We use necessary cookies to make our site work (for example, to manage your session). We’d also like to use some non-essential cookies (including third-party cookies) to help us improve the site. By clicking ‘Accept recommended settings’ on this banner, you accept our use of optional cookies.
Necessary cookies
Analytics cookies
Yes
Yes
Yes
No
Necessary cookies
Necessary cookies enable core functionality on our website such as security, network management, and accessibility. You may disable these by changing your browser settings, but this may affect how the website functions.
Analytics cookies
We use analytics cookies so we can keep track of the number of visitors to various parts of the site and understand how our website is used. For more information on how these cookies work please see our Cookie policy.
Likelihood inference in non-linear term structure models: the importance of the lower bound
Working papers set out research in progress by our staff, with the aim of encouraging comments and debate.
Published on
20 December 2013
Working Paper No. 481
By Martin Andreasen and Andrew Meldrum
This paper shows how to use adaptive particle filtering and Markov chain Monte Carlo methods to estimate quadratic term structure models (QTSMs) by likelihood inference. The procedure is applied to a quadratic model for the United States during the recent financial crisis. We find that this model provides a better statistical description of the data than a Gaussian affine term structure model. In addition, QTSMs account perfectly for the lower bound whereas Gaussian affine models frequently imply forecast distributions with negative interest rates. Such predictions appear during the recent financial crisis but also prior to the crisis.