A New Perspective on Gaussian Dynamic Term Structure Models

By Scott Joslin, Kenneth J. Singleton, Haoxiang Zhu. 2011

468 citations recorded by the source corpus.

In any canonical Gaussian dynamic term structure model (GDTSM), the conditional forecasts of the pricing factors are invariant to the imposition of no-arbitrage restrictions. This invariance is maintained even in the presence of a variety of restrictions on the factor structure of bond yields. To establish these results, we develop a novel canonical GDTSM in which the pricing factors are observable portfolios of yields. For our normalization, standard maximum likelihood algorithms converge to the global optimum almost instantaneously. We present empirical estimates and out-of-sample forecasts for several GDTSMs using data on U.S. Treasury bond yields. The Author 2011. Published by Oxford University Press on behalf of The Society for Financial Studies. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com., Oxford University Press.

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Citations in the corpus

International channels of the Fed’s unconventional monetary policy (2013, unknown): “Several studies, including Cochrane and Piazzesi (2008), Joslin et al. (2010), and Bauer (2011), have advocated restricting risk prices in estimating dynamic term structure models, in order to reduce the statistical uncertainty and the small-sample bias in the inference about the VAR parameters.”
A Tractable Framework for Zero-Lower-Bound Gaussian Term Structure Models (2013, unknown): “Joslin, Singleton, and Zhu (2011) provides an empirically reliable method for estimating GATSMs in general, and Hamilton and Wu (2012) provides another approach.”
Risk Premia in Crude Oil Futures Prices (2013, background): “…¼ 0: (35) Since24 f0tf4t f8t 35 ¼ 24a0a4 a8 35þ 24 b00b04 b08 35xt ; our normalization could alternatively be described as xt ¼ H1ðf0t ; f4t ; f8tÞ0 for all t: Following Joslin et al. (2011) and Hamilton and Wu (2013), this can be implemented by defining x1 and x2 to be the eigenvalues of rQ ¼ r L.”
The Signaling Channel for Federal Reserve Bond Purchases (2013, unknown): “We use the normalization of Joslin et al. (2011). The estimation is a two-step procedure: First, the parameters of the vector autoregression (VAR) for the risk factors are estimated using ordinary least squares (OLS).”
Testable Implications of Affine Term Structure Models (2011, background): “8 Joslin et al. (2011) derived a similar result for maximum likelihood estimation.”
Modeling Yields at the Zero Lower Bound: Are Shadow Rates the Solution? (2013, methodology): “In particular, there is no explicit partial differential equation (PDE) that bond prices must satisfy, including boundary conditions, for the absence of arbitrage as in Kim and Singleton (2012). 16. For details of the derivations, see Christensen and Rudebusch (2015). 17. Due to the nonlinear measur”
Estimation of Affine Term Structure Models with Spanned or Unspanned Stochastic Volatility (2014, unknown): “New procedures for Gaussian ATSMs have made them easier to estimate, further increasing their popularity ; see, Joslin et al. (2011), Christensen et al. (2011), Hamilton and Wu (2012), Adrian et al. (2012) and Diez de Los Rios (2013).”
Why Gaussian macro-finance term structure models are (nearly) unconstrained factor-VARs (2011, background): “…to output or inflation,4 or about expectations puzzles in bond markets.5 The theoretical propositions and empirical illustrations about the role of no-arbitrage restrictions in Joslin, Singleton, and 1This has been widely documented for U.S. Treasury yields (e.g., Litterman and Scheinkman (1991)).”