# Good Deal Hedging and Valuation under Combined Uncertainty about Drift and Volatility

## TL;DR

This paper develops a robust framework for good-deal hedging and valuation under combined drift and volatility uncertainty, using second-order BSDEs and a multiple priors approach to ensure strategies are supermartingale robust.

## Contribution

It introduces a novel non-dominated multiple priors approach to model uncertainty, characterizes bounds via non-convex second-order BSDEs, and derives robust hedging strategies.

## Key findings

- Good-deal bounds exclude arbitrage and overly favorable deals.
- Hedging strategies minimize a coherent risk measure under uncertainty.
- Strategies are robust with supermartingale tracking errors across all priors.

## Abstract

We study robust notions of good-deal hedging and valuation under combined uncertainty about the drifts and volatilities of asset prices. Good-deal bounds are determined by a subset of risk-neutral pricing measures such that not only opportunities for arbitrage are excluded but also deals that are too good, by restricting instantaneous Sharpe ratios. A non-dominated multiple priors approach to model uncertainty (ambiguity) leads to worst-case good-deal bounds. Corresponding hedging strategies arise as minimizers of a suitable coherent risk measure. Good-deal bounds and hedges for measurable claims are characterized by solutions to second-order backward stochastic differential equations whose generators are non-convex in the volatility. These hedging strategies are robust with respect to uncertainty in the sense that their tracking errors satisfy a supermartingale property under all a-priori valuation measures, uniformly over all priors.