A Comparison of Univariate Stochastic Volatility Models for U.S. Short Rates Using EMM Estimation
In 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.
|Date of creation:||Aug 2006|
|Date of revision:|
|Contact details of provider:|| Postal: Box 353330, Seattle, WA 98193-3330|
Web page: http://www.econ.washington.edu/
More information through EDIRC
When requesting a correction, please mention this item's handle: RePEc:udb:wpaper:uwec-2006-17. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Michael Goldblatt)
If references are entirely missing, you can add them using this form.