Term Structure Analysis with Big Data
Martin M. Andreasen (),
Jens H.E. Christensen () and
Glenn Rudebusch ()
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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
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)
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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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