Accurate Short-Term Yield Curve Forecasting using Functional Gradient Descent

Francesco Audrino and Fabio Trojani ()

University of St. Gallen Department of Economics working paper series 2007 from Department of Economics, University of St. Gallen

Abstract: We 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.

Keywords: Conditional mean and variance estimation, Filtered Historical Simulation, Functional Gradient Descent, Term structure; Multivariate CCC-GARCH models (search for similar items in EconPapers)
Pages: 51 pages
Date: 2007-06
New Economics Papers: this item is included in nep-cmp, nep-ecm, nep-ets, nep-for, nep-mac, nep-mon and nep-rmg
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Citations: View citations in EconPapers (6)

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Journal Article: Accurate Short-Term Yield Curve Forecasting using Functional Gradient Descent (2007)
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Persistent link: https://EconPapers.repec.org/RePEc:usg:dp2007:2007-24

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