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Modelling and forecasting short-term interest rate volatility: A semiparametric approach

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  • Hou, Ai Jun
  • Suardi, Sandy

Abstract

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.

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Bibliographic Info

Article provided by Elsevier in its journal Journal of Empirical Finance.

Volume (Year): 18 (2011)
Issue (Month): 4 (September)
Pages: 692-710

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Handle: RePEc:eee:empfin:v:18:y:2011:i:4:p:692-710

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Web page: http://www.elsevier.com/locate/jempfin

Related research

Keywords: Interest rates GARCH modelling Nonparametric method Volatility estimation Forecasts;

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