4

For a very short answer, given that the event is scheduled, the implied vol for a fixed future expiry date decreases, and the historical volality increases at event time. This could seem a bit counterintuitive but the implied vol factors in all scheduled forthoming events up to expiry. As the event has hit the market and its impact is priced into the ...


2

As far as your second model concerned: Abnormal returns for good news is $\beta_4$ The t-value of 3 tells it is significantly different from 0 The model does not account for effect of bad news so the effect of bad news will mostly be found in spikes in residuals around time of bad news releases. $\beta_0$ is return when all other factors in the model (...


2

I just found this free simple API, returning today's earnings by default and symbols with earnings call if you pass date: https://api.earningscalendar.net/ or https://api.earningscalendar.net/?date=20190925


2

I can not see why you can't use more than one event per country. As long as they are independent of each other, you can easily group them without adjusting for cross correlation. Also, if they don't overlap on the estimation period as well.


2

We do exactly that in our paper on FX returns before monetary policy announcements (link): there are several countries, and in each country there are several announcements per year, so the event matrix (in the time/country dimensions) looks like a punched card. In short, you do compute the CARs for each country-event, then compute the average CAR across ...


1

As part of your analysis, it is always a good idea to do something simple before pulling out the big guns. So, OLS by country with perhaps a handful of controls would be a good benchmark, if only to tell later if your complicated ideas don't amount to squashing a fly with a sledge hammer. As for the panel idea, you have to think about how you'd be pooling ...


1

Looking at Markit spreads, when both USD and EUR spreads are published, then usually (not always) either they are exactly the same, or differ only by 1-2%. I think this means that many contributors just submit the same numbers for USD and EUR spreads. However for a few names the spreads differ a lot, most notably EUR-denominated protection on Italy ...


1

If I understand it rightly, I have to take the mean of the abnormal returns for the 50 companes at $t_0$ as the numerator. for example: $\bar A_{t} \ = \ \frac{1}{N_t}\sum_{i=1}^{n_t}A_{i,t'} $ where $N_t \ = \ 50 \ and \ t \ = \ t_0$ That's absolutely right. $\bar{A_{t}}$ is the cross-sectional average of abnormal returns on day $t$. But how to ...


1

This paper illustrates your problem. Basically, it depends on the volatility of prices. S. P Khotari and Jerold B. Warner : “Econometrics of Event Studies” (2006) https://www.bu.edu/econ/files/2011/01/KothariWarner2.pdf


1

You just have to treat every event as day zero and look at the other days relative to the event day. So the estimation period will be something like days -260 to -11. After that you can calculate tej abnormal returns around the event date. Then, just take for example every day 1 abnormal return (5 tines 300 in your example) and test them against zero or ...


1

"Size adjusted return for company X" on day t is defined as the return of company X on day t minus the equal weighted return of all stocks in the same size decile as company X. So for example if company X is in the third NYSE size decile, you average together the returns of all third size decile companies (whether NYSE or not) on day t and you subtract this ...


1

Summarizing the discussion in the comments: BHAR are computed using simple returns, not logarithmic returns.


1

For the t ratio, you should re-parameterise your equation so that $(β_0+β_4)$ is treated as one coefficient, say $\gamma$ or a coeff of a single variable. you cannot use one's t ratio for making inference on the other. $β_0$ is the average return on this stock, the coef on dummy variable absorbs the abnormal returns. You could do this: $$R_t=\beta_0+\beta_{0}...


1

It is the same. With enough data, you could not reject the null γ1=β2. You could test that with simulation. See this with R: ## set.seed(12456) ns=500 t=1:ns D[]=0 D[t>.1*ns&t<.33*ns]=1 rm=rnorm(ns,.01,1.5) ri=0.01+1.2*rm+.15*D+rnorm(ns,0,.5) plot(ri~rm,col=D+2) #Model 1 summary(lm(ri~rm+D)) #Model 2 (m1=lm(ri~rm)) res=resid(m1) summary(lm(res~...


1

The rating downgrade/upgrade effect is definitely more extreme during financial crisis, because of several effects (among all, flight-to quality, flight-to-liquidity and news effects itself), as shown by: Arezki, Rabah, Bertrand Candelon, and Amadou Nicolas Racine Sy. "Sovereign rating news and financial markets spillovers: Evidence from the European debt ...


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