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Modeling and forecasting short-term interest rate volatility: a semi-parametric approach
Stockholm University, Faculty of Social Sciences, Stockholm Business School, Finance.
2011 (English)In: Journal of Empirical Finance, ISSN 0927-5398, E-ISSN 1879-1727, Vol. 18, no 4, 692-710 p.Article in journal (Refereed) Published
Abstract [en]

This paper employs a semiparametric procedure to estimate the diffusion process of short-term interest rates. The Monte Carlo study shows that the semiparametric approach produces more accurate volatility estimates than models that accommodate asymmetry, level effect and serial dependence in the conditional variance. Moreover, the semiparametric approach yields robust volatility estimates even if the short rate drift function and the underlying innovation distribution are misspecified. Empirical investigation with the U.S. three-month Treasury bill rates suggests that the semiparametric procedure produces superior in-sample and out-of-sample forecast of short rate changes volatility compared with the widely used single-factor diffusion models. This forecast improvement has implications for pricing interest rate derivatives.

Place, publisher, year, edition, pages
2011. Vol. 18, no 4, 692-710 p.
Keyword [en]
Interest rates; GARCH modelling; Nonparametric method; Volatility estimation; Forecasts
National Category
Business Administration
Identifiers
URN: urn:nbn:se:su:diva-116970DOI: 10.1016/j.jempfin.2011.05.001OAI: oai:DiVA.org:su-116970DiVA: diva2:809626
Available from: 2015-05-04 Created: 2015-05-04 Last updated: 2017-12-04Bibliographically approved

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