Short rate forecasting based on the inference from the CIR model for multiple yield curve dynamics
dc.contributor.author | Hin, L. | |
dc.contributor.author | Dokuchaev, Nikolai | |
dc.date.accessioned | 2017-01-30T11:49:31Z | |
dc.date.available | 2017-01-30T11:49:31Z | |
dc.date.created | 2015-10-07T03:43:48Z | |
dc.date.issued | 2016 | |
dc.identifier.citation | Hin, L. and Dokuchaev, N. 2016. Short rate forecasting based on the inference from the CIR model for multiple yield curve dynamics. Annals of Financial Economics. 11 (1): 1650004. | |
dc.identifier.uri | http://hdl.handle.net/20.500.11937/15379 | |
dc.identifier.doi | 10.1142/S2010495216500044 | |
dc.description.abstract |
In this paper, we propose a strategy to extract the information on the market participants’ expectation of the future short rate from the cross-sectional zero coupon bond prices. In line with the current market practice of building different yield curves for different tenors, we construct multiple one-factor short rate processes to pin down the salient features of the yield curve at different tenors. We represent this information in the form of the Cox–Ingersoll–Ross model implied parameters, and show that this information can be used to forecast the future short rate. This approach of representing the information on the market participants’ consensus in the form of implied model parameters and using these implied parameters for forecasting purposes resembles the approach of representing the market expectation of the underlying asset volatility reflected by stock option prices in the form of implied volatility, and using it to forecast the realized volatility. We illustrate the implementation of this method using historical US STRIPS prices and effective Federal Funds rate. | |
dc.publisher | World Scientific Publishing Co. | |
dc.subject | optimization | |
dc.subject | CIR spot rate model | |
dc.subject | interest rates | |
dc.title | Short rate forecasting based on the inference from the CIR model for multiple yield curve dynamics | |
dc.type | Journal Article | |
dcterms.source.volume | TBA | |
dcterms.source.issn | 2010-4952 | |
dcterms.source.title | Annals of Financial Economics | |
curtin.note |
Electronic version of an article published as Annals of Financial Economics, Vol. 11, No. 01 (2016), doi: 10.1142/S2010495216500044 © copyright World Scientific Publishing Company, | |
curtin.department | Department of Mathematics and Statistics | |
curtin.accessStatus | Open access |