Matlab“索引超出矩阵尺寸。”

Question

I keep getting this error:

??? Index exceeds matrix dimensions.

Error in ==> example3_6 at 48 results=regress(cl1(trainset), cl2(trainset)); % use regression function

GDX file contains 7 columns and 386 rows, GLD file contains 7 columns and 765 rows, but if i'm taking a sample of 250 out of both this should not be a problem.

Can somebody advise what is the problem here?

Thank you

clear; % make sure previously defined variables are erased.

[num, txt]=xlsread('GLD'); % read a spreadsheet named "GLD.xls" into MATLAB. 

tday1=txt(2:end, 1); % the first column (starting from the second row) is the trading days in format mm/dd/yyyy.

tday1=datestr(datenum(tday1,'mm/dd/yyyy'), 'yyyymmdd');

tday1=str2double(cellstr(tday1)); % convert the date strings first into cell arrays and then into numeric format.

adjcls1=num(:, end); % the last column contains the adjusted close prices.

[num, txt]=xlsread('GDX'); % read a spreadsheet named "GDX.xls" into MATLAB. 

tday2=txt(2:end, 1); % the first column (starting from the second row) is the trading days in format mm/dd/yyyy.

tday2=datestr(datenum(tday2,'mm/dd/yyyy'), 'yyyymmdd');

tday2=str2double(cellstr(tday2)); % convert the date strings first into cell arrays and then into numeric format.

adjcls2=num(:, end); % the last column contains the adjusted close prices.

[tday, idx1, idx2]=intersect(tday1, tday2); % find the intersection of the two data sets, and sort them in ascending order

cl1=adjcls1(idx1); 

cl2=adjcls2(idx2);  

trainset=1:252; % define indices for training set

testset=trainset(end)+1:length(tday); % define indices for test set

% determines the hedge ratio on the trainset
results=ols(cl1(trainset), cl2(trainset)); % use regression function 
hedgeRatio=results.beta;

Answer 1

It looks like a problem with your call to the regress function.

According to doc regress:

regress(y,X) returns a p-by-1 vector b of coefficient estimates for a multilinear regression of the responses in y on the predictors in X. X is an n-by-p matrix of p predictors at each of n observations. y is an n-by-1 vector of observed responses.

But since we don't know how you use the regress function in your ols function, it's pretty hard to help you. Just run a size(...) check on the two arguments you send to regress() and I'm sure you will find out why MATLAB is complaining.