Balance Sheet Capacity and the Price of Crude

I’ve written before about the macrofinancial importance of broker-dealers (a.k.a. Wall Street banks). I emphasized the key role played by dealers in the so-called shadow banking system and have shown that fluctuations in balance sheet capacity explain the cross-section of stock excess returns. I have also argued for a monetary-financial explanation of the commodities rout. In this post, I will show that fluctuations in dealer balance sheet capacity also explain fluctuations in the price of crude.

The evidence can be read off Figure 1. Recessions are shown as dark bands. The top-left plot shows the real price of crude for reference. The spikes in the 1970s correspond to the oil price shocks in 1973 and 1979. Note the price collapse in 1986 and the price shock that attended the Iraqi occupation of Kuwait (the spike in the 1990 recession). Note also the extraordinary run-up in the price of crude during the 2000s boom and the return of China-driven triple digit prices after the great recession. Finally, note the dramatic oil price collapse in 2014 due to the US fracking revolution. We know that much of the fluctuation in the oil price was a result of geopolitical, supply-side and exogenous demand-side factors. My claim is that much of the rest is driven by the excess elasticity of the financial intermediary sector.

Figure 1. Source: Haver Analytics, author’s calculations.

Specifically, I show that fluctuations in the balance sheet capacity of US securities broker-dealers predict fluctuations in the oil price. We define balance sheet capacity as the log of the ratio of aggregate financial assets of broker-dealers to the aggregate financial assets of US households. We stochastically detrend the quarterly series by subtracting the trailing 4-quarter moving average from the original series. The plot on the top-right displays the stochastically detrended balance sheet capacity. We will show that it predicts 1-quarter ahead excess returns on crude.

We run 30-quarter rolling regressions of the form,

{R^{crude}_{t+1}=\alpha+\beta\times capacity_{t}+\varepsilon_{t+1}}, \qquad (1)

where {R^{crude}_{t+1}} is the return on Brent in quarter {t+1} in excess of the risk-free rate and {capacity_{t}} is the shock to balance sheet capacity in quarter {t}. We must take care to interpret rolling regressions because instead of two parameters suggested by equation (1), we are in effect running 183 regressions with different parameters.

The plot on the bottom right displays the percentage of variation explained in each predictive regression. We see that balance sheet capacity became a significant predictor of the price of crude in the mid-1980s. It’s predictive capability diminished in the mid-1990s, before gaining new heights in the 2000s. The period 1999-2007 was the heydey of financially-driven fluctuations in the price of crude. That relationship collapsed in the second quarter of 2007. During the financial crisis and the period of postcrisis financial repression, the relationship disappeared entirely. It only recovers at the very end of our sample in 2016.

The bottom-left plot in Figure 1 displays a signed measure of the influence of balance sheet capacity on the price of crude. We display the product of the slope coefficient in equation (1) with one minus its p-value. This measure kills three birds with one stone. We can (a) keep track of the sign of the slope coefficients (to see whether or not it reverses direction too much), (b) get an additional handle on the time-variation of the strength of the predictive relationship, and (c) control the noise by attenuating the slope coefficients in inverse proportion to their statistical significance. Note that we have reversed the direction of the Y axis in the plot on the bottom-left.

The slope and significance metric tells a story that is very similar to the one told by the percentage of variation explained. Moreover, we can see that the relationship is economically large and negative. The interpretation is that positive shocks to balance sheet capacity compress the risk premium embedded in the price of crude. When balance sheet capacity is plentiful, risk arbitrageurs (speculators who make risky bets) bid away expected excess returns. Conversely, when balance sheet capacity is scarce, risk arbitrageurs are constrained in the amount of leverage they can obtain from their dealers and are therefore compelled to leave expected excess returns on the table.

The main result above—that dealer balance sheet growth predicts returns on crude oil—was originally obtained by Erkko Etula for his doctoral dissertation at Harvard. 

One thought on “Balance Sheet Capacity and the Price of Crude

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