Abstract: This paper deals with heterogeneous autoregressive
models of realized volatility (HAR-RV models) on high-frequency
data of stock indices in the USA. Its aim is to capture the behavior of
three groups of market participants trading on a daily, weekly and
monthly basis and assess their role in predicting the daily realized
volatility. The benefits of this work lies mainly in the application of
heterogeneous autoregressive models of realized volatility on stock
indices in the USA with a special aim to analyze an impact of the
global financial crisis on applied models forecasting performance.
We use three data sets, the first one from the period before the global
financial crisis occurred in the years 2006-2007, the second one from
the period when the global financial crisis fully hit the U.S. financial
market in 2008-2009 years, and the last period was defined over
2010-2011 years. The model output indicates that estimated realized
volatility in the market is very much determined by daily traders and
in some cases excludes the impact of those market participants who
trade on monthly basis.