Work detail

Forecasting realized volatility: Do jumps in prices matter?

Author: Mgr. Štefan Lipták
Year: 2012 - summer
Leaders: doc. PhDr. Jozef Baruník Ph.D.
Work type: Finance, Financial Markets and Banking
Language: English
Pages: 83
Awards and prizes: M.A. with distinction from the Dean of the Faculty of Social Sciences for an excellent state-final examination performance and for an extraordinarily good masters diploma thesis.
Abstract: This thesis uses Heterogeneous Autoregressive models of Realized Volatility on
ve-minute data of three of the most liquid nancial assets { S&P 500 Futures
index, Euro FX and Light Crude NYMEX. The main contribution lies in the
length of the datasets which span the time period of 25 years (13 years in
case of Euro FX). Our aim is to show that decomposing realized variance into
continuous and jump components improves the predicatability of RV also on
extremely long high frequency datasets. The main goal is to investigate the
dynamics of the HAR model parameters in time. Also, we examine if volatilities
of various assets behave di erently.
The results reveal that decomposing RV into its components indeed improves
the modeling and forecasting of volatility on all datasets. However, we
found that forecasts are best when based on short, 1-2 years, pre-forecast periods
due to high dynamics of HAR model's parameters in time. This dynamics
is revealed also by a year-by-year estimation on all datasets. Consequently,
we consider HAR models to be inapproppriate for modeling RV on such long
datasets as they are not able to capture the dynamics of RV. This was indicated
on all three datasets, thus, we conclude that volatility behaves similarly
for di erent types of assets with similar liquidity.
Downloadable: DT Lipták
June 2023


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