Recession Probability Back at 90%

US treasury yields climbed throughout last week after hawkish commentary from Federal Reserve (Fed) speakers. Chair Powell indicated that the terminal federal funds rate (FFR) may have to rise if growth and inflation remain strong. Markets followed, adding 50bp to the December 2023 FFR. Furthermore, data from the University of Michigan’s consumer survey on Friday showed U.S consumer sentiment improved in February (66.4, last month: 64.9), while household expectations of year-ahead inflation rebounded. Looking forward, we get January CPI data tomorrow – Dominique sees scope for a small negative surprise – and another series of Fed speakers.

Turning to market moves, US 10Y yields closed the week at 3.74% (+21bps WoW) compared to 4.50% (+20bps WoW) for the 2Y and 4.66% (+9bps WoW) for the 3M. The magnitude of the 2s10s inversion deepened to -82bps on Wednesday before closing the week at -76bps on Friday. The magnitude of the 3M10Y inversion reduced to -92bps compared to -104bps last week. The probability of recession increases with yield curve inversion.

Our recession model, which uses the 2Y10Y part of the yield curve, assigns an 90% chance of a recession within the next twelve months (Charts 1 and 3). Meanwhile, the Fed recession model, which uses the 3M10Y part of the yield curve, produces a 53% chance of recession (Chart 2). Notably, both models are producing recession probabilities higher than that of the 2007-2008 Global Financial Crisis (GFC).

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.

Bilal Hafeez is the CEO and Editor of Macro Hive. He spent over twenty years doing research at big banks – JPMorgan, Deutsche Bank, and Nomura, where he had various “Global Head” roles and did FX, rates and cross-markets research.

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