Mean–variance hedging with model risk

Abstract

This paper studies a hedging problem of a derivative security in a one-period model when there is the model risk. The hedging error is measured by a quadratic criterion. The model risk means that the true model is uncertain and there are many candidates for the true model. The true model is assumed to be in a set of models. We study an optimal strategy which minimizes the worst-case hedging error over all models in the set. We show how to calculate an optimal strategy and the minimum hedging error effectively. Finally we give some numerical examples to demonstrate the usefulness of our method.

A Minimum Variance Hedging Ratio Model Based on Nonlinear Grey Classification Model

“…e basis for determining HR, according to reference [22], is to make Sharpe ratio optimal, and it proposes that HR be determined on the basis of maximizing hedging utility. Matsumoto [26] confirmed the OLS model's suitability for hedging as well as the fact that the HR obtained by a simple linear regression model is lower than that obtained by a traditional one and improved the model and hedging method. However, the OLS model is still based on a set of assumptions that ignore autocorrelation and heteroskedasticity of residual series, as well as time series cointegration.…”