
I have been working on my first paper with the title "Can
Power Law help us avoid highly improbable tail events?" the premise of the argument is that if some economic variables fall in the
Power Law distribution then policies associated with those economic variables need to be handled differently. For example, I am taking the problem of the
Icelandic Krona, I have
written about the
Icelandic Krona before and I am convinced that it follows a
Power Law distribution.
If we know that
ISK follows a power law distribution and future fluctuations in
ISK are going to follow the Power Law distribution should the Icelandic state still use the currency in its current form? or should it switch over to a more stable currency say the
Euro?
this debate is on-going in Iceland and I am afraid people are viewing this decision with their political lense and not the economic lense.
Just a simple switch in the
probability distribution of the
ISK has some serious policy implications. Lets do a simple thought experiment: The
Financial Supervisory Authority of Iceland used a 20%
devaluation of the ISK as a basis to stress test the banks in Iceland, why did they choose 20% because they thought ISK followed a
Gaussian Normal Distribution and a 20% devaluation is a
2 or 3 sigma event or a very low probability of occurance, however if we change the distribution to
Power Law a 20% devaluation of the
ISK has a much higher probability therefore they would have chosen a much higher deviation for the stress test say 50% this would have shown the true color of the Icelandic banks
capital adequacy and vulnerability to wholesale funding. The question is if the stress test had been done in
2006 when Iceland had a mini crisis, the
FSA could have put a stop to the aggressive borrowing by the Icelandic banks which in turn would have reduced the leverage of the companies in
Iceland and the citizens.
So choosing the right probability distribution matters! atleast that is the basis for my
PhD thesis lets hope the thesis could influence some policy choices.