Tables 4 and 5 shows the copula correlation matrix which is responsible for the part of the contemporeneous and lagged cross-correlation, between individual banks’ quoting activity, which does not go through the time-varying mean. Cross-correlation varies between 0.04 and 0.48. This means that contemporeneous effect between different banks depends on the importance of influence of some banks’ quoting activity on others.
Estimation results are presented in Tables 6 and 7. There is evidence of diurnal seasonality in the activity of all banks except three (see end of Section 3). The three pairs of trigonometric function at the daily, half-daily and hourly frequency are always jointly significant. The effect of news announcements is generally significant for all banks, as can be seen from a Wald test of the joint significance of all announcement. What the dummy variables results of individual banks show clearly, is that their reaction to the same news announcements are different. There is variation across banks, both in whether or not they react to a certain category of news and in the way they react to it, by increasing or decreasing their activity. Again, the use of the double Poisson is justified by the fact that we have estimated both overdispersed distributions (the majority of them) and some underdispersed distributions.
The variance of the standardized residuals is within a few percent of one for nearly all banks, except OKOH, which means that the dispersion is well captured. Upon closer inspection of its time series, we can see that there seems to be a change of regime in OKOH, which went from heavy quoting to lower levels of activity after October 8, 2001. The autocorrelations of the standardized residuals of BARL and UBSZ, shown as representative example in Figure 2, are often in the confidence band. However, some autocorrelations are a little outside of the bands, resulting in significant Q-statistics (the sample size is large). Nevertheless, the Q-statistics are very strongly reduced, compared to the raw data, even though they are still significant.
Another way of testing the specification is to look at the density forecast tools. The probability integral transformation Z (PIT) of the data under the estimated distributions should be uncorrelated and uniformly distributed. Figure 3 shows the quantile plot of Z, which is very close to the 45-degree line for six banks, shown as examples. For both samples of banks, it seems that at least one type of scheduled news event has an impact on every bank, except for OHVA. Positive and negative surprises in U.S. and European figures (respectively η1, η2 and η3) seem to have the most important effects. This is in line with the findings of Andersen, Bollerslev, Diebold, and Vega (2002), that macroeconomic surprises have the most significant impact on the level of the exchange rate.
According to Evans (2002), these types of announcements are therefore NCK news, as they impact order flow. Given that they are simultaneously received by all dealers, it has to be the case that they are interpreted differently. A lot of banks react to the first three scheduled news announcements. In particular SGOX and DREF increase their activity as a response to US and European macroeconomic figures, RABO and OKOH decrease it as a response to US figures but increase it to react to European figures. On the other hand, banks like BHFX, RABO and SHKH reduce their quoting in response to US figures. However, speeches of senior officials of the government ( η4) seem to pertain to the category of CK news, given that this variable is not significant for any bank. It could of course also be that this variable simply does not have any informational content, as perceived by foreign exchange dealers, but Bauwens, Ben Omrane, and Giot (2003) find that it has an impact on volatility, which is significant at the 1% level.
The remaining unscheduled news (respectively η6, η7, η8 and η9) hardly affects banks’ quoting activity and we can thus consider them as CK news, unless markets don’t regard them to be very informative at all. Table 8 shows for every type of announcement the result of a Wald test of the null hypothesis that the announcement impacts all banks in the same way. The results show that US and European macroeconomic figures affect banks differently in both samples, whereas interest rate reports are only significantly different in sample 2. The remaining announcements have impacts on different banks that are not significantly different, which is not a surprise since the latter announcements are much less significant in general.
In addition, we estimate a restricted model, DACP model i.e. equation (4.4) when αi,j= 0 for i ≠ j, on the same banks’ quoting activity. We find almost the same estimated dummy coefficients as for the general model, MDACP (results are not reported). Thus, we estimate a DACP on aggregate quoting activity, as well as those of the remaining banks (respectively ”Aggregate” and ”Rest” in Tables 6 and 7), adopting the same seasonality variables, an ARMA(1,1) structure and the same samples of banks, in order to compare the obtained results with those generated by MDACP.
We find, for instance in the case of positive US macroeconomic figures, that there are both increases and decreases in quoting activity of individual dealers. These effects offset each other, which reduces the significance of news on quoting activity at the aggregate level. Another example is European and US interest rate reports, which are significant for three banks of sample 2, but not at the aggregate level. However, in the case of negative US and European macroeconomic figures, there are both increases and decreases in activity, but the increases seem to dominate at the aggregate level.
This is strong evidence that aggregate analysis of quoting activity can miss the fact that individual banks have different reactions. In some cases, even though there is no aggregate impact of news on quoting activity, individual banks do respond, but their responses can offset each other, and in other cases, a positive coefficient at the aggregate level can conceal a less unified picture at the level of individual dealers.
Finally and with regard to dealers interaction, the results in Tables 6 and 7 show that banks’ dealers react to each other. The quoting activity of each dealer increases or decreases in response to the lagged activity of some other dealers. Although, there are some banks that do not influence the quoting activity of other banks. In sample 1 each bank quoting activity is sensitive to at least one other bank quotes.
In sample 2, however, at least three bank quotes have a significant impact on each dealers’ quotes. This supports the hypothesis according to which some dealers observe the frequency of price revision of some influent dealers’ quoting activity to infer useful information. Consequently, the results related to dealers’ quoting activity sensitivity to both news announcements and quoting activity of some other dealers, confirm the general hypothesis according to which quoting activity provides an important informative signal. Indeed, during event periods, dealers monitor quoting activity of some others in order to infer their manner of reaction to news announcements before their immediate or afterward react.
By Dr W. B. Omrane and A. Heinen
Next: Conclusion
Summary: Index