Specification Analysis of Affine Term Structure Models

Dai, Qiang; Singleton, Kenneth J.

doi:10.1111/0022-1082.00278

Cited by 1,600 publications

(1,051 citation statements)

References 27 publications

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“…Dai and Singleton (2000) proposed the normalization conditions Σ = I N , δ 1 ≥ 0, c = 0 and ρ lower triangular.…”

Section: Example 1: Latent Factor Modelmentioning

confidence: 99%

“…The experiment did not uncover them because the other difficulties with maximization were sufficiently severe that for the 100 different starting values used, only one of these 6 configurations was reached. Dai and Singleton (2000) and Singleton (2006) originally proposed lower triangularity of ρ or ρ Q and nonnegativity of δ 1 as sufficient identifying conditions. Our proposition establishes that one needs a further condition such as ρ Q 11 ≥ ρ Q 22 ≥ ρ Q 33 to have a globally identified structure.…”

Section: Example 3: Macro Finance Model With 12 Lags (Mf12)mentioning

confidence: 99%

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Identification and estimation of Gaussian affine term structure models

Hamilton

2012

Journal of Econometrics

171

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This paper develops new results for identification and estimation of Gaussian affine term structure models. We establish that three popular canonical representations are unidentified, and demonstrate how unidentified regions can complicate numerical optimization. A separate contribution of the paper is the proposal of minimum-chi-square estimation as an alternative to MLE. We show that, although it is asymptotically equivalent to MLE, it can be much easier to compute. In some cases, MCSE allows researchers to recognize with certainty whether a given estimate represents a global maximum of the likelihood function and makes feasible the computation of small-sample standard errors.

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“…Dai and Singleton (2000) proposed the normalization conditions Σ = I N , δ 1 ≥ 0, c = 0 and ρ lower triangular.…”

Section: Example 1: Latent Factor Modelmentioning

confidence: 99%

Section: Example 3: Macro Finance Model With 12 Lags (Mf12)mentioning

confidence: 99%

Identification and estimation of Gaussian affine term structure models

Hamilton

2012

Journal of Econometrics

171

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“…We combine various features of the above models, by considering a version of the oft examined 3-factor model due to CKLS (1992), which is discussed in detail in Dai and Singleton (2000). In particular, we consider the Chen (1996) 3-factor model:…”

Section: Three Factor Model (Chen)mentioning

confidence: 99%

“…Hong and Li (2005) and Chen and Hong (2008)). Another category that includes papers by Gallant and Tauchen (1997), Andersen and Lund (1997), Dai and Singleton (2000), Ahn, Dittmar and Gallant (2002), ABL (2004), Thompson (2008), Aït-Sahalia and Kimmel (2007), and Corradi and Swanson (2005a), to name but a few, who use parametric methods to examine the "goodness of fit" of models. The testing approaches reviewed and used in this paper falls within this category.…”

Section: Introductionmentioning

confidence: 99%

In- and out-of-sample specification analysis of spot rate models: Further evidence for the period 1982–2008

Cai

Swanson

2011

Journal of Empirical Finance

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We review and construct consistent in-sample specification and out-of-sample model selection tests on conditional distributions and predictive densities associated with continuous multifactor (possibly with jumps) and (non)linear discrete models of the short term interest rate. The results of our empirical analysis are used to carry out a "horserace" comparing discrete and continuous models across multiple sample periods, forecast horizons, and evaluation intervals. Our evaluation involves comparing models during two distinct historical periods, as well as across our entire weekly sample of Eurodollar deposit rates from 1982-2008. Interestingly, when our entire sample of data is used to estimate competing models, the "best" performer in terms of distributional "fit" as well as predictive density accuracy, both in-sample and out-of-sample, is the three factor Chen (CHEN: 1996) model examined by Andersen, Benzoni and Lund (2004). Just as interestingly, a logistic type discrete smooth transition autoregression (STAR) model is preferred to the "best" continuous model (i.e. the one factor Cox, Ingersoll, and Ross (CIR: 1985) model) when comparing predictive accuracy for the "Stable 1990s" period that we examine. Moreover, an analogous result holds for the "Post 1990s" period that we examine, where the STAR model is preferred to a two factor stochastic mean model. Thus, when the STAR model is parameterized using only data corresponding to a particular sub-sample, it outperforms the "best" continuous alternative during that period. However, when models are estimated using the entire dataset, the continuous CHEN model is preferred, regardless of the variety of model specification (selection) test that is carried out. Given that it is very difficult to ascertain the particular future regime that will ensue when constructing ex ante predictions, thus, the CHEN model is our overall "winning" model, regardless of sample period.

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“…Another strand of the literature, which is closer to our modelling philosophy, has grown from the affine term-structure models of, for example, Duffie and Kan (1996) and Dai and Singleton (2000), and draws a more direct connection between macro economic variables and the evolution of the term-structure [see Dai, Singleton and Yang (2003), Bansal and Zhou (2002), Driffill and Kenc (2003), Bansal, Tauchen andZhou (2003), andEvans (2003)]. These models tend to focus on regime-switches in the parameters characterising the mean, the mean-reversion speed and the volatility of the short rate process and generally allow for the presence of two states.…”

Section: Introductionmentioning

confidence: 99%