Forecasting Term Structure of Government Bonds Using High Frequency Data
|Author:||Mgr. Jakub Kožíšek|
|Year:||2018 - winter|
|Leaders:|| doc. PhDr. Jozef Baruník Ph.D.
|Work type:|| Economic Theory
|Awards and prizes:|
|Abstract:||This thesis investigates the use of realized volatility features from high frequency data in combination
with neural networks to improve forecasts of the yield curve of government bonds. I
use high frequency data on futures of four U.S. Treasury securities to estimate the Nelson-Siegel
yield curve and realized variance of its parameters over the period of 25 years. The estimated
parameters are used in prediction of the level, slope and curvature of the yield curve using an
LSTM neural network and compared to the Dynamic Nelson-Siegel model. Results show that
the use of realized variance and neural network outperforms autoregressive methods in prediction
of the level and curvature in daily and monthly forecasts. The yield curve of government bonds
itself has a predictive power on multiple macroeconomic variables, therefore improvements in its
forecastability may have broader implications on forecasting the overall state of the economy.