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Accurate Short-Term Yield Curve Forecasting using Functional Gradient Descent

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  • Francesco Audrino
  • Fabio Trojani

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.

Suggested Citation

  • Francesco Audrino & Fabio Trojani, 2007. "Accurate Short-Term Yield Curve Forecasting using Functional Gradient Descent," University of St. Gallen Department of Economics working paper series 2007 2007-24, Department of Economics, University of St. Gallen.
    • Handle: RePEc:usg:dp2007:2007-24
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    Cited by:

    1. Klaus Wohlrabe & Teresa Buchen, 2014. "Assessing the Macroeconomic Forecasting Performance of Boosting: Evidence for the United States, the Euro Area and Germany," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(4), pages 231-242, July.
    2. Francesco Audrino, 2012. "What Drives Short Rate Dynamics? A Functional Gradient Descent Approach," Computational Economics, Springer;Society for Computational Economics, vol. 39(3), pages 315-335, March.
    3. Audrino, Francesco, 2014. "Forecasting correlations during the late-2000s financial crisis: The short-run component, the long-run component, and structural breaks," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 43-60.
    4. Emrich, Eike & Pierdzioch, Christian, 2015. "Public goods, private consumption, and human-capital formation: On the economics of volunteer labour supply," Working Papers of the European Institute for Socioeconomics 14, European Institute for Socioeconomics (EIS), Saarbrücken.
    5. Emrich Eike & Pierdzioch Christian, 2016. "Public Goods, Private Consumption, and Human Capital: Using Boosted Regression Trees to Model Volunteer Labour Supply," Review of Economics, De Gruyter, vol. 67(3), pages 263-283, December.

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    Keywords

    Conditional mean and variance estimation; Filtered Historical Simulation; Functional Gradient Descent; Term structure; Multivariate CCC-GARCH models;
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