Page 46 - Contributed Paper Session (CPS) - Volume 8

CPS2176 Chiraz KARAMTI et al.
Tableau 1. Parameter estimates of wavelet based EGARCH (1,1) models
Full sample Before Brexit After Brexit
α γ β α γ β α γ β
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0.0017 0.0212 0.3253 -0.0048 0.0408 0.0203 -0.0422 0.0110 0.0165
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0.0136 0.0235 0.2020 0.0818 -0.0047 0.0645 -0.0115 0.0360 0.1916
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0.1719 0.0587 0.9264 0.0401 0.0140 0.0924 -0.0007 0.0121 0.0613
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0.2768 -0.0001 0.9834 0.2798 -0.0004 0.9807 0.2758 0.0001 0.9741
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0.2743 0.0011 0.9783 0.2936 -0.0012 0.9737 0.2686 0.0018 0.9696
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0.2580 0.0004 0.9596 0.2058 0.0005 0.9656 0.2721 8.15E-05 0.9582
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0.1156 -0.0061 0.3188 0.1937 -0.0004 0.9721 0.1866 -0.00003 0.9625

Although every uncertainty may not cause volatility, uncertainty about
major events can result in volatile market. In our case, the results indicate that
the effect of previous volatilities is high and stable before and after Brexit over
the medium and long term. However, Brexit appears to have a significant
negative impact on the currency market volatility and shows that the volatility
of returns is higher before the Brexit and decreases at the post-Brexit period.
Lots of uncertainty before the Brexit brought this increase in the volatility and
realizing the outcome of the referendum and cutting the interest rate by the
central bank resulted in investors asking less risk premium which led to the
decrease in volatility. Before major events like Brexit, investors lose their trust
to the central bank to be able to have policies that positively interfere with the
market. As the event passes, the realization of the outcome appears to reduce
the need for the risk premiums to be introduced to the market thus the implied
volatility is reduced. Furthermore, the asymmetric effect in most wavelets after
Brexit is positive and small indicating an unfavorable asymmetric reaction to
good news increasing volatility more than bad news. However, this effect
disappears in the medium to long run (sacles 4-7). The most striking evidence
from the above results is that no general pattern can be found since volatility
at each scale have its own dynamics. However,
these results in general indicate the dominance of low frequency elements
(high scales) in the exchange rates market.
The Akaike Information Criterion (AIC) is used to decide the best fitting
model for each MOWT exchange series and the values are presented in Table
3. The forecasting ability of EGARCH models for different scales was judged

results based on applying the iterative Box-Jenkins procedure, are not reported, only the conditional
variance part is presented.
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