9944 Time Transformations , Intraday Data and Volatility Models
نویسنده
چکیده
In this paper, we focus on the trade and quote data for the IBM stock traded at the NYSE. We present two di erent frameworks 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 seasonality being accounted for. We also highlight the impact of characteristics of the trade process (traded volume, number of trades and average volume per trade) on the volatility speci cations. Secondly, we deal directly with the irregularly spaced data. We review two time transformations that allow a thinning of the original dataset such that new durations are de ned. The newly de ned price and volume durations are characterized and the performance of the Log-ACD model for modelling these durations is assessed. Moreover, price durations allow an easy computation of intraday volatility and this method compares favorably to ARCH estimations.
منابع مشابه
9944 Time Transformations , Intraday Data Andvolatility
In this paper, we focus on the trade and quote data for the IBM stock traded at the NYSE. We present two diierent frameworks 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 seasonality being accounted for. We also highlight the impact of ch...
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