Prediction Bias Correction for Dynamic Term Structure Models

24 Pages Posted: 15 Mar 2013  

Eran Raviv

APG Asset Management

Date Written: December 1, 2012

Abstract

When the yield curve is modelled using an affine factor model, residuals may still contain relevant information and do not adhere to the familiar white noise assumption. This paper proposes a pragmatic way to improve out of sample performance for yield curve forecasting. The proposed adjustment is illustrated via a pseudo out-of-sample forecasting exercise implementing the widely used Dynamic Nelson Siegel model. Large improvement in forecasting performance is achieved throughout the curve for different forecasting horizons. Results are robust to different time periods, as well as to different model specifications.

Keywords: Yield curve, Nelson Siegel, Time varying loadings, Factor models

JEL Classification: E43, E47, G17

Suggested Citation

Raviv, Eran, Prediction Bias Correction for Dynamic Term Structure Models (December 1, 2012). Available at SSRN: https://ssrn.com/abstract=2232798 or http://dx.doi.org/10.2139/ssrn.2232798

Eran Raviv (Contact Author)

APG Asset Management ( email )

Gustav Mahlerplein 3
Amsterdam, 1082 MS
Netherlands

HOME PAGE: http://eranraviv.com/about/

Paper statistics

Downloads
123
Rank
184,180
Abstract Views
689