate figures as compiled by the World Bank. This shouldn’t be at all surprising considering that U.S. and world GDP growth rates have been found to be highly correlated (0.79). One problem with the world data though is that they don’t include numbers for 2014, which would have made it impossible to discuss the current crisis (See Table 3).

Chart 4

Table 3

Sources: U.S. Bureau of Economic Analysis and World Bank .

In Charts 5 and 6 we can see the evolution of WTI and Brent Coefficients of Variation and U.S. GDP growth rates over the two periods of analysis under consideration. As it can be seen, the highest coefficients of variation — which also appear to be above the total average for the whole period — always precede the lowest U.S. GDP growth rates — which also appear to be below the total average for the whole period — confirming the occurrence of the world’s three most significant crises (Gulf War, Early 2000s Recession, and Great Recession) between 1986 or 1987 and 2014.

Two aspects of these four charts makes us unequivocally think that the Great Recession was much more significant than the Gulf War. For one thing, the concomitant fall of the U.S. GDP growth rate in the case of the Great Recession was much more pronounced than in the case of the Gulf War. For another, the amplitude of the crisis (as reflected by the area formed by all the coefficients of variation above or all the U.S. GDP growth rates below the total average at each crisis space) also seems to be more important in the first case than in the second one. Based on these results, and contrary to our initial expectations, we are now in a position to conclude that WTI (rather than Brent) oil prices were more effective to determine the magnitude of the Great Recession versus the Gulf War.

Hence yearly oil price coefficients of variation can help us predict the occurrence (and in certain instances the magnitude) of a global economic and/or financial crisis well before they actually take place.

Chart 5

Chart 6

As for the Stock Market Crash, it’s quite striking that the corresponding relatively high coefficient of variation in 1986 doesn’t seem to be reflected in any important fall of the U.S. GDP growth rate thereafter. It appears that even though oil prices had most likely something to do with the 1987 Stock Market Crash, just as it happened during the 1973-74 oil price hike, this didn’t result in a global economic and financial crisis. Why? Economic policy may have been the key here to prevent a global economic slump. Following an interesting comment to an analysis of the Great Recession. in 1987, when the stock market crashed, spending growth was raised, spending growth turned negative in 2008, bringing down both the stock market and the real economy (increasing unemployment). This of course lowers the order of magnitude of the 1987 Stock Market Crash in comparison with the other three global crises described above which is consistent with our overall findings.

Disclosure: The author has no positions in any stocks mentioned, and no plans to initiate any positions within the next 72 hours. (More. ) The author wrote this article themselves, and it expresses their own opinions. The author is not receiving compensation for it (other than from Seeking Alpha). The author has no business relationship with any company whose stock is mentioned in this article.