I've been going back and forth with how I should work to find an event effect. would be so grateful for some clarification.
I have daily time series of exchange rates for different countries ( 1 for each country). the period is from jan1, 2018 to dec 31, 2019. I want to know if a certain event that had multiple announcements during this period had an effect on each of the countries. ( I am taking the news that happened during this period 65 in total) and I want to analyse if the event had an effect on each country's exchange rate. this is the market model and my variables would be ER= (dependent)exchange rate, D= dummy, SP= stock market return
my question is, should i have 1 dummy variable for the whole regression with all 1 when a news happen and zeros when it did not and analyse that, as in my data frame would look like
and so on for 546 observations( business days) which contains 65 dummys. so total would be 481 observations with 0 and 65observations with a 1 during this period of jan1, 2018 to dec312019 when there was a news announcement.
I should then just run an OLS regression of the market model and the dummy=1 would tell me the effect of the event on the FX.
or should I have had to reduce the time window, to, for example, 6 months and analysed each news announcement separately, of course now focusing on less news announcement ( 1 every 6 months), and adding a dummy=1 to days around the event date (-1,+1). so in a 6months daily observations, i would only have 3 dummys=1 everything else would be zero for example:
or am i completely wrong with my both approaches?
thank you so much!
