AI 中文总结
研究在市场摩擦下对衍生品头寸套期保值的交易规则，应用稳健套期保值估值调整，结合资金和保证金附加项评估跟踪损失CVaR，在不同流动性市场比较经典与学习到的套期保值规范，给出不同风险预算下的选择建议。
AI 中文摘要
在交易成本和市场摩擦下对衍生品头寸进行套期保值需要适应不断变化情况的交易规则。深度套期保值为此任务训练神经策略，但策略训练并不能确定交易台是否能够承担运行该策略的成本。我们应用稳健套期保值估值调整（HVA）作为训练后的估值调整层，它结合明确的资金和保证金附加项来评估跟踪损失条件风险价值（CVaR）。资金和保证金附加项与HVA共享相同的KL不确定性集。对于每个策略，单个共同压力倾斜联合计算HVA、资金和保证金，交易台可以获得一个内部一致的储备金，而不是分别获得三个。我们在三种不同流动性的市场环境中比较经典套期保值策略和学习到的套期保值规范。没有单一的规范在每个市场中都占主导地位。在严格的跟踪风险预算下，在高流动性和中等流动性市场中选择伽马宽的经典区间，而在低流动性市场中选择稀疏的学习执行方式。在较宽松的验证预算下，通常选择更宽的经典区间。
英文摘要
Hedging a derivative position under transaction costs and market frictions requires a trading rule that adapts to changing conditions. Deep hedging trains a neural policy for this task but policy training does not determine whether a trading desk can afford to run the policy. We apply robust hedging valuation adjustment (HVA) as a post-training valuation-adjustment layer that evaluates tracking-loss CVaR together with explicit funding and margin add-ons. The funding and margin add-ons share the same KL uncertainty set as HVA. For each policy, a single common-stress tilt computes HVA, funding and margin jointly and a trading desk can get one internally consistent reserve instead of the three separately. We compare classical hedge policies with learned hedge specifications across three market environments with different liquidity. No single specification dominates in every market. Under the strict tracking-risk budget, gamma-wide classical bands are selected in High and Middle Liquidity while sparse learned execution is selected in Low Liquidity. At looser validation budgets wider classical bands are generally selected.
