Basic Functions

This is a summary of a commonly used MATLAB functions, I suggest you to use MATLAB help in order to have the syntax complete view with all the possible option and features.


To find the size of a dimension of a matrix, use z=size(x,DIM), where DIM is the dimension.
Note that dimension 1 is the number of rows while dimension 2 is the number of columns,
so if x is T by K, z=size(x,1) returns T while z=size(x,2) returns K .
alternatively in a better way

>> [m,n] = size (x)

the command will return m = T and n = K.


This command find the size of the maximum dimension of x. YOU WILL NOT get the dimension (BE CAREFUL!).

>> z=length(x).

Note: If x is T by K , T > K , z = T . If K > T , z = K.


To compute the sum matrix,

use the command sum(x).

>> z = sum(x,dim)

dim as always the same meaning as above and in the all MATLAB enviroment.
If dim is equal 1, z will sum up by rows while if it is equal 2 by columns.


To find the minimum element of a vector or the rows of a matrix,

use the command min(x). If x is a vector, min(x) is scalar otherwise if it is a matrix use dim as usual.

>> z = min(x,dim)


It works as min function.

>> z = max(x,dim)


To sort the values of a vector or the rows of a matrix from smallest to largest, use the command sort(x). If x is a vector, sort(x) is vector where x(1)=min(x) and x(i) < x(i+1).
The complete command,

z = sort(x,dim,mode)

where mode is ‘ascend’ for the ascending order (default) or ‘descend’ for descending order.


To take the exponential of a vector or matrix (element by element),

use exp.


returns a vector or matrix the same size as x where z(i,j)=exp(x(i,j)).



returns a vector or matrix the same size as x where z(i,j)=log(x(i,j)).



returns a vector or matrix the same size as x where z(i,j)=sqrt(x(i,j)), you can obtain the same by z=x.^(1/2).



M = mean(A) returns the mean values of the elements along different dimensions of an array by default dim is 1.


To compute the sample variance of a vector or a matrix,

If x is a vector, var(x) is scalar. If x is a matrix, var(x) is a K by 1 vector containing the sample variances of each column.
Note: This command uses T – 1 in the denominator unless an optional second argument is used.

>> var(x)


To compute the sample covariance of a vector or matrix,

use the command

>> cov(x)

It works the same as covariance for the normalization T-1.


It is the equivalent of sqrt(var(x)),

>> std(x)


>> skewness(x)


>> kurtosis(x)

: operator

The : operator has two uses. One, to access elements in a matrix of vector (e.g. x(1,:)), has described before. The other is to create a row vector with evenly spaced points. We have the basic form, first:last and the other form first:increment:last. The first, first:last, produces a row vector of the form

[first, first + 1, . . . first + N]

where N is the largest integer such that first+N<= last

>> x = 1:10

x =

   1  2  3  4  5  6  7  8  9  10

>> x = 1:3.5

x =

  1  2  3

>> x = -5:4

x =

  -5  -4  -3  -2  -1  0  1  2  3  4

The second one, first:increment:last, include an increment (positive or negative),

[first, first + increment, first + 2(increment), .. first + N(increment)]

where N is the largest integer such that first+N(increment)<= last

>> x = -1:-1:-10

x =

   -1  -2  -3  -4  -5  -6  -7  -8  -9  -10

>> x = 0:.1:1

x =

    0.1000  0.2000  0.3000  0.4000  0.5000  0.6000  0.7000  0.8000  0.9000  1.0000

>> x = -1:.25:1

x =

   -1.0000   -0.7500   -0.5000   -0.2500         0    0.2500    0.5000    0.7500    1.0000


similar to : operator, rather than producing a row vector with a predetermined increment, it produce a row vector with a predetermined number of n nodes (including lower and upper bound).

>> linspace(0,1,5)

ans =

         0    0.2500    0.5000    0.7500    1.0000

>> linspace(0,1,6)

ans =

         0    0.2000    0.4000    0.6000    0.8000    1.0000


Y = diff(X) calculates differences between adjacent elements of X.

>> Y = diff(x,n,dim)

where n is the number of differentiations (1 by default) and dim as usual defines rows by 1 and columns by 2.
If it is a matrix is the same to compute[x(2:end,:)-x(1:end-1,:)] while if it is a vector [X(2)-X(1) X(3)-X(2) … X(n)-X(n-1)].

x = [1 2 3 4 5];
y = diff(x)
y =
     1     1     1     1

z = diff(x,2)
z =
     0     0     0


    • Load the “Price” matrix previously saved.
    • Create the “Return” matrix (approximate it by logarithmic difference)
    • Compute mean, covariance, skewness and kurtosis of “Return” matrix.
    • Compute the min, max of data returns.
    • Compute manually without using the specific functions, mean, covariance skewness and kurtosis.
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