5
May 22

Vector autoregressions: preliminaries

Vector autoregressions: preliminaries

Suppose we are observing two stocks and their respective returns are x_{t},y_{t}. A vector autoregression for the pair x_{t},y_{t} is one way to take into account their interdependence. This theory is undeservedly omitted from the Guide by A. Patton.

Required minimum in matrix algebra

Matrix notation and summation are very simple.

Matrix multiplication is a little more complex. Make sure to read Global idea 2 and the compatibility rule.

The general approach to study matrices is to compare them to numbers. Here you see the first big No: matrices do not commute, that is, in general AB\neq BA.

The idea behind matrix inversion is pretty simple: we want an analog of the property a\times \frac{1}{a}=1 that holds for numbers.

Some facts about determinants have very complicated proofs and it is best to stay away from them. But a couple of ideas should be clear from the very beginning. Determinants are defined only for square matrices. The relationship of determinants to matrix invertibility explains the role of determinants. If A is square, it is invertible if and only if \det A\neq 0 (this is an equivalent of the condition a\neq 0 for numbers).

Here is an illustration of how determinants are used. Suppose we need to solve the equation AX=Y for X, where A and Y are known. Assuming that \det A\neq 0 we can premultiply the equation by A^{-1} to obtain A^{-1}AX=A^{-1}Y. (Because of lack of commutativity, we need to keep the order of the factors). Using intuitive properties A^{-1}A=I and IX=X we obtain the solution: X=A^{-1}Y. In particular, we see that if \det A\neq 0, then the equation AX=0 has a unique solution X=0.

Let A be a square matrix and let X,Y be two vectors. A,Y are assumed to be known and X is unknown. We want to check that X=\sum_{s=0}^{\infty }A^{s}Y\left( A^{T}\right) ^{s} solves the equation X-AXA^{T}=Y. (Note that for this equation the trick used to solve AX=Y does not work.) Just plug X:

\sum_{s=0}^{\infty }A^{s}Y\left( A^{T}\right) ^{s}-A\sum_{s=0}^{\infty }A^{s}Y\left( A^{T}\right) ^{s}A^{T} =Y+\sum_{s=1}^{\infty }A^{s}Y\left(A^{T}\right) ^{s}-\sum_{s=1}^{\infty }A^{s}Y\left( A^{T}\right) ^{s}=Y

(write out a couple of first terms in the sums if summation signs frighten you).

Transposition is a geometrically simple operation. We need only the property \left( AB\right) ^{T}=B^{T}A^{T}.

Variance and covariance

Property 1. Variance of a random vector X and covariance of two random vectors X,Y are defined by

V\left( X\right) =E\left( X-EX\right) \left( X-EX\right) ^{T}, Cov\left(  X,Y\right) =E\left( X-EX\right) \left( Y-EY\right) ^{T},

respectively.

Note that when EX=0, variance becomes

V\left( X\right) =EXX^{T}=\left(  \begin{array}{ccc}EX_{1}^{2} & ... & EX_{1}X_{n} \\  ... & ... & ... \\  EX_{1}X_{n} & ... & EX_{n}^{2}\end{array}\right) .

Property 2. Let X,Y be random vectors and suppose A,B are constant matrices. We want an analog of V\left( aX+bY\right) =a^{2}V\left( X\right) +2abcov\left( X,Y\right) +b^{2}V\left( X\right) . In the next calculation we have to remember that the multiplication order cannot be changed.

V\left( AX+BY\right) =E\left[ AX+BY-E\left( AX+BY\right) \right] \left[  AX+BY-E\left( AX+BY\right) \right] ^{T}

=E\left[ A\left( X-EX\right) +B\left( Y-EY\right) \right] \left[ A\left(  X-EX\right) +B\left( Y-EY\right) \right] ^{T}

=E\left[ A\left( X-EX\right) \right] \left[ A\left( X-EX\right) \right]  ^{T}+E\left[ B\left( Y-EY\right) \right] \left[ A\left( X-EX\right) \right]  ^{T}

+E\left[ A\left( X-EX\right) \right] \left[ B\left( Y-EY\right) \right]  ^{T}+E\left[ B\left( Y-EY\right) \right] \left[ B\left( Y-EY\right) \right]  ^{T}

(applying \left( AB\right) ^{T}=B^{T}A^{T})

=AE\left( X-EX\right) \left( X-EX\right) ^{T}A^{T}+BE\left( Y-EY\right)  \left( X-EX\right) ^{T}A^{T}

+AE\left( X-EX\right) \left( Y-EY\right) ^{T}B^{T}+BE\left( Y-EY\right)  \left( Y-EY\right) ^{T}B^{T}

=AV\left( X\right) A^{T}+BCov\left( Y,X\right)  A^{T}+ACov(X,Y)B^{T}+BV\left( Y\right) B^{T}.

 

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