Discretized Time and Conditional Duration Modelling for Stock Transaction Data
The paper considers conditional duration models in which durations are in continuous time but measured in grouped or discretized form. This feature of recorded durations in combination with a frequently traded stock is expected to negatively influence the performance of conventional estimators. A few estimators that account for the discreteness are discussed and compared in a Monte Carlo experiment. An EM-algorithm accounting for the discrete data performs better than those which do not. Empirical results are reported for trading durations in Ericsson B at Stockholmsbörsen for a three-week period of July 2002. The incorporation of level variables for past trading is rejected in favour of change variables. This enables an interpretation in terms of news effects. No evidence of asymmetric responses to news about prices and spreads is found.
|Date of creation:||20 May 2003|
|Date of revision:|
|Publication status:||Published in Applied Financial Economics, 2007, pages 647-658.|
|Contact details of provider:|| Postal: Department of Economics, Umeå University, S-901 87 Umeå, Sweden|
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- Lee, Lung-Fei, 1997.
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- Robert F. Engle & Jeffrey R. Russell, 1998. "Autoregressive Conditional Duration: A New Model for Irregularly Spaced Transaction Data," Econometrica, Econometric Society, vol. 66(5), pages 1127-1162, September.
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