Term structure dynamics with macro-factors using high frequency data

Hwagyun Kim () and Hail Park

Journal of Empirical Finance, 2013, vol. 22, issue C, 78-93

Abstract: 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.

Keywords: Term structure estimation; Latent macro-factors; Yield forecasts (search for similar items in EconPapers)
JEL-codes: C13 C32 E43 G12 (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (10)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:empfin:v:22:y:2013:i:c:p:78-93

DOI: 10.1016/j.jempfin.2013.03.003

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