Audrino, Francesco Trojani, Fabio 2007 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. application/pdf https://n2t.net/ark:/12658/srd1318086 https://susi.usi.ch/global/documents/318086 https://susi.usi.ch/documents/318086/files/trojani_JFE_2007.pdf eng http://proquest.umi.com/pqdweb?did=1674283231&sid=3&Fmt=2&clientId=65081&RQT=309&VName=PQD info:eu-repo/semantics/altIdentifier/ark/12658/srd1318086 info:eu-repo/semantics/openAccess License undefined Journal of financial econometrics. - Oxford Publishing Limited. - 2007, vol. 5, no. 4, p. 591–623 Conditional mean and variance estimation filtered historical simulation functional gradient descent term structure multivariate CCC-GARCH models info:eu-repo/classification/udc/33 Accurate short-term yield curve forecasting using functional gradient descent http://purl.org/coar/resource_type/c_6501