@techreport{Hong2002Nonparametric,
abstract = {We propose two nonparametric transition density-based speci\th{}cation tests for continuous-time diffusion models. In contrast to marginal density as used in the literature, transition density can capture the full dynamics of a diffusion process, and in particular, can distinguish processes with the same marginal density but different transition densities. To address the concerns of the \th{}nite sample performance of nonparametric methods in the literature, we introduce an appropriate data transformation and correct the boundary bias of kernel estimators. As a result, our tests are robust to persistent dependence in data and provide reliable inferences for sample sizes often encountered in empirical \th{}nance. Simulation studies show that our tests have reasonable size and good power against a variety of alternatives in \th{}nite samples even for data with highly persistent dependence. Besides the single-factor diffusion models, our tests can be applied to a broad class of dynamic economic models, such as discrete time series models, time-inhomogeneous diffusion models, stochastic volatility models, jump-diffusion models, and multi-factor term structure models. When applied to daily Eurodollar interest rates, our tests overwhelmingly reject some popular spot rate models, including those with nonlinear drifts that some existing tests can not reject after correcting size distortions. We \th{}nd that models with nonlinear drifts do not signi\th{}cantly improve the goodness-of-\th{}t, and the main source of model inadequacy seems to be the violation of the Markov assumption. We also \th{}nd that GARCH, regime switching and jump diffusion models perform signi\th{}cantly better than single-factor diffusion models, although they are far from being adequate to fully capture the interest rate dynamics. Our study shows that nonparametric methods are a reliable and powerful tool for analyzing \th{}nancial data.},
address = {Berlin},
author = {Yongmiao Hong and Haitao Li},
copyright = {http://www.econstor.eu/dspace/Nutzungsbedingungen},
keywords = {C4; E4; G0; 330; Boundary bias; Continuous-time model; Hellinger metric; Kernel method; Parameter estimation uncertainty; Probability integral transform; Quadratic form; Short-term interest rate; Transition density},
language = {eng},
note = {urn:nbn:de:kobv:11-10048908},
number = {2002,32},
publisher = {Humboldt-Universit\"{a}t},
title = {Nonparametric specification testing for continuous-time models with application to spot interest rates},
type = {Discussion Papers, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes},
url = {http://hdl.handle.net/10419/65366},
year = {2002}
}
