‘OG’ Yield Curve Whisperer’s Fourth Horseman Of Recession Rode In On Wednesday

CAMPBELL HARVEY: I’m Campbell Harvey. I’m a professor of finance at Duke University. And in 1986, I published a thesis that detailed the predictive ability of the yield curve to forecast U.S. economic growth.

CARDIFF GARCIA: This was the seminal paper on this topic. Am I right?

HARVEY: It is the first paper.

GARCIA: (Laughter).

That’s an excerpt from an August 2018 NPR interview with Cam Harvey, the “OG of yield curve whisperers.”

Over the last six months, analysts, economists and armchair macro watchers have spilled gallons upon gallons of digital ink and debated until their voices are hoarse in an effort to come to some kind of consensus on whether “this time is different” when it comes to an inverted curve presaging an economic downturn. There are myriad reasons to argue that it is. At the same time, historical precedent is a valuable ally in debate, and it’s tantalizingly easy to pull up a chart of your favorite slope, slap the red recession indicators on top and call the argument “settled”.

For reference, here’s a handy chart from Goldman that shows the proportion of the curve that’s inverted.

(Goldman)

For what it’s worth, the 3-month-5-year curve wrapped up a full quarter of inversion on Wednesday (bottom pane in the visual).

Why is that notable? Well, back when this portion of the curve first inverted, the above-mentioned Cam Harvey flagged it in a blog post called “The Fourth Horseman of the Next Recession Approaches”.

The first three horseman were the Duke-CFO survey (which, incidentally, was flagged by BofA’s Ajay Kapur in his December SOS to central bankers), the “realization of anti-growth protectionism” and market volatility.

“Since March 7, the five-year yield has been lower than the three-month Treasury bill yield. If it stays that way for a full quarter — not merely a few days or a few hours — then the model predicts recession will follow”, Harvey wrote, on March 12. “I consider the yield curve the last of four horsemen of the recession to rear its head”.

Draw your own conclusions.


 

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3 thoughts on “‘OG’ Yield Curve Whisperer’s Fourth Horseman Of Recession Rode In On Wednesday

  1. Need credit spreads to widen for a recession, although the longer and harder the yield curve stays inverted the likelier that credit spreads will do so…

  2. I’ve had an on-going question that I’m hoping some more “experienced” folks can answer. In 2006/2007, 2000, ’89, ’81, and ’79 was there such comprehensive and expansive coverage of the yeild curve inversion and that it presages a recession?

    My assumption is that current market participants exist in a much more robust context of available information, which directly impacts how someone behaves – so, does the active observation of the inversion of the yield curve, and it’s historical meaning, impact how market participants act – and will they act differently than in other times of inversion? And if that’s the case, how does that impact interpretations of the yield curve moving forward?

    1. @Jbona3 my understanding is that public interest in the yield curve’s predictive ability really gained traction after the financial crisis in ’08. The thesis was published in ’86, and I don’t think it was really a topic of discussion prior to that. I’ve heard that leading up to the ’89, ’00, and ‘The Great Recession’, there was more of the ‘here’s why this time is different’ attitude prevailing rather than a ‘lets keep an eye on this’ kind of thought process.

      Your assumption is well grounded, but I don’t think that we have a good understanding of how the attention given to it could affect the behavior of market participants. One thing to keep in mind, though, is that even though the yield curve is focused on the US macro-economic environment, it’s sourced globally (because governments, corporations, and individuals around the world are also buying US treasuries) and in that way it sort of becomes a global sentiment analysis. I.e., it’s almost a measure of confidence based on billions of inputs around the globe. So, trying to parse how those billions of participants will react to the knowledge of what’s potentially in the pipeline becomes understandably convoluted.

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