A Comparison of Univariate Stochastic Volatility Models for U.S. Short Rates Using EMM Estimation
AbstractIn this paper, the efficient method of moments (EMM) estimation using a seminonparametric (SNP) auxiliary model is employed to determine the best fitting model for the volatility dynamics of the U.S. weekly three-month interest rate. A variety of volatility models are considered, including one-factor diffusion models, two-factor and three-factor stochastic volatility (SV) models, non-Gaussian diffusion models with Stable distributed errors, and a variety of Markov regime switching (RS) models. The advantage of using EMM estimation is that all of the proposed structural models can be evaluated with respect to a common auxiliary model. We find that a continuous-time twofactor SV model, a continuous-time three-factor SV model, and a discrete-time RS-involatility model with level effect can well explain the salient features of the short rate as summarized by the auxiliary model. We also show that either an SV model with a level effect or a RS model with a level effect, but not both, is needed for explaining the data. Our EMM estimates of the level effect are much lower than unity, but around 1/2 after incorporating the SV effect or the RS effect.
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Bibliographic InfoPaper provided by University of Washington, Department of Economics in its series Working Papers with number UWEC-2006-17.
Date of creation: Aug 2006
Date of revision:
This paper has been announced in the following NEP Reports:
- NEP-ALL-2007-05-12 (All new papers)
- NEP-ECM-2007-05-12 (Econometrics)
- NEP-ETS-2007-05-12 (Econometric Time Series)
- NEP-MAC-2007-05-12 (Macroeconomics)
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