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Accurate Short-Term Yield Curve Forecasting using Functional Gradient Descent

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Author Info
Francesco Audrino ()
Fabio Trojani ()

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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.

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File URL: http://www.vwa.unisg.ch/RePEc/usg/dp2007/DP-24-Au.pdf
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Publisher Info
Paper 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.

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Length: 51 pages
Date of creation: Jun 2007
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Handle: RePEc:usg:dp2007:2007-24

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

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  1. Francis X. Diebold & Monika Piazzesi & Glenn D. Rudebusch, 2005. "Modeling bond yields in finance and macroeconomics," Working Paper Series 2005-04, Federal Reserve Bank of San Francisco. [Downloadable!]
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  2. Audrino, Francesco & Barone-Adesi, Giovanni, 2005. "Functional gradient descent for financial time series with an application to the measurement of market risk," Journal of Banking & Finance, Elsevier, vol. 29(4), pages 959-977, April. [Downloadable!] (restricted)
  3. Diebold, Francis X. & Li, Canlin, 2006. "Forecasting the term structure of government bond yields," Journal of Econometrics, Elsevier, vol. 130(2), pages 337-364, February. [Downloadable!] (restricted)
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  4. Mancini, Loriano & Ronchetti, Elvezio & Trojani, Fabio, 2005. "Optimal Conditionally Unbiased Bounded-Influence Inference in Dynamic Location and Scale Models," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 628-641, June. [Downloadable!] (restricted)
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  5. Gallant, A.R. & Tauchen, G., 1988. "Seminonparametric Estimation Of Conditionally Constrained Heterogeneous Processes: Asset Pricing Applications," Papers 88-59, Chicago - Graduate School of Business.
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  6. Francesco Audrino & Giovanni Barone-Adesi, 2005. "A multivariate FGD technique to improve VaR computation in equity markets," Computational Management Science, Springer, vol. 2(2), pages 87-106, 03. [Downloadable!] (restricted)
  7. Bollerslev, Tim, 1990. "Modelling the Coherence in Short-run Nominal Exchange Rates: A Multivariate Generalized ARCH Model," The Review of Economics and Statistics, MIT Press, vol. 72(3), pages 498-505, August. [Downloadable!] (restricted)
  8. Kiefer, Nicholas M, 1988. "Economic Duration Data and Hazard Functions," Journal of Economic Literature, American Economic Association, vol. 26(2), pages 646-79, June. [Downloadable!] (restricted)
  9. Andrew Ang & Monika Piazzesi, 2001. "A No-Arbitrage Vector Autoregression of Term Structure Dynamics with Macroeconomic and Latent Variables," NBER Working Papers 8363, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
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