Pareto frontier of portfolio investment under volatility uncertainty and short-sale constraints market

TL;DR

This paper introduces a new portfolio optimization model under volatility uncertainty and short-sale constraints, demonstrating its theoretical properties and superior empirical performance over traditional models.

Contribution

It develops the SLE-MUV model, providing a polynomial analytical solution for the Pareto frontier under volatility uncertainty and validating its effectiveness empirically.

Findings

The Pareto frontier of the SLE-MUV model is a continuous convex curve.

The optimal solution can be expressed as a polynomial in the risk factor w.

The SLE-MUV model significantly improves risk-adjusted returns compared to the traditional MV model.

Abstract

In this paper, we investigate a portfolio investment problem under volatility uncertainty and short-sale constraints market via sublinear expectation which is used to model volatility uncertainty. We assume the stocks admit volatility uncertainty. Thus the related portfolio has upper variance (maximum risk) and lower variance (minimum risk). By introducing a risk factor $w$ to conduct coupled modeling of the maximum and minimum risks, a simplified Sublinear Expectation Mean-Uncertainty Variance (SLE-MUV) model is constructed. Theoretically, we show that the Pareto frontier of the SLE-MUV model is a continuous convex curve, and its optimal solution can be expressed as a polynomial analytical expression with respect to the risk factor $w$. Empirically, we systematically test the practical performance of the SLE-MUV model and conduct comparative analysis with the traditional Mean-Variance (MV) model as the benchmark based on three sets of samples -- simulated generated data, data of the US stock market and the A-share market. The empirical results show that the SLE-MUV model can significantly improving the risk-adjusted return of the investment portfolio.