Nonparametric Recovery of the Yield Curve Evolution from Cross-Section and Time Series Information
Davide La Vecchia and
Oliver Linton ()
Cambridge Working Papers in Economics from Faculty of Economics, University of Cambridge
We develop estimation methodology for an additive nonparametric panel model that is suitable for capturing the pricing of coupon-paying government bonds followed over many time periods. We use our model to estimate the discount function and yield curve of nominally riskless government bonds. The novelty of our approach is the combination of two different techniques: cross-sectional nonparametric methods and kernel estimation for time varying dynamics in the time series context. The resulting estimator is able to capture the yield curve shapes and dynamics commonly observed in the fixed income markets. We establish the consistency, the rate of convergence, and the asymptotic normality of the proposed estimator. A Monte Carlo exercise illustrates the good performance of the method under different scenarios. We apply our methodology to the daily CRSP bond dataset, and compare with the popular Diebold and Li (2006) method.
Keywords: nonparametric inference; panel data; time varying; yield curve dynamics (search for similar items in EconPapers)
JEL-codes: C13 C14 C22 G12 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
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Persistent link: https://EconPapers.repec.org/RePEc:cam:camdae:1916
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