This article exploits the Itô's formula to derive the conditional moments vector for the class of interest rate models that allow for nonlinear volatility and flexible jump specifications. Such a characterization of continuous-time processes by the Itô conditional moment generator noticeably enlarges the admissible set beyond the affine jump-diffusion class. A simple generalized method of moments (GMM) estimator can be constructed based on the analytical solution to the lower-order moments, with natural diagnostics of the conditional mean, variance, skewness, and kurtosis. Monte Carlo evidence suggests that the proposed estimator has desirable finite sample properties relative to the asymptotically efficient maximum- likelihood estimator (MLE). The empirical application singles out the nonlinear quadratic variance as the key feature of the U.S. short-rate dynamics. , .
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References listed on IDEAS Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
Torben G. Andersen & Tim Bollerslev & Peter F. Christoffersen & Francis X. Diebold, 2005.
"Volatility Forecasting,"
PIER Working Paper Archive
05-011, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
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Torben G. Andersen & Tim Bollerslev & Peter F. Christoffersen & Francis X. Diebold, 2005.
"Volatility Forecasting,"
NBER Working Papers
11188, National Bureau of Economic Research, Inc.
[Downloadable!] (restricted)
Torben G. Andersen & Tim Bollerslev & Peter F. Christoffersen & Francis X. Diebold, 2005.
"Volatility Forecasting,"
CFS Working Paper Series
2005/08, Center for Financial Studies.
[Downloadable!]
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