GARCH modelling of individual stock data: The impact of censoring, firm size and trading volume

This paper explores the problems of testing and estimating GARCH models with particular emphasis on the impact of data censoring, firm size and trading volume. We conduct our investigation by analysing 1 year of daily returns data on 1014 Australian companies. Generally, our results indicate that GARCH model testing and estimation are impacted by the degree of censoring, firm size and trading volume. Specifically, our analysis produces three major findings. First, we find that low trading volume, small firm size and high censoring tend to be associated with a reduction in the presence of GARCH effects detected in the data by the LM test. Second, according to the estimation of a 'response surface' regression, the degree of censoring is found to be the dominant factor and stocks having a level of censoring less than 42.2% are predicted to have significant GARCH errors. Third, we find that low trading volume, small firm size and high censoring lead to a higher persistence of GARCH effects in the estimated models.