A Comparison of Univariate Stochastic Volatility Models for U.S. Short Rates Using EMM Estimation

Ying Gu and Eric Zivot

No UWEC-2006-17, Working Papers from University of Washington, Department of Economics

Abstract: 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: 2006-08
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-mac
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://faculty.washington.edu/ezivot/research/ShortRateEMM.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:udb:wpaper:uwec-2006-17

Access Statistics for this paper

More papers in Working Papers from University of Washington, Department of Economics Contact information at EDIRC.
Bibliographic data for series maintained by Michael Goldblatt ().