Hedging market risk and uncertainty via a robust portfolio approach

TL;DR

This paper introduces a robust dynamic hedging framework that explicitly accounts for volatility forecast uncertainty, resulting in more stable, lower-turnover hedges with improved downside protection and risk-adjusted performance.

Contribution

It develops a closed-form robust hedge ratio incorporating forecast uncertainty, enhancing stability and performance over standard methods in diverse asset classes.

Findings

Robust hedge ratios are more stable than standard dynamic hedges.

The approach reduces turnover and improves downside protection.

Statistical tests confirm the significance of performance gains.

Abstract

Shorting for hedging exposes to risk when the market dynamics is uncertain. Managing uncertainty and risk exposure is key in portfolio management practice. This paper develops a robust framework for dynamic minimum-variance hedging that explicitly accounts for forecast uncertainty in volatility estimation to achieve empirical stability and reduced turnover, further improving other standard performance metrics. The approach combines high-frequency realized variance and covariance measures, autoregressive models for multi-step volatility forecasting, and a box-uncertainty robust optimization scheme. We derive a closed-form solution for the robust hedge ratio, which adjusts the standard minimum-variance hedge by incorporating variance forecast uncertainty. Using a diversified sample of equity, bond, and commodity ETFs over 2016-2024, we show that robust hedge ratios are more stable and entail lower turnover than standard dynamic hedges. While overall variance reduction is comparable, the robust approach improves downside protection and risk-adjusted performance, particularly when transaction costs are considered. Bootstrap evidence supports the statistical significance of these gains.