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Machine Learning and the Yield Curve:Tree-Based Macroeconomic Regime Switching

Author

Listed:
  • Siyu Bie

    (City University of Hong Kong)

  • Francis X. Diebold

    (University of Pennsylvania)

  • Jingyu He

    (City University of Hong Kong)

  • Junye Li

    (Fudan University)

Abstract

We explore tree-based macroeconomic regime-switching in the context of the dynamic Nelson-Siegel (DNS) yield-curve model. In particular, we customize the treegrowing algorithm to partition macroeconomic variables based on the DNS model’s marginal likelihood, thereby identifying regime-shifting patterns in the yield curve. Compared to traditional Markov-switching models, our model offers clear economic interpretation via macroeconomic linkages and ensures computational simplicity. In an empirical application to U.S. Treasury bond yields, we find (1) important yield curve regime switching, and (2) evidence that macroeconomic variables have predictive power for the yield curve when the short rate is high, but not in other regimes, thereby refining the notion of yield curve “macro-spanning”.

Suggested Citation

  • Siyu Bie & Francis X. Diebold & Jingyu He & Junye Li, 2024. "Machine Learning and the Yield Curve:Tree-Based Macroeconomic Regime Switching," PIER Working Paper Archive 24-028, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    • Handle: RePEc:pen:papers:24-028
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    More about this item

    Keywords

    Decision Tree; Macro-Finance; Term Structure; Regime Switching; Dynamic Nelson-Siegel Model; Bayesian Estimation;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • E43 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Interest Rates: Determination, Term Structure, and Effects
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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