MATLAB: Returns and standard deviation groupwise

distance to defaultgroupwisereturnsstandard deviation

I have a time series of daily stock prices for several firms. Now I want to calculate the daily returns and the rolling standard deviation of 250 days for each firm. I have no clue how to implement that in Matlab. Whereas in Stata I could only use the "by" command, it didnt find a similar function in Matlab.

I did group my firm ID and tried to apply splitapply. It works but what I get is a cell array. If I do not specify "{}" (cell array) in my function, this function wont work.

func =@(p){log(p(2:end)./p(1:end-1))} ;
r = splitapply(func,p,Entity);

The goal is to calculate daily returns (in an array) and annualized std. dev to further calculate the distance to default for each company.

Has someone a hint, how this might work? Thanks for any hint!

Best Answer

I'm not sure what you are asking by "daily returns". Perhaps you want a moving mean, on a 250 day time window. That is simple.

mean250 = conv(dailydata,ones(250)/250,'valid');

But a moving window standard deviation is also easy enough. Download my movingstd tool from the File Exchange. It uses convolution with a trick to get the std in a window, and do it efficiently.

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MATLAB: Group-wise mean in multi-dimentional array

"cell array of the dimension 1 x 121 x 515. It represents 515 sets of 1 x 121..."

First, why the complication of a cell array: convert to a 2D array of 515x121; then the manipulations are straightforward to average over groups of N (N divisible into M, the number of rows/columns comprising the sets dimension).

If the array were that 121x515, then it's (in steps to see the process) --

[r,c]=size(x);      % get the initial size (121x515 here, general case)
N=5;                % the number over which averaging desired
m=x.';              % transpose to put the grouping dimension by row
m=reshape(m,N,[]);  % get columns containing N sequential elements
m=mean(m);          % take average of those groups of N
m=reshape(m,c,[]);  % split back up the means by length of signal

m will now be same number of rows but averaged over N successive columns to reduce that by a factor of 1/N. And, of course, the above can all be strung together; it's just harder initially to read...

   m=reshape(mean(reshape(x.',N,[])),c,[]);

When writing such expressions, don't begin from the leftmost part trying to keep track of paren's, etc. Instead start from the inside out as in the above steps; when you have the first reshape() expression, then surround it by the next set of parens for the next outer function and so on until done...it's best to practice on these kinds of things with small datasets at the command line that are small enough to see easily the full array but aren't symmetric so can watch the shape changes. A good test case here would be something like

x=rand(4,9);
N=3;

which would be nine realizations of a very short spectrum of length four but the actual size is immaterial to the operations.

MATLAB: Group-wise mean in multi-dimensional array (part II)

This works, right?

M = rand(256,256,515); % Use your actual data here
M_r = reshape(M,256,256,103,5);
M_r_mean = mean(M_r,4);

Best Answer

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MATLAB: Group-wise mean in multi-dimentional array

MATLAB: Group-wise mean in multi-dimensional array (part II)

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