Time Transformations, Intraday Data and Volatility Models.

In this paper, we focus on the trade and quote data for the IBM stock traded at the NYSE. We present two different framworks for analyzing this dataset. First, using regularly sampled observations, we characterize the intraday volatility of the mid-point of the bid-ask quotes by estimating GARCH and EGARCH models, with intraday seasonaity being accounted for. We also highlight the impact of characteristics of the trade process (traded voluame, number of trades and average volume per trade) on the volatility specifications. Secondly, we deal directly with the irregularity spaced data. We review two time transformations that allow a thinning of the original dataset such that new durations are defined.