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Term structure dynamics with macro-factors using high frequency data


  • Kim, Hwagyun
  • Park, Hail


This paper empirically studies the role of macro-factors in explaining and predicting daily bond yields. In general, macro-finance models use low-frequency data to match with macroeconomic variables available only at low frequencies. To deal with this, we construct and estimate a tractable no-arbitrage affine model with both conventional latent factors and macro-factors by imposing cross-equation restrictions on the daily yields of bonds with different maturities, credit risks, and inflation indexation. The estimation results using both the US and the UK data show that the estimated macro-factors significantly predict actual inflation and the output gap. In addition, our daily macro-term structure model forecasts better than no-arbitrage models with only latent factors as well as other statistical models.

Suggested Citation

  • Kim, Hwagyun & Park, Hail, 2013. "Term structure dynamics with macro-factors using high frequency data," Journal of Empirical Finance, Elsevier, vol. 22(C), pages 78-93.
  • Handle: RePEc:eee:empfin:v:22:y:2013:i:c:p:78-93 DOI: 10.1016/j.jempfin.2013.03.003

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    References listed on IDEAS

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    More about this item


    Term structure estimation; Latent macro-factors; Yield forecasts;

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • 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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