Analytic solution to variance optimization with no short-selling

TL;DR

This paper analytically examines variance portfolio optimization under no-short selling constraints, revealing a phase transition at a critical ratio where the estimator remains finite but becomes highly sensitive.

Contribution

It provides an exact analytic solution showing how no-short selling acts as an asymmetric regularizer and shifts the phase transition point in variance optimization.

Findings

The no-short selling constraint shifts the critical ratio from 1 to 2.

At the critical point, the out-of-sample variance estimator remains finite.

Numerical simulations confirm the analytic phase transition and solution behavior.

Abstract

A large portfolio of independent returns is optimized under the variance risk measure with a ban on short positions. The no-short selling constraint acts as an asymmetric $\ell_1$ regularizer, setting some of the portfolio weights to zero and keeping the out of sample estimator for the variance bounded, avoiding the divergence present in the non-regularized case. However, the susceptibility, i.e. the sensitivity of the optimal portfolio weights to changes in the returns, diverges at a critical value $r=2$. This means that a ban on short positions does not prevent the phase transition in the optimization problem, it merely shifts the critical point from its non-regularized value of $r=1$ to $2$. At $r=2$ the out of sample estimator for the portfolio variance stays finite and the estimated in-sample variance vanishes. We have performed numerical simulations to support the analytic results and found perfect agreement for $N/T<2$. Numerical experiments on finite size samples of symmetrically distributed returns show that above this critical point the probability of finding solutions with zero in-sample variance increases rapidly with increasing $N$, becoming one in the large $N$ limit. However, these are not legitimate solutions of the optimization problem, as they are infinitely sensitive to any change in the input parameters, in particular they will wildly fluctuate from sample to sample. We also calculate the distribution of the optimal weights over the random samples and show that the regularizer preferentially removes the assets with large variances, in accord with one's natural expectation.