Proving Nonnegativity of a Markowitz Portfolio Determinant
Summary
The document proves that a two-by-two determinant arising in the Markowitz portfolio allocation derivation is nonnegative when the covariance matrix is symmetric and positive definite. It rewrites the determinant using the inverse covariance matrix, then factors that matrix through its symmetric square root. This turns the expression into the difference between a product of squared vector lengths and the square of an inner product.
The Cauchy–Schwarz inequality establishes that this difference is at least zero. The proof therefore does not require all expected returns to differ. It establishes nonnegativity, rather than strict positivity: equality can occur when the transformed expected-return vector and transformed all-ones vector are linearly dependent. The argument assumes an invertible positive definite covariance matrix and real-valued vectors; it does not discuss singular covariance matrices or other portfolio constraints.
Key ideas
- A positive definite covariance matrix has a symmetric square root, as does its inverse.
- The determinant can be rewritten as an inner-product expression using the inverse covariance matrix.
- Applying the square root transforms the expression into the Cauchy–Schwarz inequality.
- The result is nonnegativity, with equality possible when the transformed vectors are linearly dependent.
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# Prove that a determinant in markowitz method derivation is greater than zero
# Prove that a determinant in markowitz method derivation is greater than zero
I want to prove that the following determinant, that appears in the markowitz method of portfolio allocation is greater than zero. ($\mu$ is the vector of returns and $\sum$ is the covariance matrix)
## Answer by FinanceGuyThatCantCode (score 2, accepted)
https://quant.stackexchange.com/a/33127
The comments above re all the entries of $\mu$ not being the same is true, but can be removed if you make the 2x2 determinant in question $\ge 0$ instead of $> 0$. The commenters know this of course.
The answer to your question can be obtained by an application of the Cauchy-Schwartz inequality along with knowledge that a symmetric positive definite matrix has a square root.
Since $\Sigma^{-1}$ is positive definite, there exists a symmetric matrix $A$ such that $A^2=\Sigma^{-1}$. One might say that $A=\Sigma^{-1/2}$. The existence of $A$ can be seen by noting that $\Sigma^{-1}$ is diagonalizable. Look that up.
Let's call your 2x2 determinant $D$. Note that $D$ can be expressed as a bunch of inner products as follows:
$$<\Sigma^{-1}\mu, \mu><\Sigma^{-1}1_n,1_n>-<\Sigma^{-1}\mu,1_n><\Sigma^{-1}1_n, \mu>$$
Since $\Sigma^{-1}$ is symmetric and real, it is self-adjoint, which means that the product in the second term is of equal numbers (the second equality is because we are in a real vector space - in a complex vector space, we would need to take the complex conjugate to retain equality): $$<\Sigma^{-1}\mu,1_n>=<\mu,\Sigma^{-1}1_n>=<\Sigma^{-1}1_n,\mu>$$
Let's rewrite in terms of $A$:
$$<A^2\mu,\mu><A^21_n,1_n>-<A^2\mu, 1_n>^2$$
Again, A is symmetric and real, so it is also self-adjoint and this becomes:
$$<A\mu,A\mu><A1_n,A1_n>-<A\mu,A1_n>^2$$
The Cauchy-Schwarz inequality finishes us off. As a reminder, the Cauchy-Schwarz inequality states that using the usual inner product in $R^n$ or $C^n$, we get that:
$$|<x,y>| \le <x,x>^{1/2}<y,y>^{1/2}$$
So we get that:
$$<A\mu,A1_n>^2 \le <A\mu,A\mu><A1_n,A1_n>$$
Then subtracting the left handside on both sides of this inequality gives us $D \ge 0$.Shown in full with attribution under the source's licence. Licence: CC BY-SA 4.0 (Stack Exchange)
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.