# Inflexible Multi-Asset Hedging of incomplete market

arXiv: https://arxiv.org/abs/2211.00948 · HTML: https://mlsanity.com/paper/2211.00948/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 8 references that are in the map.
- First posted on arXiv 2 Nov 2022.

## Builds on (most cited first, 1 of 1)

- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)

## Abstract (arXiv)

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.