🤖 AI Summary
This study addresses the trade-off between rebalancing costs and hedging error risk under liquidity constraints in dynamic hedging. The authors propose a robust methodology for quantifying Hedging Valuation Adjustment (HVA) by simulating rebalancing and unwinding trades under a no-trade band policy, constructing the resulting loss distribution, and optimizing the worst-case expected loss within a relative entropy uncertainty set—considering both fixed-radius and fixed-reference-stress specifications. Their analysis demonstrates that the width of the no-trade band significantly influences turnover, costs, and risk: while wider bands reduce transaction costs, they concurrently increase hedging error. This framework provides both theoretical grounding and a practical tool for implementing robust hedging strategies in liquidity-constrained environments.
📝 Abstract
This paper develops a robust hedging valuation adjustment (HVA) measure for dynamic hedging. Simulated rebalancing and maturity-unwind trades generate a loss distribution for each no-trade-band rule, and we define robust HVA as the worst-case expected loss over a relative-entropy neighborhood of that distribution. Because band width affects turnover, the same relative-entropy radius applied to different bands can imply different levels of demand-liquidity stress. We distinguish a fixed-radius convention from a fixed benchmark-stress convention and show that wider no-trade bands lower rebalancing costs but raise hedge-error risk.