Estimating Interest Rate Curves by Support Vector Regression
A model that seeks to estimate an interest rate curve should have two desirable capabilities in addition to the usual characteristics required from any function-estimation model: it should incorporate the bid-ask spreads of the securities from which the curve is extracted and restrict the curve shape. The goal of this article is to estimate interest rate curves by using Support Vector Regression (SVR), a method derived from the Statistical Learning Theory developed by Vapnik (1995). The motivation is that SVR features these extra capabilities at a low estimation cost. The SVR is specified by a loss function, a kernel function and a smoothing parameter. SVR models the daily U.S. dollar interest rate swap curves, from 1997 to 2001. As expected from a priori and sensibility analyses, the SVR equipped with the kernel generating a spline with an infinite number of nodes was the best performing SVR. Comparing this SVR with other models, it achieved the best cross-validation interpolation performance in controlling the bias-variance trade-off and generating the lowest error considering the desired accuracy fixed by the bid-ask spreads.
If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Volume (Year): 29 (2010)
Issue (Month): 5-6 ()
|Contact details of provider:|| Web page: http://www.tandfonline.com/LECR20|
|Order Information:||Web: http://www.tandfonline.com/pricing/journal/LECR20|
When requesting a correction, please mention this item's handle: RePEc:taf:emetrv:v:29:y:2010:i:5-6:p:717-753. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ()
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.