Realized Jump GARCH model: Can decomposition of volatility improve its forecasting?
|Author:||PhDr. Jiří Poláček|
|Year:||2015 - summer|
|Leaders:|| doc. PhDr. Jozef Baruník Ph.D.
|Work type:|| Doctoral
|Awards and prizes:|
|Abstract:||The present thesis focuses on exploration of the applicability of realized measures in volatility
modeling and forecasting. We provide a first comprehensive study of jump variation impact
on future volatility of Central and Eastern European stock markets. As a main workhorse, the
recently proposed Realized Jump GARCH model, which enables a study of the impact of
jump variation on future volatility forecasts, is used. In addition, we estimate Realized
GARCH and heterogeneous autoregressive (HAR) models using one-minute and five-minute
high frequency data. We find that jumps are important for future volatility, but only to a
limited extent due to the high level of information aggregation within the stock market index.
Moreover, Realized (Jump) GARCH models outperform the standard GARCH model in terms
of data fit and forecasting performance. Comparison of forecasts with HAR models reveals
that Realized (Jump) GARCH models capture higher portion of volatility variation.
Eventually, Realized Jump GARCH compared to other Realized GARCH models provides
comparable or even better forecasting performance.