Selling Premiums On Stocks

[newbunch]

I figured it out. And it's entirely because of 1987. Using weekly 1987 data, I get a kurtosis of 3.3. If I change that week in 1987 from -12% to -22% (the daily loss), the kurtosis immediately jumps to 8.5.

Obviously, the daily data has a much higher kurtosis than the weekly data.

 

[Profitaker]

There ya go newbunch, curtesy of GBOS.

Looks like a very similar distribution to the DOW.

GBOS, what's that software you're using please ?

 

[newbunch]

Quote from gbos:

Out of curiosity i downloaded the s&p500 data from yahoo (^GSPC) symbol (1950 - today)
and the calculator gave these statistics

Can you do the same with the weekly data?

 

[Profitaker]

Quote from newbunch:

Can you do the same with the weekly data?

Why do you want to exclude 80% of the data ?

 

[Pa(b)st Prime]

There's been millions made by traders doing index-dispersion trades which are basically gamma index longs against a portfolio of short premium trades in the components......

 

[Profitaker]

Quote from Pa(b)st Prime:

There's been millions made by traders doing index-dispersion trades which are basically gamma index longs against a portfolio of short premium trades in the components......

That's actually a reverse dispersion / long correlation trade. You'd have to look at implied index correlation to see whether the conditions are right for that to be profitable now. When I last looked, it wasn't.

 

[newbunch]

Quote from Profitaker:

Why do you want to exclude 80% of the data ?

Why did you look at daily instead of hourly or minute by minute or tick by tick?

What data to use obviously depends on your frequency of trading....

 

[gbos]

Quote from newbunch:

Can you do the same with the weekly data?

Not directly comparable results in this case, you are filtering out information. The weekly sigma for example is sqrt(5) times the daily sigma and the calculation formula will be affected cause

kurt = E[((x-m)/sigma)^4]

In the weekly case the kurt is around 3.46 (6.46 if you prefer the notation of normal distribution having kurt = 3).

With monthly data the filtering is even more kurt = 2.51 (5.51 with the notation of normal distribution having kurt = 3).

So if you are affected by prices only once a month and don't have any consequences by in between fluctuations (margin calls etc.) then monthly or even better yearly prices aproach the normal distribution.

 

[gbos]

Quote from Profitaker:


GBOS, what's that software you're using please ?

Just some slow home made - basic language code - software.

 

[Pa(b)st Prime]

Thank's for the correction on terminology. I know it was big in the mid-late 90's.

Quote from Profitaker:

That's actually a reverse dispersion / long correlation trade. You'd have to look at implied index correlation to see whether the conditions are right for that to be profitable now. When I last looked, it wasn't.