Tunaru, Diana (2017) Gaussian estimation and forecasting of the U.K. yield curve with multi-factor continuous-time models. International Review of Financial Analysis, 52 . pp. 119-129. ISSN 1057-5219. (doi:10.1016/j.irfa.2017.05.003) (KAR id:65972)
PDF
Author's Accepted Manuscript
Language: English
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
|
|
Download this file (PDF/1MB) |
|
Request a format suitable for use with assistive technology e.g. a screenreader | |
Official URL: http://dx.doi.org/10.1016/j.irfa.2017.05.003 |
Abstract
In this paper we will estimate the term structure of daily U.K. interest rates using a range of more flexible continuous-time models. A multivariate framework is employed for the dynamic estimation and forecasting of four classic models over the eventful period of 2000–2013. The extensions are applied in two stages to four- and five-factor formulations, allowing us to assess the potential benefit of gradually increasing the model-flexibility. The Gaussian estimation methods for dynamic continuous-time models yield insightful comparative results concerning the two different segments of the yield curve, short- and long-term, respectively. In terms of in-sample performance the newly extended multi-factor general model is superior to all other restricted models. When compared to benchmark discrete-time models, the out-of-sample performance of the extended continuous-time models seems to be consistently superior with regards to the short-term segment of the yield curve.
Item Type: | Article |
---|---|
DOI/Identification number: | 10.1016/j.irfa.2017.05.003 |
Uncontrolled keywords: | continuous-time models, forecasting, multi-factor diffusion models with feedbacks, term structure of interest rates, U.K. yield curve, Gaussian estimation |
Subjects: | H Social Sciences > H Social Sciences (General) |
Divisions: | Divisions > Kent Business School - Division > Department of Accounting and Finance |
Depositing User: | Thomas Wiffen |
Date Deposited: | 08 Feb 2018 14:38 UTC |
Last Modified: | 05 Nov 2024 11:04 UTC |
Resource URI: | https://kar.kent.ac.uk/id/eprint/65972 (The current URI for this page, for reference purposes) |
- Link to SensusAccess
- Export to:
- RefWorks
- EPrints3 XML
- BibTeX
- CSV
- Depositors only (login required):