Inflexible Multi-Asset Hedging of incomplete market
This work addresses hedging challenges for financial practitioners in incomplete markets, but it is incremental as it applies existing neural network methods to a specific domain problem.
The paper tackles the hedging problem in incomplete markets by addressing three sources of incompleteness—risk factor, illiquidity, and discrete transaction dates—using a new jump-diffusion model and neural networks, with Mogrifier-LSTM achieving the best and fastest results under MSE and Huber Loss.
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.