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Exchange rate volatility and the mixture <strong>of</strong> distribution hypothesis 19<br />

Table 5 Regressions <strong>of</strong> log <strong>of</strong> realised NOK/EUR volatility<br />

(3) (14) (15)<br />

Est. Pval. Est. Pval. Est. Pval.<br />

Const. −1.012 0.00 −1.690 0.00 −1.916 0.02<br />

r<br />

vt−1 0.405 0.05 0.643 0.00 0.483 0.00<br />

r<br />

vt−2<br />

0.078 0.29 0.014 0.81 0.047 0.36<br />

r<br />

vt−3<br />

0.104 0.04 0.086 0.07 0.085 0.05<br />

r<br />

vt−4 −0.059 0.25 −0.065 0.16 −0.050 0.28<br />

r<br />

vt−5 0.122 0.00 0.087 0.03 0.069 0.08<br />

qt 0.139 0.03 0.173 0.02<br />

Δ qt 0.876 0.00 0.725 0.00<br />

r<br />

mt 0.194 0.00<br />

r<br />

ot −0.021 0.62<br />

xt r<br />

0.070 0.08<br />

ut r<br />

−0.007 0.85<br />

a<br />

ft −0.256 0.63<br />

b<br />

ft 1.403 0.00<br />

id 4.275 0.00 3.777 0.00 3.985 0.00<br />

sdt 0.659 0.00 0.382 0.00 0.532 0.00<br />

et−1 −0.130 0.53 −0.380 0.00 −0.238 0.03<br />

R 2<br />

0.53 0.57 0.60<br />

AR1−10 0.86 0.57 1.17 0.31 0.81 0.62<br />

ARCH1−10 0.44 0.93 1.34 0.20 1.15 0.32<br />

Het. 6.16 0.91 10.91 0.82 33.69 0.21<br />

Hetero. 30.94 0.27 50.15 0.24 128.63 0.10<br />

JB 3.15 0.52 0.44 0.80 0.47 0.79<br />

Obs. 568 568 568<br />

See Table 4 for details<br />

support the idea that exchange rate variability <strong>in</strong>creases with the number <strong>of</strong> <strong>in</strong>formation<br />

events, and the results suggest the impact is <strong>high</strong>er for weekly than for<br />

realised volatility. There might be a small caveat <strong>in</strong> the realised case though. The<br />

MA(1) term e t−1 is needed <strong>in</strong> Eqs. (14) and (15) <strong>in</strong> order to account for residual<br />

serial correlation at lag 1 <strong>in</strong>duced by the <strong>in</strong>clusion <strong>of</strong> Δq t. We have been unsuccessful<br />

so far <strong>in</strong> identify<strong>in</strong>g why Δq t <strong>in</strong>duces this serial correlation, and exclud<strong>in</strong>g<br />

Δq t from (15) also removes the signs <strong>of</strong> heteroscedasticity <strong>in</strong>dicated by<br />

White’s (1980) test with cross products <strong>in</strong> the sense that the p-value <strong>in</strong>creases from<br />

10% to 24%.<br />

2. Number <strong>of</strong> traders. The hypothesised effect <strong>of</strong> an <strong>in</strong>crease <strong>in</strong> the number <strong>of</strong><br />

traders as measured by qt is negative, but <strong>in</strong> all the four specifications <strong>in</strong> which it is<br />

<strong>in</strong>cluded it comes out positive. Moreover, it is significantly positive at 5% <strong>in</strong><br />

both realised specifications. Figure 3 aims at throw<strong>in</strong>g light on why we obta<strong>in</strong> these<br />

unanticipated results and conta<strong>in</strong>s recursive OLS estimates <strong>of</strong> the impact <strong>of</strong> q t with<br />

approximate 95% confidence bands. In the weekly case the value starts out negative,<br />

but then turns positive and stays so for the rest <strong>of</strong> the sample. However, it<br />

descends steadily towards the end. In the realised case, the value is positive all the

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