Accurate Yield Curve Scenarios Generation using Functional Gradient Descent

Fabio Trojani () and Francesco Audrino

No 14, Computing in Economics and Finance 2005 from Society for Computational Economics

Abstract: We propose a multivariate nonparametric technique for generating reliable historical yield curve scenarios and confidence intervals. The approach is based on a Functional Gradient Descent (FGD) estimation of the conditional mean vector and volatility 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 volatility estimators as in the RiskMetrics approach

Keywords: Conditional mean and volatility estimation; Filtered Historical Simulation; Functional Gradient Descent; Term structure; Multivariate CCC-GARCH models (search for similar items in EconPapers)
JEL-codes: C14 C15 (search for similar items in EconPapers)
Date: 2005-11-11
New Economics Papers: this item is included in nep-ecm and nep-for
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