In this paper, using high-frequency intra-daily data from the UK market, we employ genetic programming (GP) to uncover a hedging strategy for FTSE 100 call options, hedged using FTSE 100 futures contracts. The output from the evolved strategies is a rebalancing signal which is conditioned upon a range of dynamic non-linear factors related to market conditions including liquidity and volatility. When this signal exceeds threshold values during the trading day, the hedge position is rebalanced. The performance of the GP-evolved strategy is evaluated against a number of commonly used, time-based, deterministic hedging strategies where the hedge position is rebalanced at fixed time intervals ranging from 5 min to 1 day. Assuming the delta hedger pays the bid-ask spread on the futures contract whenever the portfolio is rebalanced, this study finds that the GP-evolved hedging strategy out-performs standard, deterministic, time-based approaches. Empirical analysis shows that the superior performance of the GP strategy is driven by its ability to account for non-linear intra-day persistence in high frequency measures of liquidity and volatility. This study is the first to apply a GP methodology for the task of delta hedging with high frequency data, a significant risk management issue for investors and market makers in financial options.

中文翻译：

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delta对冲的遗传编程方法

在本文中，我们使用来自英国市场的高频日内数据，采用遗传编程 (GP) 来揭示 FTSE 100 看涨期权的对冲策略，使用 FTSE 100 期货合约进行对冲。演化策略的输出是一个再平衡信号，该信号取决于与市场条件（包括流动性和波动性）相关的一系列动态非线性因素。当该信号在交易日内超过阈值时，对冲头寸将重新平衡。GP-evolved 策略的表现是根据许多常用的、基于时间的、确定性的对冲策略进行评估的，其中对冲头寸在 5 分钟到 1 天的固定时间间隔内重新平衡。假设 delta 套期保值者在投资组合重新平衡时支付期货合约的买卖价差，本研究发现，GP 演化的对冲策略优于标准的、确定性的、基于时间的方法。实证分析表明，GP 策略的卓越表现是由其在流动性和波动性的高频度量中考虑非线性日内持续性的能力驱动的。本研究首次将 GP 方法应用于具有高频数据的 delta 对冲任务，这是金融期权投资者和做市商面临的重大风险管理问题。