We present a robust Deep Hedging framework for the pricing and hedging of
option portfolios that significantly improves training efficiency and model
robustness. In particular, we propose a neural model for training model
embeddings which utilizes the paths of several advanced equity option models
with stochastic volatility in order to learn the relationships that exist
between hedging strategies. A key advantage of the proposed method is its
ability to rapidly and reliably adapt to new market regimes through the
recalibration of a low-dimensional embedding vector, rather than retraining the
entire network. Moreover, we examine the observed Profit and Loss distributions
on the parameter space of the models used to learn the embeddings. The results
show that the proposed framework works well with data generated by complex
models and can serve as a construction basis for an efficient and robust
simulation tool for the systematic development of an entirely model-independent
hedging strategy.

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