We work with a tick-by-tick data set bought from Olsen & Associates for the period May 14 to October 26, 2001. The data comes from different quoting systems. From May 14 until September 10, 2001, the data comes from Reuters, and from August 24 until October 26, 2001, it comes from Tenfore Systems. We take into consideration two electronic screens to eliminate the quoting activity bias shown by Goodhart and Demos (1991) (i.e. all dealers do not conduct their quoting through only one electronic screen, but they choose different ones). We therefore work with two samples of banks during different periods. We selected the most active banks in our sample.
Tables 1 and 2 show, that for the first sample, the 4 banks we select cover about 24:4% of the overall quotes, whereas the 6 banks of the second sample post about 45% of the total number of quotes in the sample. The reason why we focus attention on these banks is that they are the most active dealers in our data and the remaining quotes are posted by a very large number of dealers with a very small contribution. Our first sample of banks contains BG Bank, Copenhagen (BGFX), Berliner Handels- und Frankfurter Bank, Frankfurt (BHFX), Rabobank, London (RABO), Soci´et´e G´en´erale, Paris (SGOX) for the period May 14 to September 10, 2001. This corresponds to 9396 5-minute observations. The second sample of banks includes Barclay’s Bank, London (BARL), Dresdner Bank, Frankfurt (DREF), Oolder & de Jong, Amsterdam (OHVA), Oko Bank, Helsinki (OKOH), SHK Bank, Hong Kong (SHKH) and Union Bank of Switzerland, Zurich (UBSZ), for the period August 24 to October 26 2001, for a total of 4968 5-minute quoting intervals. Descriptive statistics for the first sample are shown in Table 1 and in Table 2 for the second sample.
The minimum number of quotes is zero and the mean is generally quite small, which justifies the use of discrete distributions like the Poisson. Moreover, most series are overdispersed (meaning that the variance is larger than the mean), with the exception of BHFX, RABO and OHVA, which are underdispersed. This justifies the use of the double Poisson distribution, since, unlike other count distributions, it allows for both over- and underdispersion. We use the same news announcements as in Bauwens, Ben Omrane, and Giot (2003) and we test the impact of nine categories of news. News announcements, shown in Table 3 are classified into two groups, scheduled and unscheduled announcements.
The first group contains US macroeconomic figures, more specifically employment reports, producer and consumer price indices, gross domestic product and other important figures. This group also includes European macroeconomic figures, scheduled speeches of senior officials of the government and of public agencies, such as the president of the Federal Reserve, the European Central Bank and the economy and finance ministers, and US and European interest rate reports. The second group contains forecasts of key institutes and specialized organizations, such as the IMF, the World Bank, and the IFO institute (an influential service-based research organization in Germany). This group also contains declarations of OPEC members, rumors of Central Bank intervention and other extraordinary events (natural disasters, wars, terrorist attacks, etc.).
To highlight the effect of the possible ‘surprise’ contained in the scheduled US macroeconomic figures, we distinguish so-called positive from negative news by computing the difference between the expected and realized values. If the realization is larger than the expectation and it is a figure which contributes to the growth of the economy, the news is classified as positive. If the actual figure indicates worse-than-expected inflation or a slowdown of the economy, it is regarded as negative. This methodology is also used in Andersen, Bollerslev, Diebold, and Vega (2002), who test the effect of non-anticipated news announcements on currency returns. They conclude that unanticipated events lead to jumps in the conditional mean of currency returns and that negative news have a greater impact than positive news.
As can be seen from Table 3, the total number of news announcements in the first sample is 377, the most frequent type of news event is European macroeconomic figures with 105 events, but there are only 3 occurrences of rumors of central bank interventions. In the second sample, there are 251 events, with 53 speeches of government officials and only 3 rumors of central bank intervention. We compute averages of the quoting activity over 5-minute intervals for all banks and divide them by their cross-sectional average in order to make them comparable across banks. The seasonal patterns are shown in Figure 1. First of all, we note that the seasonality of the banks in the sample is not the same for all, which is not surprising. BGFX, SGOX, BARL, DREF, SHKH, and UBSZ all start with a small decrease in the morning until 10 AM GMT, and after that quoting activity starts increasing from around 12 PM GMT, which corresponds to the morning on the East Coast of the US, to a peak around 2 or 3 PM GMT, and then the activity decreases until 5 PM GMT, when European offices start to close. SHKH is somewhat different, as it starts the day with an increase, but then its pattern is similar to the one of the other banks.
DREF is different from other banks, in that it starts closing earlier, which means that its quoting activity decreases sharply shortly before 4 PM GMT. A similar pattern is observed for other banks, but between 6 and 7 PM for most of them, which is why we chose to stop our sample at 5 PM. The remaining banks (BHFX, RABO, OHVA and OKOH) do not seem to exhibit any particular seasonality over our sample period. This is confirmed for RABO, OHVA and OKOH by the Wald test for joint significance of the seasonality variables shown in Tables 6 and 7 (see Section 4.2 for more details). Furthermore we note that DREF has a particular pattern of diurnal seasonality, with very important spikes on or around the hour.
By Dr W. B. Omrane and A. Heinen
Next: Models and Results
Summary: Index