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

HOU, Ai Jun LU and Suardi, Sandy (2011) In Journal of Empirical Finance 18(4). p.692-710
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, levels e¤ect 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 misspeci.ed. Empirical investigation with the U.S. three-month Treasury bill rates suggests that the semipara-metric procedure produces superior in-sample and out-of-sample forecast of short rate changes volatility compared with the widely used single-factor diffusion... (More)
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, levels e¤ect 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 misspeci.ed. Empirical investigation with the U.S. three-month Treasury bill rates suggests that the semipara-metric 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. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Volatility esti- mation, GARCH modelling, Nonparametric method, Forecasts
in
Journal of Empirical Finance
volume
18
issue
4
pages
692 - 710
publisher
North-Holland
external identifiers
  • wos:000295297600009
  • scopus:80052173316
ISSN
0927-5398
DOI
10.1016/j.jempfin.2011.05.001
language
English
LU publication?
yes
id
0f2ab1e6-45ea-438e-9c00-35244e9e455a (old id 1763384)
date added to LUP
2011-01-21 15:02:29
date last changed
2017-08-20 04:14:05
@article{0f2ab1e6-45ea-438e-9c00-35244e9e455a,
  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, levels e¤ect 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 misspeci.ed. Empirical investigation with the U.S. three-month Treasury bill rates suggests that the semipara-metric 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.},
  author       = {HOU, Ai Jun and Suardi, Sandy},
  issn         = {0927-5398},
  keyword      = {Volatility esti- mation,GARCH modelling,Nonparametric method,Forecasts},
  language     = {eng},
  number       = {4},
  pages        = {692--710},
  publisher    = {North-Holland},
  series       = {Journal of Empirical Finance},
  title        = {Modelling and Forecasting Short-Term Interest Rate Volatility: A Semiparametric Approach},
  url          = {http://dx.doi.org/10.1016/j.jempfin.2011.05.001},
  volume       = {18},
  year         = {2011},
}