The Affine Arbitrage-Free Class of Nelson-Siegel Term Structure Models

By Jens H. E. Christensen, Francis X. Diebold, Glenn D. Rudebusch. 2010

498 citations recorded by the source corpus.

Subfields: Finance, Mathematical Physics, Modeling and Simulation

View Paper: https://www.semanticscholar.org/paper/47842f1b76f3bbd02cad9214de1ccf7f9c6784d2

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Citations in the corpus

Bond Liquidity Premia (2012, methodology): “A Arbitrage-Free Term Structure Model This section follows Christensen et al. (2007) and provides a description of the term structure model.”
Analyzing the Term Structure of Interest Rates Using the Dynamic Nelson–Siegel Model With Time-Varying Parameters (2010, unknown): “The importance of λ and its constancy over time is also discussed in Christensen, Diebold, and Rudebusch (2007), where an arbitrage-free version of the Nelson–Siegel framework is proposed.”
The Signaling Channel for Federal Reserve Bond Purchases (2013, unknown): “Krishnamurthy and Vissing-Jorgensen (2011), henceforth KVJ, consider changes in money market futures rates and conclude that signaling likely was an important channel for LSAP effects on both safe and risky assets.”
Testable Implications of Affine Term Structure Models (2011, methodology): “, 2006), inferringmarket expectations of inflation (Christensen et al., 2010), and evaluating the effects of the extraordinary central bank interventions during the financial crisis (Christensen et al., 2009; Smith, 2010; Hamilton andWu, 2012a). Gürkaynak and Wright (2012) and Rudebusch (2010) provi”
The response of interest rates to U.S. and U.K. quantitative easing (2012, methodology): “13 For related applications of the AFNS model, see Christensen et al. (2010), who examine yields for nominal and real Treasuries, and Christensen et al. (2009), who examine short-term LIBOR and highly rated financial firms corporate bond rates. 14 As discussed in CDR, with a unit root in the level f”
Estimation of Affine Term Structure Models with Spanned or Unspanned Stochastic Volatility (2014, background): “New procedures for Gaussian ATSMs have made them easier to estimate, further increasing their popularity; see, Joslin, Singleton, and Zhu(2011), Christensen, Diebold, and Rudebusch(2011), Hamilton and Wu(2012), Adrian, Crump, and Moench(2012) and Diez de Los Rios(2013).”
A Theoretical Foundation for the Nelson and Siegel Class of Yield Curve Models (2012, unknown): “…in the generic GATSM.7 Once the appropriate NS model has been chosen, consideration needs to be given to whether the model is made AF by adding the AF term with respect to the NS components (as in Christensen et al. (2009, 2011), or directly via the HJM framework as in the example of section 4).”
Modeling Yields at the Zero Lower Bound: Are Shadow Rates the Solution? (2013, methodology): “It improves the econometric identification of the latent factors, which facilitates model estimation.7 The CR model is an arbitrage-free NelsonSiegel (AFNS) representation as introduced in Christensen et al. (2011) with three latent state variables, Xt = (Lt, St, Ct).”
Macroeconomics and the Term Structure (2012, methodology): “Rudebusch (1995) and Longstaff (2000) considered regressions of the form of equations (8) and (9) where the maturity of the long bond is measured in days or weeks. Little evidence is found against the expectations hypothesis. However, Piazzesi and Swanson (2008) conducted a similar exercise with sho”
A New Perspective on Gaussian Dynamic Term Structure Models (2011, unknown): “9 Duffie and Kan (1996) and Cochrane and Piazzesi (2005) also propose to use an identification scheme where the yields themselves are factors.”
Term Structure Analysis with Big Data: One-Step Estimation Using Bond Prices (2017, methodology): “Our benchmark DTSM for estimation is the arbitrage-free Nelson–Siegel (AFNS) model of Christensen et al. (2011), which is a Gaussian affine model where a level, slope, and curvature factor explain the evolution of the yield curve. We estimate this DTSM on Canadian bond prices using both the one- and”