Transcription of A New Perspective on Gaussian Dynamic Term Structure Models
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A New Perspective on Gaussian Dynamic Term Structure Models
ANew Perspective on Gaussian DynamicTerm Structure ModelsScottJoslinMIT Sloan School of ManagementKenneth J. SingletonGraduate School of Business, Stanford University, and NBERH aoxiang ZhuGraduate School of Business, Stanford UniversityInany canonical Gaussian Dynamic term Structure model (GDTSM), the conditional fore-casts of the pricing factors are invariant to the imposition of no-arbitrage restrictions. Thisinvariance is maintained even in the presence of a variety of restrictions on the factorstructure of bond yields. To establish these results, we develop a novel canonicalGDTSMin which the pricing factors are observable portfolios of yields. For our normalization,standard maximum likelihood algorithms converge to the global optimum almost instanta-neously. We present empirical estimates and out-of-sample forecasts for severalGDTSM susing data on Treasury bond yields. (JELE43, G12, C13)Dynamicmodels of the term Structure often posit a linear factor Structure for acollection of yields, with these yields related to underlying factorsPthrougha no-arbitrage relationship.
The Review of Financial Studies / v 24 n 3 2011 conditional covariance matrix of yields factors from the VAR. That is, given ΣP, the entire cross-section of bond yields in anN-factorGDTSM is fully de- termined by only the N +1 parameters r
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