Modelling and Forecasting Short-Term Interest Rate Volatility: A Semiparametric Approach

Posted: 25 Apr 2012

See all articles by Ai Jun Hou

Ai Jun Hou

Stockholm University

Sandy Suardi

University of Wollongong

Multiple version iconThere are 2 versions of this paper

Date Written: July 20, 2009

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.

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

Suggested Citation

Hou, Ai Jun and Suardi, Sandy, Modelling and Forecasting Short-Term Interest Rate Volatility: A Semiparametric Approach (July 20, 2009). Journal of Empirical Finance, Vol. 18, No. 4, 2011, Available at SSRN: https://ssrn.com/abstract=2045861

Ai Jun Hou (Contact Author)

Stockholm University ( email )

Universitetsvägen 10
Stockholm, Stockholm SE-106 91
Sweden

Sandy Suardi

University of Wollongong ( email )

Northfields Avenue
Wollongong, New South Wales 2522
Australia

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