We investigate the performance of the Deep Hedging framework under training paths beyond the (finite dimensional) Markovian setup. In particular we analyse the hedging performance of the original architecture under rough volatility models with view to existing theoretical results for those. Furthermore, we suggest parsimonious but suitable network architectures capable of capturing the non-Markoviantity of time-series. Secondly, we analyse the hedging behaviour in these models in terms of P\&L distributions and draw comparisons to jump diffusion models if the the rebalancing frequency is realistically small.

中文翻译：

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波动剧烈时的深层对冲

我们研究了深度套期保值框架在超出（有限维）马尔可夫设置的训练路径下的性能。尤其是，我们将在粗糙波动率模型下分析原始体系结构的套期保值性能，并参考现有的理论结果。此外，我们建议使用精简但合适的网络架构，以捕获时间序列的非马尔可夫性。其次，我们根据盈亏分布分析了这些模型中的套期保值行为，如果再平衡频率实际上很小，则可以与跳跃扩散模型进行比较。