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What drives short rate dynamics? A functional gradient descent approach

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abstract Functional gradient descent, a recent technique coming from computational statistics, is applied to the estimation of the conditional moments of the short rate process with the goal of finding the main drivers of the drift and volatility dynamics. Functional gradient descent can improve the accuracy of some reasonable starting estimates obtained using classical short rate models introduced in the literature. It exploits the predictive information of an enlarged set of variables, including yields at other maturities, time, and macroeconomic indicators. Fitting this methodology to the time series of monthly US 3-month Treasury bill rates, we find that the drift dynamics react mostly in a non-linear way to changes in macroeconomic variables, whereas volatility dynamics are subjected to time-dependent regime-switches. Finally we show the superior performance of the final predictions obtained by applying functional gradient descent in a forecasting exercise.
   
type journal paper
   
keywords Functional gradient descent; Short rate process; Macroeconomic variables; Time-varying drift and volatility dynamics.
   
language English
kind of paper journal article
date of appearance 3-2012
journal Computational Economics
publisher Springer (US)
ISSN 0927-7099
ISSN (online) 1572-9974
DOI 10.1007/s10614-011-9310-y
volume of journal 39
number of issue 3
page(s) 315-335
review double-blind review
   
profile area SEPS - Quantitative Economic Methods
citation Audrino, F. (2012). What drives short rate dynamics? A functional gradient descent approach. Computational Economics, 39(3), 315-335, DOI:10.1007/s10614-011-9310-y.