论文标题
具有深度对冲的衍生品的同等风险定价
Equal Risk Pricing of Derivatives with Deep Hedging
论文作者
论文摘要
本文提出了一种深厚的加强学习方法和对冲金融衍生品。这种方法扩展了Guo and Zhu（2017）的工作，后者最近引入了同等风险定价框架，在这种框架中，应有索赔的价格分别等同于在衍生产品中的长期和短位置相关的最佳对冲剩余风险暴露。对后一个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。通过这种修改方法获得的衍生价格表明无套利。当前的论文还为受Buehler等人的深度对冲算法启发的同等风险定价框架提供了一般且可进行的实施。 （2019）。还提出了一项$ε$ completentes措施，允许量化与衍生物相关的残留对冲风险。后一种措施概括了Bertsimas等人中提出的措施。 （2001）基于二次罚款。蒙特卡洛模拟是在各种市场动态下进行的，以证明我们方法的实用性，以对传统方法进行基准测试并进行灵敏度分析。
This article presents a deep reinforcement learning approach to price and hedge financial derivatives. This approach extends the work of Guo and Zhu (2017) who recently introduced the equal risk pricing framework, where the price of a contingent claim is determined by equating the optimally hedged residual risk exposure associated respectively with the long and short positions in the derivative. Modifications to the latter scheme are considered to circumvent theoretical pitfalls associated with the original approach. Derivative prices obtained through this modified approach are shown to be arbitrage-free. The current paper also presents a general and tractable implementation for the equal risk pricing framework inspired by the deep hedging algorithm of Buehler et al. (2019). An $ε$-completeness measure allowing for the quantification of the residual hedging risk associated with a derivative is also proposed. The latter measure generalizes the one presented in Bertsimas et al. (2001) based on the quadratic penalty. Monte Carlo simulations are performed under a large variety of market dynamics to demonstrate the practicability of our approach, to perform benchmarking with respect to traditional methods and to conduct sensitivity analyses.
