[R] Securities earning covariance

[Patrick Burns]

I would start by creating a matrix that held the
returns with rows being the dates and columns
being the securities.  You can do this by something
along the lines of:

days <- as.character(df[, 'DAY'])
sec <- as.character(df[, 'SEC_ID']
earningmat <- array(NA, c(length(unique(days)),
   length(unique(sec))), list(sort(unique(days)),
   unique(sec)))
submat <- cbind(match(days, rownames(earningmat)),
    match(sec, colnames(earningmat)))
earningmat[submat] <- as.numeric(as.character(df[, 'EARNING']))

Notice that while the 'as.numeric-as.character' in the last
line may not be needed -- if it is needed, it is needed in a big
way.  If the 'EARNING' column is a factor (because there
was at least one item that didn't appear to be numeric when
it was read in), then skipping the 'as.numeric-as.character'
call will put the codes for the factor into the matrix.  It will be
numeric as you expect, but complete garbage.

The trick with 'submat' is explained in any complete description
of subscripting -- the subscripting section of Chapter 1 of
S Poetry, for instance.

Once you have a suitable matrix, then you can use 'var' or
some other function to get the variance matrix.  Depending on
where you are going, a factor model variance may be better.
You can get 'factor.model.stat' from the public domain area
of the Burns Statistics website.  This is especially useful if there
are missing values in your matrix.


Patrick Burns
patrick at burns-stat.com
+44 (0)20 8525 0696
http://www.burns-stat.com
(home of S Poetry and "A Guide for the Unwilling S User")



ANGELO.LINARDI at bancaditalia.it wrote:
> Good morning,
>
> I am a new R user and I am trying to learn how to use it.
> I am trying to solve this problem.
> I have a dataframe df of daily securities (for a year) earnings as
> follows:
>
> SEC_ID		DAY		EARNING
> IT0000001	20070101	5.467
> IT0000001	20070102	5.456
> IT0000001	20070103	4.954
> IT0000001	20070104	3.456
> 	            ..........................
> IT0000002	20070101	1.456
> IT0000002	20070102	1.345
> IT0000002	20070103	1.233
> 		..........................
> IT0000003	20070101	0.345
> IT0000003	20070102	0.367
> IT0000003	20070103	0.319	
> 		..........................
>
> And so on: about 800 different SEC_ID and about 180000 rows.
> I have to calculate the "covariance" for each couple of securities x and
> y according to the formula:
>
> Cov(x,y) = (sum[(x-x')*(y-y')]/N)/(sx*sy)  
>
> being x' and y' the mean of securities earning in the year, N the number
> of observations, sx and sy the standard deviation of x and y.
> To do this I could build a df2 data frame like this:
>
> DAY		SEC_ID.x	SEC_ID.y	EARNING.x
> EARNING.y	x'	y'	sx	sy
> 20070101	IT0000001	IT0000002	5.467		1.456
> a	b	aa	bb
> 20070101	IT0000001	IT0000003	5.467		0.345
> a	c	aa	cc
> 20070101	IT0000002	IT0000003	1.456		0.345
> b	c	bb	cc
> 20070102	IT0000001	IT0000002	5.456		1.345
> a	b	aa	bb
> 20070102	IT0000001	IT0000003	5.456		0.367
> a	c	aa	cc
> 20070102	IT0000002	IT0000003	1.345		0.367
> b	c	bb	cc
> ........................................................................
> .......................................................
>
> (merging df with itself with a condition SEC_ID.x < SEC_ID.y) and then
> easily calculate the formula; but the dimensions are too big (the
> process stops whit an out-of-memory message). 
> Besides partitioning the input and using a loop, are there any smarter
> solutions (eventually using split and other ways of "subgroup merging"
> to solve the problem ?
> Are there any "shortcuts" using statistical built-in functions (e.g.
> cov, vcov) ?
> Thank you in advance
>
> Angelo Linardi
>
>
>
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