Is the Short Rate Drift Actually Nonlinear

By David A. Chapman, Neil D. Pearson. 2000

271 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/709d3632e11706c43d5c8bfb26e975dfb2149d5e

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Citations in the corpus

Nonlinear Mean Reversion in the Short-Term Interest Rate (2003, background): “While both Pritsker (1998) and Chapman and Pearson (2000) suggest that nonparametric methods may be unreliable in the detection of nonlinearities, there is a more general concern in estimating time-series models that has nothing to do with nonparametric methods.”
Nonparametric specification testing for continuous-time models with application to spot interest rates (2002, unknown): “Third, as pointed out by Chapman and Pearson (2000), a conventional kernel density estimator produces biased estimates near the boundaries of the observations, due to the asymmetric coverage of the kernel for the data in the boundary regions.”
Bandwidth Selection in Nonparametric Kernel Testing (2008, result): “Thus we believe that this improved test further reinforces the findings of Chapman and Pearson (2000) and Fan and Zhang (2003) that there is no definitive answer to the question of whether the short rate drift is actually nonlinear.”
Nonparametric Specification Testing for Continuous-Time Models with Applications to Term Structure of Interest Rates (2005, unknown): “Chapman and Pearson (2000) also show that the nonparametric methods used by Ait-Sahalia (1996) and Stanton (1997) produce biased estimates near the boundaries of data, which could produce a spurious nonlinear drift.”
Time-dependent Diffusion Models for Term Structure Dynamics and the Stock Price Volatility (2003, background): “For instan e, GBM: dXt = Xt dt+ Xt dWt; VAS: dXt = ( 0 + 1Xt) dt+ dWt; CIR: dXt = ( 0 + 1Xt) dt+ pXt dWt; CKLS: dXt = ( 0 + 1Xt) dt+ X t dWt: These time-homogeneous models are a spe i family of the nonparametri model dXt = (Xt) dt+ (Xt) dWt; (3) studied by Stanton (1997), Fan and Yao (1998) and Chap”
Numerical simulation of a strongly nonlinear Ait-Sahalia-type interest rate model (2011, background): “Some authors [3, 7 ] pointed out that the Ait-Sahalia test has poor finite sample performance because of persistent dependence in interest rate data and slow convergence of the nonparametric density estimator.”
Simulation-Based Estimation of Contingent-Claims Prices (2009, unknown): “When ML is used to estimate one-factor models for short term interest rates, Ball and Torous (1996) and Chapman and Pearson (2000) provided evidence of large finite sample biases in the mean reversion parameter.”
Asset Pricing under the Quadratic Class (2002, background): “Most recently, Chapman and Pearson (2000) and Duffee and Stanton (2001) find that econometric problems make even linear models look nonlinear in small samples and thus cast doubt on the robustness of the previous evidence on nonlinearities.”
Likelihood-based specification analysis of continuous-time models of the short-term interest rate (2003, methodology): “This finding is particularly remarkable in light of Chapman and Pearson (2000), who argue that there may be a small-sample bias toward finding nonlinearity where none exists.”
A Reexamination of Diffusion Estimators With Applications to Financial Model Validation (2003, methodology): “In our implementation, the values of the model parameters are cited from Chapman and Pearson (2000), that is, Š D 0214591 ˆ D 0085711‘ D 007830, and ã D 1=250.”