Monetary Policy & Inflation | US
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Last week, US 10Y yields exceeded 5% for the first time since July 2007 as investors digested comments from Federal Reserve officials reacting to the September CPI data. There was limited reaction from the doves (Harker, Williams, Barkin, Goolsbee, and Bostic) while Waller and Fed Chair Powell stood out by leaving the door open for higher hikes. Notably, Chair Powell said that ‘inflation is still too high, and a few months of good data are only the beginning of what it will take to build confidence that inflation is moving down sustainably toward our goal.’ Dominique continues to expect no hike in November and a December hike, as well as two to three hikes in H2 2024.
Turning to market moves, US 10Y yields closed the week at 4.93% (+30bps WoW, +56bps MoM). Meanwhile, the yield on the policy-sensitive US 2Y closed the week at 5.07% (+3bps WoW, -1bps MoM). In terms of yield curve inversion, the magnitude of the 2s10s inversion sat at -14bps on Friday, up from year lows of -109bps seen in early July. The probability of recession increases with yield curve inversion.
The probability of recession within the next twelve months, assigned by the 2Y10Y part of the yield curve, dropped to 66% from 78% a week earlier (Chart 1). Meanwhile, the Fed’s recession model, which uses the 3M10Y part of the yield curve, produced a 37% chance of recession (Chart 2).
Background to Models
We introduced two models for predicting US recessions using the slope of the US yield curve. When long-term yields start to fall towards or below short-term yields, the curve flattens or inverts. This has often predicted a recession in subsequent months. Our model is based on the 2s10s curve compared to a model from the Fed that is based on 3M10Y curve. We believe that the 2Y better captures expectations for Fed hikes in coming years and is therefore more forward-looking.
Dalvir Mandara is a Quantitative Researcher at Macro Hive. Dalvir has a BSc Mathematics and Computer Science and an MSc Mathematical Finance both from the University of Birmingham. His areas of interest are in the applications of machine learning, deep learning and alternative data for predictive modelling of financial markets.
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