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| Author: | Giot, P. |
| Title: | Time transformations, intraday data, and volatility models |
| Journal: | Journal of Computational Finance
2001 : WINTER, VOL. 4:2, p. 31-62 |
| Index terms: | Volatility Stock exchanges Econometric models USA |
| Language: | eng |
| Abstract: | The article focuses on trade and quote data for IBM stock traded on the New York Stock Exchange. Two different frameworks for analyzing this dataset are presented. First, using regularly sampled observations, the intraday volatility of the midpoint of the bid-ask quotes is characterized by estimating GARCH and EGARCH models, with intraday seasonality being accounted for. Second, irregularly spaced data are dealt with rirectly. Two time transformations are reviewed that allow a thinning of the original dataset such that new durations are defined. An application to intraday value-at-risk is presented in which both types of models are used to forecast the one-step-ahead value-at-risk. |
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