The impacts of the coronavirus on the global economy: Part VI The oil market

On January 24, 2020, a barrel of the West Texas Intermediate (WTI) crude oil for delivery on May 20 was traded at US$ 55.54, on the New York Mercantile Exchange (which is the largest physical commodity futures exchange in the world). At that time, the spot price of the WTI crude oil was US$ 54.09. This means, the cost of carry (i.e., the interest rate plus the storage cost minus the convenience yield) of the crude oil was 8.3 % per annum. One month before the delivery date (i.e. on April 20), the closing price of the May WTI crude oil fell to $ -2.60 and its spot price went further down to $ -36.98, which is unprecedented (see Figure 1).

Figure 1: Daily Spot and May Futures Prices of the WTI Crude Oil, Jan 24, 2020 - April 21, 2020.

First, the spot price of the WTI and its futures price both became negative, on April 20, and, second, they diverged suddenly. The law of supply and demand explains the decrease of the spot price of WTI crude oil into negative territory. The overproduction of crude oil (i.e., the increase in its supply) and the simultaneous drop in its demand due to the lockdown of economies worldwide result in the drop of its spot price. When he spot price of the WTI was $ -36.98, its futures price went as low as $ -39.44. The reasons for this important drop are: (1) there was no longer a convenience yield from holding inventories of crude oil compared to holding its futures contracts and (2) the storage cost of this commodity increased due to its overproduction. The divergence between the closing price of the May WTI crude oil and its spot price simply resulted from the fact that traders anticipated that the situation was temporary since the spot price of the alternative Brent crude oil was US$ 17.36 that day. This then caused the futures price of the WTI to rise.

Stock markets keep recovering from the crisis caused by the outbreak of the coronavirus. Between April 3 and April 10, the global financial turbulence score fell again, going from 7.7 to 4.1 (a 46.8 % decrease). As for the VIX, the implied volatility index, it went down from 41.67 to 38.15 (an 8 % decrease). The global financial turbulence score has been falling since the peak of March 13. One can wonder if the market bottom is reached, with this important fall.

Figure 2: Global Financial Turbulence Scores, Jan 8, 2000 - Apr 10, 2020.

Is the financial crisis over?

It is true that the high turbulence characterizing a financial crisis went down considerably. In my first post dedicated to the impacts of the coronavirus on the global economy [here], I predicted that the probability of a high turbulence in stock markets across the globe would decrease to 32 % by May 29. This probability remains unchanged, given the new available data. As one could see in Figure 2, the current level of the global financial turbulence score is still well above 3.4, which is the level expected during a turning point (represented by the green dotted line). By the end of the month of May, the probability of exiting the financial crisis would be 21.4 % and the probability of returning into it after a short recovery would be 33.7 %. Thus, the financial crisis is not over yet!

Dataset and Code

The Latest Global Financial Turbulence Scores.

Date	Score
Feb 14, 2020	1.36
Feb 21, 2020	6.23
Feb 28, 2020	3.68
Mar 6, 2020	9.74
Mar 13, 2020	12.89
Mar 20, 2020	9.37
Mar 27, 2020	8.03
Apr 3, 2020	7.70
Apr 10, 2020	4.10

The components indices of the global financial turbulence score

(1) NYA: the New York Stock Exchange composite index, (2) IXIC: the NASDAQ composite, (3) N225, the Tokyo Stock Exchange average index, (4) FTAS, the London Stock Exchange FTSE all share, (5) HSI, the Hong Kong Stock Exchange index, (6) N150, the Euronext Next 150 index, (7) GSPTSE, the Toronto Stock Exchange composite index, (8) BSESN, the Bombay Stock Exchange sensitive index, (9) GDAXI, the Frankfurt Stock Exchange performance index, (10) AXJO, the Australian Securities Exchange S&P 200, (11) SSMI, the SIX Swiss exchange mid-cap index, and (12) IBOVESPA, the Brazil Stock Exchange index.

Formula

$$d_t² = (r_t - μ ) Σ ^-1 (r_t - μ )',

where $d$ denotes the turbulence score, the vector $r$_t lists the current growth rates of the benchmark indices, the vector $$ μ their historical averages, and $$ Σ designates their variance-covariance matrix. For further details, see Mark Kritzman and Li Yuanzhen (2010).

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The impacts of the coronavirus on the global economy: Part V Is a comparison with the Great Depression possible?

More and more commentators and economists are comparing the current economic crisis to the 1929 depression (also known as the Great Depression). For some of them, the current crisis is even worse. Are they right? In my humble opinion, they are wrong.

First of all, the Great Depression was caused by a stock market crash (that started on October 24, 1929). On the other hand, the current economic crisis is voluntarily induced by governments as a policy response to a global health crisis. Thus, the turbulence we are observing on stock exchanges around the globe is the consequence but not the cause of the current economic situation. The causes of the stock market crash of October 24, 1929 were mainly consumerism, easy credit, and speculation. Its consequences were mainly bank failures, hoarding, massive job losses, and poverty. Second, if the causes of the Great Depression and those of the current crisis are not the same, their consequences are not similar either. Currently, investors have fear, but they have not lost confidence in financial institutions. Millions of people are not currently employed, but they have not definitely lost their jobs. Third, the Great Depression lasted almost a decade (from 1929 till the outbreak of World War II), whereas the current crisis is hopefully coming to an end, since governments have started preparing plans to exit the lockdown restrictions they imposed some months ago.

It is true that the stock markets have been very turbulent since the outbreak of the coronavirus disease. On March 13, the situation on the major exchanges was the worst record over the past two decades (see Figure 1). Since then, it has been gradually improving. Between March 27 and April 3, the global financial turbulence score fell from 8.03 to 7.70 (a 4% decrease). The decrease in the VIX, the fear index, was much higher. This latter index went down from 46.8 to 41.67 (an 11% decrease). This situation is explained by the rise in the major benchmark indices. On the New York Stock Exchange, the NASDAQ, the Bombay Stock Exchange, the Toronto Stock Exchange, and the Brazil Stock Exchange, the benchmark indices increased by more than 10%.

Figure 1: Global Financial Turbulence Scores and VIX, Jan 8, 2000 - Apr 3, 2020

Oil markets are also suffering severely from the on-going crisis. On March 13, in the heat of the financial turbulence, the spot price of the West Texas Intermediate (WTI) and the Brent fell by 28.9% and 33.5%, respectively. (The WTI and the Brent are both sweet light crude oil serving as benchmark in pricing.) The following week they further declined by 25.3% and 23.9%, respectively. As one can see in Figure 2, the prices of these crude oil keep falling. This price crash is due to thee overproduction of oil and the decrease in its demand after the lockdown of several economies. This situation is affecting not only oil producing firms and the industries depending directly on this activity, but also public finance. In Canada, the provinces of Alberta and Newfoundland and Labrador are the most affected by this unexpected oil price crash.

Weekly Cushing, Oklahoma WTI and Europe Brent Spot Prices, Jan 1, 2000 - Apr 3, 2020

Dataset and Code

The Latest Global Financial Turbulence Scores.

Date	Score
Feb 14, 2020	1.36
Feb 21, 2020	6.23
Feb 28, 2020	3.68
Mar 6, 2020	9.74
Mar 13, 2020	12.89
Mar 20, 2020	9.37
Mar 27, 2020	8.03
Apr 3, 2020	7.70

The components indices of the global financial turbulence score

(1) NYA: the New York Stock Exchange composite index, (2) IXIC: the NASDAQ composite, (3) N225, the Tokyo Stock Exchange average index, (4) FTAS, the London Stock Exchange FTSE all share, (5) HSI, the Hong Kong Stock Exchange index, (6) N150, the Euronext Next 150 index, (7) GSPTSE, the Toronto Stock Exchange composite index, (8) BSESN, the Bombay Stock Exchange sensitive index, (9) GDAXI, the Frankfurt Stock Exchange performance index, (10) AXJO, the Australian Securities Exchange S&P 200, (11) SSMI, the SIX Swiss exchange mid-cap index, and (12) IBOVESPA, the Brazil Stock Exchange index.

Formula

$$d_t² = (r_t - μ ) Σ ^-1 (r_t - μ )',

where $d$ denotes the turbulence score, the vector $r$_t lists the current growth rates of the benchmark indices, the vector $$ μ their historical averages, and $$ Σ designates their variance-covariance matrix. For further details, see Mark Kritzman and Li Yuanzhen (2010).

The Fall in Oil Price and Economic Growth

The fall in the price of oil will slow down growth in Canada through a decrease in business investment.

While consumers are enjoying the fall in the price of oil, federal and provincial governments are worrying about its negative impacts on their finance. Finance Minister Joe Oliver struggling to balance as promised the federal budget had no choice than postponing its announcement until April at the earliest [here]. In Alberta, the largest oil producer in Canada, the fall in oil price turned the budget surplus the province projected this year into a $500 million deficit [here].

The fall in the price of oil decreases the price of gasoline. This raises consumer surplus, increases the use of cars and consequently the consumption of gasoline. The induced increase in households’ mobility raises their propensity to consume: eating out, shopping...

The first panel of the figure below shows the evolution of the average propensity to consume along with that of the consumer price index (CPI) of gasoline.   The average propensity to consume is the ratio of households’ final consumption expenditure to their income. I have used gross domestic product (GDP) as a proxy for households’ income. 

Average Propensity to Consume, Investment Intensity, and CPI of Gasoline, Canada, 1981:Q1-2014:Q3

Growth in the average propensity to consume is negatively correlated with growth in the CPI of gasoline. The correlation coefficient between the two growth rates is -.011.

Only consumers benefit from a fall in the price of oil. Businesses and governments incur losses.  

Growth in investment intensity is positively correlated with growth in the CPI of gasoline (.16). Investment intensity is the share of business investment in GDP. A fall in the price of oil reduces the profit margin, increases uncertainty, and discourages new investment in the oil industry. To understand the importance of the oil industry in Canada, note that in provinces such as Alberta and Saskatchewan oil and gas extraction represented an average of 28 % and 18% of their respective annual GDP between 1997 and 2013. To these two shares, one can add the support activities for oil and gas extraction, which represent about 2% of the GDP of these provinces. Across Canada, oil and gas extraction represents on average 6% of GDP.

As far as federal and provincial governments are concerned, the correlation coefficient between their general revenue as a share of GDP and the CPI of gasoline is .23. 

So what could stimulate the economy given this expected slowdown? Bank of Canada’s decision on January 21^st to cut its key rate from one percent to .75% could stimulate investment and further increase households’ consumption.