Modelling and Forecasting Short-Term Interest Rate Volatility: A Semiparametric Approach
(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)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/1763384
- author
- HOU, Ai Jun LU and Suardi, Sandy
- organization
- publishing date
- 2011
- 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
- 2016-04-01 15:03:30
- date last changed
- 2022-01-28 03:54:09
@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}},
keywords = {{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 = {{https://lup.lub.lu.se/search/files/4316769/1763387.pdf}},
doi = {{10.1016/j.jempfin.2011.05.001}},
volume = {{18}},
year = {{2011}},
}