Models trained under assumptions in the complete market usually don't take effect in the incomplete market. This paper solves the hedging problem in incomplete market with three sources of incompleteness: risk factor, illiquidity, and discrete transaction dates. A new jump-diffusion model is proposed to describe stochastic asset prices. Three neutral networks, including RNN, LSTM, Mogrifier-LSTM are used to attain hedging strategies with MSE Loss and Huber Loss implemented and compared.As a result, Mogrifier-LSTM is the fastest model with the best results under MSE and Huber Loss.
翻译：在全市场假设下培训的模型通常不会在不完整的市场中产生效果。本文件用三个不完全的来源解决不完整市场的套期保值问题:风险因素、流动性不足和离散交易日期。提出了一个新的跳跃扩散模型来描述资产价格。三个中立网络,包括RNN、LSTM、Mgricor-LSTM, 用来实现执行和比较MSE Loss和Huber Loss的套期保值战略。A因此,Mgricorizer-LSTM是最快的模型,在MSE和Huber Loss下效果最佳。A因此,Mgrictor-LSTM是最快的模型。
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