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Markov-Switching and Stochastic Volatility Diffusion Models of Short-Term Interest Rates

Markov-Switching and Stochastic Volatility Diffusion Models of Short-Term Interest Rates This article empirically compares the Markov-switching and stochastic volatility diffusion models of the short rate. The evidence supports the Markov-switching diffusion model. Estimates of the elasticity of volatility parameter for single-regime models unanimously indicate an explosive volatility process, whereas the Markov-switching models estimates are reasonable. Itis found that either Markov switching or stochastic volatility, but not both, is needed to adequately fit the data. A robust conclusion is that volatility depends on the level of the short rate. Finally, the Markov-switching model is the best for forecasting. A technical contribution of this article is a presentation of quasi-maximum likelihood estimation techniques for the Markov-switching stochastic-volatility model. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Business & Economic Statistics Taylor & Francis

Markov-Switching and Stochastic Volatility Diffusion Models of Short-Term Interest Rates

Journal of Business & Economic Statistics , Volume 20 (2): 15 – Apr 1, 2002
15 pages

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References (49)

Publisher
Taylor & Francis
Copyright
© American Statistical Association
ISSN
1537-2707
eISSN
0735-0015
DOI
10.1198/073500102317351949
Publisher site
See Article on Publisher Site

Abstract

This article empirically compares the Markov-switching and stochastic volatility diffusion models of the short rate. The evidence supports the Markov-switching diffusion model. Estimates of the elasticity of volatility parameter for single-regime models unanimously indicate an explosive volatility process, whereas the Markov-switching models estimates are reasonable. Itis found that either Markov switching or stochastic volatility, but not both, is needed to adequately fit the data. A robust conclusion is that volatility depends on the level of the short rate. Finally, the Markov-switching model is the best for forecasting. A technical contribution of this article is a presentation of quasi-maximum likelihood estimation techniques for the Markov-switching stochastic-volatility model.

Journal

Journal of Business & Economic StatisticsTaylor & Francis

Published: Apr 1, 2002

Keywords: Quasi-maximum likelihood estimation; Short rate; Term structure

There are no references for this article.