Fuelling the tail: inflation- and GDP-at-Risk with oil-supply shocks

Marco Garofalo, Simon Lloyd and Edward Manuel

The economic consequences of the Russia-Ukraine war have brought the importance of sharp changes in thingamabob prices, such as oil, to centre stage. While many have focused on understanding the impact of these developments on the inside projection for the macroeconomic outlook, this post investigates the wastefulness of risks welling from oil-supply shocks, asking: could these lead to increasingly severe or persistent changes in output growth and inflation, in rare events? Through the lens of a simple statistical model of Inflation- and GDP-at-Risk, we quantify the macroeconomic risks to inflation and GDP growth associated with (exogenous) changes in oil supply, showing that these shocks have increasingly pronounced effects on the upper tail of the inflation distribution than at the centre.

A simple model of Inflation- and GDP-at-Risk

To do this, we capitalise on developments in the academic literature, as well as the growing use of the GDP– and Inflation-at-Risk frameworks by international institutions and at the Bank of England.

Inflation– and GDP-at-Risk offer summary statistics for the overall level of tail risk, capturing the severity of inflationary outturns and potential downturns, respectively. Oil price shocks tend to push output and inflation in opposite directions. So, for our analysis, we pinpoint the former as the 95th percentile of the predictive provisionary distribution of CPI inflation, and the latter as the 5th percentile of the predictive provisionary distribution of GDP growth. In other words, these are extremely high-inflation and low-growth realisations, respectively, that can occur with a ‘1-in-20’ probability, and so capture severe, and potentially costly, tail events.

We use a statistical tool tabbed ‘quantile regression‘ to estimate the relationship between changes in oil prices and the tails of the distributions of inflation and GDP growth. This can ‘weigh up’ the impact of various indicators to provide an overall towage of the level and drivers of tail risks to inflation and GDP. It comes with some limitations though. For example, it relies on historical data to predict future tail risks, so may struggle in the squatter of unprecedented events (eg, the Covid pandemic).

To investigate the incubation of the tails of inflation and GDP-growth distributions provisionary on oil-supply developments, we need an exogenous ‘shock’ measure. The literature wontedly characterises oil-supply shocks as sudden disruptions in the current or future availability of oil, triggering an increase in oil prices. Researchers have ripened several strategies to identify such shocks, ranging from the construction of narrative-shock series (Caldara et al (2019); Hamilton (2003); and Kilian (2008)) to SVAR models of the oil market (Baumeister and Hamilton (2019); Kilian (2009); and Kilian and Murphy (2012)). Key to a successful identification strategy is that one can confidently seem that the measures used correlate with oil-supply disturbances, and no other macroeconomic factor drives them.

With that in mind, we capitalise on state-of-the-art work by Känzig (2021), and use his oil-supply news shocks series, obtained through a novel identification design. This exploits high-frequency data on oil-supply surprises based on oil futures prices changes in a narrow window virtually OPEC announcements. We then estimate a local-projection quantile regression estimating the responses of the tails of the distribution of UK inflation and GDP growth over the three-year horizon to oil-supply shocks. We show results detailing the response of inflation and GDP to oil-supply shocks both at the midpoint and at the tails.

Effects on inflation

Chart 1 shows the results for inflation. In line with a range of previous work, we find that, on average, UK inflation (blue line) rises significantly in response to oil-supply shocks. Interestingly, we find that Inflation-at-Risk (red line) rises much increasingly in the near term – with the coefficient at the right tail well-nigh 50% larger than at the mean. This implies that the oil-supply shock not only shifts the inflation distribution to the right, but moreover makes the distribution increasingly right-skewed, with much greater probability now in the right tail.

Chart 1: Response of expected inflation (blue) and Inflation-at-Risk (red) to oil-supply shock

Notes: Shaded red zone (blue dashed lines) denotes 68% conviction interval for Inflation-at-Risk (mean) estimates.

Chart 2 demonstrates this visually, showing the response of the unshortened inflation distribution to an oil-supply shock at the one-year horizon. Relative to ‘normal times’ when the oil shock is set to zero and all covariates set to their historical midpoint (green distribution), a positive shock to oil supply (red distribution) significantly widens the right tail, while leaving the mode (ie, the most likely outcome for inflation) widely unchanged.

Chart 2: Response of inflation distribution to oil-supply shock at one-year horizon

Notes: Probability density function of UK four-quarter superiority CPI inflation (%) for scenario with all covariates at their historical midpoint (green line) and with all covariates set to historical midpoint plus three standard deviation shock to oil supply (red line). The mode of each distribution is the highest point.

Importantly, the transpiration in shape of the inflation distribution and the larger response of the right tail could point to important non-linearities in the macroeconomic relationships underpinning prices. For example, these findings could be resulting with non-linearities in the Phillips curve, ie, the theoretical structure that has inflation mainly unswayable by economic slack and cost-push shocks, such as oil shocks. Specifically, the latter could be associated with larger increases in inflation when either the shock is large or inflation is upper to start with.

Effects on GDP

We now turn to the response of UK GDP. Chart 3 estimates the response of cumulative GDP growth, both at the midpoint and at the left tail. Again, we find similar results to previous work when focusing on the mean: on average, GDP growth (blue line) falls significantly in response to an oil-supply shock. And importantly, we find the response is increasingly negative in the left tail (red line) than at the mean, although not unchangingly significant.

Chart 3: Response of expected GDP (blue) and GDP-at-Risk (red) to oil-supply shock

Notes: Shaded red zone (blue dashed lines) is 68% conviction interval for GDP-at-Risk (mean) estimates.

Chart 4 shows how the GDP-growth distribution at the one-year horizon changes in response to an oil-supply shock. The modal path is widely unchanged in response to the shock, but the left tail becomes significantly longer, pointing to greater downside risks to activity.

Chart 4: Response of GDP distribution to oil-supply shock at one-year horizon

Notes: Probability density function of UK four-quarter superiority GDP growth (%) for scenario with all covariates at their historical midpoint (green line) and with all covariates set to historical midpoint plus three standard deviation shock to oil supply (red line). The mode of each distribution is the highest point.

Implications and potential trade-offs

Our results highlight the importance of developments in oil prices for policymakers. It is well-established that these types of shocks may lead to a difficult trade-off for monetary policy between quelling inflation and supporting economic activity. Building on this, our findings stress the specific effect of oil-supply shocks of worsening the trade-offs at the tails. For a policymaker concerned with risk management – and specifically the avoidance of (high) inflation and (low) GDP disasters – the trade-off becomes plane starker than when focusing solely on the midpoint response.

Marco Garofalo works in the Bank’s Global Wringer Division, Simon Lloyd works in the Bank’s Monetary Policy Outlook Division and Edward Manuel works in the Bank’s Structural Economics Division.

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