通过蒙特卡洛树搜索对金融衍生品合约进行对冲
Hedging of Financial Derivative Contracts via Monte Carlo Tree Search
AI总结：
本文提出将蒙特卡洛树搜索引入金融衍生品定价与对冲的随机最优控制问题，相比Q学习具有更高样本效率和更强策略学习能力，实验表明其能直接最大化投资者终端财富效用。
摘要：
在不完全市场中构建用于衍生品合约定价和对冲的近似复制策略，是金融工程的一个关键问题。近年来，在现实市场条件下进行对冲的强化学习算法引起了广泛关注。虽然衍生品领域的研究大多集中在Q学习的各种变体上，但在人工智能领域，蒙特卡洛树搜索被公认为解决各种规划问题的最先进方法，例如Hex、国际象棋、围棋等游戏。本文引入蒙特卡洛树搜索作为解决定价和对冲任务背后的随机最优控制问题的方法。与Q学习相比，它将强化学习与树搜索技术相结合。因此，蒙特卡洛树搜索具有更高的样本效率，更不容易对特定市场模型过拟合，并且通常能更快地学习到更强的策略。在我们的实验中，我们发现蒙特卡洛树搜索作为国际象棋和围棋等游戏的世界冠军方法，能够轻松地在不建立辅助数学框架的情况下最大化投资者终端财富的效用。
英文摘要：
The construction of approximate replication strategies for pricing and hedging of derivative contracts in incomplete markets is a key problem of financial engineering. Recently Reinforcement Learning algorithms for hedging under realistic market conditions have attracted significant interest. While research in the derivatives area mostly focused on variations of $Q$-learning, in artificial intelligence Monte Carlo Tree Search is the recognized state-of-the-art method for various planning problems, such as the games of Hex, Chess, Go,... This article introduces Monte Carlo Tree Search as a method to solve the stochastic optimal control problem behind the pricing and hedging tasks. As compared to $Q$-learning it combines Reinforcement Learning with tree search techniques. As a consequence Monte Carlo Tree Search has higher sample efficiency, is less prone to over-fitting to specific market models and generally learns stronger policies faster. In our experiments we find that Monte Carlo Tree Search, being the world-champion in games like Chess and Go, is easily capable of maximizing the utility of investor's terminal wealth without setting up an auxiliary mathematical framework.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
