The Central Tendency: A Second Factor in Bond Yields

Balduzzi, Pierluigi; Das, Sanjiv Ranjan; Foresi, Silverio

doi:10.3386/w6325

Cited by 60 publications

(105 citation statements)

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“…In particular, it holds for stochastic mean models (5.3) and stochastic volatility models (5.4) alike. The square-root case for N = 2 is in Chen and Scott (1993), and the Gaussian case is in Balduzzi et al (1998). Models with only one state variable (namely the short rate r) have one persistent level factor.…”

Section: Level Slope and Curvaturementioning

confidence: 99%

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Affine Term Structure Models

Piazzesi

2010

Handbook of Financial Econometrics: Tools and Techniques

270

154

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Section: Level Slope and Curvaturementioning

confidence: 99%

“…The Brownian motions z r and z θ are independent. Balduzzi et al (1998) assume that σ θ (θ t ) does not depend on θ t , which makes the stochastic mean normally distributed. This model is a A 0 (2)-model.…”

Section: Labels Based On Moments Of the Short Ratementioning

confidence: 99%

Affine Term Structure Models

Piazzesi

2010

Handbook of Financial Econometrics: Tools and Techniques

270

154

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“…The second is a three-factor SCT model, which is similar to the models originally proposed by Balduzzi et al (1996Balduzzi et al ( , 1998:…”

Section: Modelsmentioning

confidence: 99%

“…One is a stochastic volatility (SV) model in which the short-rate volatility has a multiplicative form, and the other is a stochastic central tendency (SCT) model, as proposed by Balduzzi et al (1996Balduzzi et al ( , 1998, in which all state variables are instantaneously correlated and have level-dependent volatilities. Numerical results based on the two models are that the second-order approximation is accurate for maturities of up to five years and that the third-order approximation is effective for maturities longer than this.…”

Section: Introductionmentioning

confidence: 99%

Modeling the term structure of interest rates with general diffusion processes: A moment approximation approach

Takamizawa

Shoji

2009

Journal of Economic Dynamics and Control

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We propose an analytical approximation of the term structure of interest rates under general diffusion processes of the short-rate and state variables. A method of approximating conditional moments as the solution to a system of ordinary differential equations is applied to the pricing of bonds. Numerical experiments based on two illustrative models show that the second-order approximation is accurate for maturities of up to five years and the third-order approximation is effective for longer maturities.We also show the possibility of improving the second-order approximation without much increasing the computational burden.JEL classification: C63, G12

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“…Indeed, many do not have closed form solutions, particularly those involving one or multiple latent variables (see e.g. the stochastic mean model of Balduzzi et al (1998), the stochastic volatility model of Heston (1993), the three-factor model of Chen (1996), and the three-factor model with jumps discussed in the noteworthy paper by Andersen, Benzoni and Lund (ABL: 2004)). This issue has implications not only for pricing formulae derived from these models, but also for estimation.…”

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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The Central Tendency: A Second Factor in Bond Yields

Cited by 60 publications

References 0 publications

Affine Term Structure Models

Affine Term Structure Models

Modeling the term structure of interest rates with general diffusion processes: A moment approximation approach

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

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