Accurate Short-Term Yield Curve Forecasting using Functional Gradient Descent
AbstractWe propose a multivariate nonparametric technique for generating reliable shortterm 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 non-linearities 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.
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Bibliographic InfoPaper provided by Department of Economics, University of St. Gallen in its series University of St. Gallen Department of Economics working paper series 2007 with number 2007-24.
Length: 51 pages
Date of creation: Jun 2007
Date of revision:
Conditional mean and variance estimation; Filtered Historical Simulation; Functional Gradient Descent; Term structure; Multivariate CCC-GARCH models;
Other versions of this item:
- Fabio Trojani, 2007. "Accurate Short-Term Yield Curve Forecasting using Functional Gradient Descent," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 5(4), pages 591-623, Fall.
- NEP-ALL-2007-07-20 (All new papers)
- NEP-CMP-2007-07-20 (Computational Economics)
- NEP-ECM-2007-07-20 (Econometrics)
- NEP-ETS-2007-07-20 (Econometric Time Series)
- NEP-FOR-2007-07-20 (Forecasting)
- NEP-MAC-2007-07-20 (Macroeconomics)
- NEP-MON-2007-07-20 (Monetary Economics)
- NEP-RMG-2007-07-20 (Risk Management)
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