Volatility at High Frequency
- Author(s): Whang, Duke
- Advisor(s): Ellickson, Bryan C
- et al.
The availability of software tools, high frequency data, and
recent advances in statistical inference all allow a greater study of
continuous-time models of price and volatility processes.
This research studies the structure of intraday stock volatility over
a selected group of stocks from 2007 to 2011. We use nearly every
valid transaction in the Trades and Quotes database to obtain a price
series which is sampled every second. We calculate realized variation
(RV), the sum of squared log returns, to estimate squared volatility.
We partition the trading day at the level of 100-second time
intervals, and we observe mean reversion in RV even at this time
scale. We estimate a modified Heston model for RV in which
statistical criteria are used to detect volatility jumps.