Accurate Short-Term Yield Curve Forecasting Using Functional Gradient Descent

Posted: 1 Jun 2009

See all articles by Francesco Audrino

Francesco Audrino

University of St. Gallen

Fabio Trojani

Swiss Finance Institute; University of Geneva

Multiple version iconThere are 2 versions of this paper

Date Written: Fall 2007


We propose a multivariate nonparametric technique for generating reliable short-term historical yield curve scenarios and confidence intervals. The approach is based on a Functional Gradient Descent (FGD) estimation of the conditional mean vector and covariance matrix of a multivariate interest rate series. It is computationally feasible in large dimensions and it can account for nonlinearities in the dependence of interest rates at all available maturities. Based on FGD we apply filtered historical simulation to compute reliable out-of-sample yield curve scenarios and confidence intervals. We back-test our methodology on daily USD bond data for forecasting horizons from 1 to 10 days. Based on several statistical performance measures we find significant evidence of a higher predictive power of our method when compared to scenarios generating techniques based on (i) factor analysis, (ii) a multivariate CCC-GARCH model, or (iii) an exponential smoothing covariances estimator as in the RiskMetricsTM approach.

Keywords: conditional mean and variance estimation, filtered historical simulation, functional gradient descent, multivariate CCC-GARCH models, term structure

Suggested Citation

Audrino, Francesco and Trojani, Fabio, Accurate Short-Term Yield Curve Forecasting Using Functional Gradient Descent (Fall 2007). Journal of Financial Econometrics, Vol. 5, Issue 4, pp. 591-623, 2007, Available at SSRN: or

Francesco Audrino (Contact Author)

University of St. Gallen ( email )

Bodanstrasse 6
St. Gallen, CH-9000

Fabio Trojani

Swiss Finance Institute ( email )

c/o University of Geneva
40, Bd du Pont-d'Arve
CH-1211 Geneva 4

University of Geneva ( email )

Geneva, Geneva

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Abstract Views
PlumX Metrics