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Forecasting the U.S. Treasury Yield Curve using Targeted Diffusion Indices

Piene, Fredrik Bergh; Vedvik, Jan Ove
Master thesis
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2605524.pdf (2.510Mb)
DataBonds5&USTresData.xlsx (1.762Mb)
ThesisCodeFinal.m (132.4Kb)
VariableMasterfileRecursive3.xlsx (3.696Mb)
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https://hdl.handle.net/11250/2686980
Utgivelsesdato
2020
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Samlinger
  • Master of Science [963]
Sammendrag
We investigate possible empirical linkages between variation in the U.S. Treasury

yield curve and several measures of economic and financial activity by the

methodology targeted diffusion index forecasting. First, we model the entire yield

curve with the Nelson-Siegel exponential components framework period-by-period,

thereby distilling the yield curve into three, dynamic parameters. We show that

these three parameters can be interpreted as yield curve factors corresponding to

level, slope and curvature, and that their variation explain almost all yield curve

variation. We then use targeted diffusion indices estimated from a set of 1196 different

macroeconomic and financial variables to produce both in-sample and out-ofsample

forecasts these three parameters, thus obtaining forecasts of the the entire

yield curve. While we do find in-sample predictability of the Nelson-Siegel dynamic

paramaters by the targeted diffusion indices, we do not find that they are able to

produce better out-of-sample forecasts than the competitor models. Additionally,

we find that the established Diebold-Li yield curve forecasting model, which has

previously been found to produce superior forecasts, is outperformed by a simple

random walk model. Our findings on a new, updated sample thus contradict earlier

findings.
Beskrivelse
Masteroppgave(MSc) in Master of Science in Business, Finance - Handelshøyskolen BI, 2020
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Handelshøyskolen BI

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