The recent work of Horikawa and Nakagawa (2024) claims that under a complete market admitting statistical arbitrage, the difference between the hedging position provided by deep hedging and that of the replicating portfolio is a statistical arbitrage. This raises concerns as it entails that deep hedging can include a speculative component aimed simply at exploiting the structure of the risk measure guiding the hedging optimisation problem. We test whether such finding remains true in a GARCH-based market model, which is an illustrative case departing from complete market dynamics. We observe that the difference between deep hedging and delta hedging is a speculative overlay if the risk measure considered does not put sufficient relative weight on adverse outcomes. Nevertheless, a suitable choice of risk measure can prevent the deep hedging agent from engaging in speculation.
A deep BSDE approach for the simultaneous pricing and delta-gamma hedging of large portfolios consisting of high-dimensional multi-asset Bermudan options
提出基于离散反射 BSDE、One Step Malliavin 格式与神经网络回归 Monte Carlo 的深度方法，实现高维多资产百慕大期权大型投资组合的联合定价及 delta-gamma 对冲。
On the Performance of Delta Hedging Strategies in Exponential Lévy Models
本文研究指数Lévy模型中非最优套期保值策略的性能。假设未定权益和套期保值策略的收益都有合适的积分表示，我们使用Hubalek et al.（2006）的Laplace变换方法，根据潜在莱维过程的累积量母函数，推导出由此产生的均方对冲误差的半显式公式。在两个数值例子中，我们应用这些结果比较的效率的Black-Scholes对冲和模型delta的均值-方差最优对冲在正常的逆高斯和扩散扩展CGMY Lévy模型。
Equal Risk Pricing of Derivatives with Deep Hedging
本文提出了一种用于定价和对冲金融衍生品的深度强化学习方法。这种方法扩展了Guo和Zhu（2017）的工作，他们最近引入了等风险定价框架，其中或有债权的价格是通过分别与衍生品中的多头和空头头寸相关联的最佳对冲剩余风险敞口来确定的。对后一种方案的修改被认为是为了规避与原始方法相关的理论陷阱。通过这种经修改的方法获得的衍生工具价格显示为无仲裁。本文还提出了一种通用且易于操作的等风险定价框架的实现，该框架受到Buehler et al.（2019）的深度对冲算法的启发。一个$ε$完整性措施，允许量化的剩余套期保值风险与衍生工具也提出了。后一种措施推广了Bertsimas等人（2001）提出的基于二次惩罚的措施。蒙特卡罗模拟在各种各样的市场动态下进行，以证明我们的方法的实用性，相对于传统方法进行基准测试，并进行敏感性分析。
Enhancing Deep Hedging of Options with Implied Volatility Surface Feedback Information
Department of Finance, HEC Montréal, Canada（蒙特利尔HEC金融系）；GERAD and Department of Decision Sciences, HEC Montréal, Canada（GERAD与蒙特利尔HEC决策科学系）；Concordia University, Department of Mathematics and Statistics, Canada（康科迪亚大学数学与统计学系）
Autonomous AI Agents for Option Hedging: Enhancing Financial Stability through Shortfall Aware Reinforcement Learning
Cornell Ann S. Bowers College of Computing and Information Science, Cornell University（康奈尔大学安·索斯·博尔斯计算与信息科学学院）；Department of Mathematics, University of Texas at Austin（德克萨斯大学奥斯汀分校数学系）；School of Social Sciences, Nanyang Technological University（南洋理工大学社会科学学院）；Krieger School of Arts and Sciences, Johns Hopkins University（约翰霍普金斯大学克里eger艺术与科学学院）
Application of Deep Reinforcement Learning to At-the-Money S&P 500 Options Hedging
Faculty of Economic Sciences, University of Warsaw（华沙大学经济学院）；Department of Quantitative Finance and Machine Learning, University of Warsaw（华沙大学量化金融与机器学习系）；TripleSun, Krakow（克拉科夫TripleSun）
