π€ AI Summary
This study addresses the limitations of traditional risk-neutral pricing in the SOFR derivatives market, where illiquidity and market incompleteness lead to pricing failure and unquantifiable hedging errors. To overcome these challenges, the paper introduces convex risk measures into an indifference pricing framework, yielding a novel model that integrates market quotes, investor positions, subjective views on uncertainty, and risk preferences. The proposed approach not only generates prices and hedging strategies but also provides explicit, interpretable measures of hedging error and risk, making it particularly suitable for non-replicable OTC products. Empirical tests using hundreds of CME-listed contracts demonstrate that the model can produce sparse, highly efficient hedging portfolios on a standard PC within one minute, effectively approximating target payoff structures.
π Abstract
Thousands of SOFR derivatives are available in exchanges and OTC, but the market remains illiquid and incomplete. Such a market is beyond the scope of classic risk-neutral approaches that imply linear pricing rules and, at best, approximate hedging strategies whose hedging error may be difficult to quantify. This paper develops an indifference pricing model which is consistent with observed derivative quotes, the agent's financial position and views about the uncertain future as well as risk preferences as described by a convex risk measure. In addition to prices and hedging strategies, the model gives an explicit description of the hedging error and the associated risk. The approach is illustrated numerically using hundreds of CME-listed derivatives to price and hedge unreplicable OTC SOFR derivatives. The indifference prices are computed in less than a minute on a regular PC. We find that the optimal hedging portfolios tend to be sparse but still provide good approximations of the derivative payouts.