Term Structure Analysis with Big Data

Martin M. Andreasen (), Jens H.E. Christensen () and Glenn Rudebusch
Additional contact information
Martin M. Andreasen: Aarhus University and CREATES, Postal: Department of Economics and Business Economics, Fuglesangs Allé 4, 8210 Aarhus V, Denmark
Jens H.E. Christensen: Federal Reserve Bank of San Francisco, Postal: Federal Reserve Bank of San Francisco, 101 Market Street MS 1130, San Francisco, CA 94105, USA

CREATES Research Papers from Department of Economics and Business Economics, Aarhus University

Abstract: Analysis of the term structure of interest rates almost always takes a two-step approach. First, actual bond prices are summarized by interpolated synthetic zero-coupon yields, and second, a small set of these yields are used as the source data for further empirical examination. In contrast, we consider the advantages of a one-step approach that directly analyzes the universe of bond prices. To illustrate the feasibility and desirability of the onestep approach, we compare arbitrage-free dynamic term structure models estimated using both approaches. We also provide a simulation study showing that a one-step approach can extract the information in large panels of bond prices and avoid any arbitrary noise introduced from a first-stage interpolation of yields.

Keywords: extended Kalman filter; fixed-coupon bond prices; arbitrage-free Nelson-Siegel model (search for similar items in EconPapers)
JEL-codes: C55 C58 G12 G17 (search for similar items in EconPapers)
Pages: 49
Date: 2017-09-18
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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Working Paper: Term Structure Analysis with Big Data (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:aah:create:2017-31

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