[R-SIG-Finance] Modeling sharp drops in volatility
s.n.stoyanov at gmail.com
Mon Aug 6 16:11:22 CEST 2012
This is less of a programming question and more of a theoretical issue, but
hopefully someone can help. How would you suggest going about modeling a
sharp drop in volatility using a univariate GARCH model? What I am trying to
capture is the overstatement of volatility forecasts that occurs after
earnings announcements. As you surely know, the memory of GARCH models
incorporates recent (and not so recent) swings in volatility into the
variance of the stock price process. As a result, in the periods following
an earnings event volatility forecasts will be severely overstated.
I have been able to predict future increases in volatility owed to an
earnings event, through a binary event day dummy. However, the effects of
such single big moves persist in my process much longer than they should.
Instead of having my GARCH model take an isolated big move as significant
information affecting volatility in future periods, I am trying to model a
very sharp reversal back to levels observed before the earnings event.
I guess this is to be expected with GARCH models, but I am wondering if
there is a common technique to deal with this.
I am using the rugarch package to do build my model.
Thanks in advance for any suggestions.
The University of Chicago Booth School of Business
MBA Class of 2013
(312) 532-0120 | stoyanov at chicagobooth.edu
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