# Theoretical and practical drawbacks of using Deep Learning for calibration and pricing

I am investigating the suitability of using deep learning for pricing and calibration for the full implied volatility surface. Such examples of their application are in papers here and here. During examples, the latter achieved the calibration task in mere milliseconds, which were orders of magnitude faster than numerical approximations and monte carlo methods.

However, I was wondering what some of the drawbacks of using Deep Learning for this task could be, and whether there were any papers or research that highlight this issue. The latter paper cited 'cumbersome computations' during training, but I was wondering if there were any drawbacks regarding, perhaps, the architecture of DL networks as well?

• Not sure if it is in scope of your question, but there was a recent article in Risk explaining some major banks have tried to implement deep learning techniques to automate hedging, and the performance seems to be extremely poor (~20%-30% lower PnL than using traditional hedging methods). – Daneel Olivaw Mar 22 at 18:53
• Could you kindly providly a link to a paper or a source on this? Many thanks. – Hamish Gibson Mar 23 at 11:35
• "Deep hedging strays when volatility gets rough" - paywall though. – Daneel Olivaw Mar 23 at 12:44

Deep learning has drawbacks in that it can take a long time to train. In application, if the market conditions change, you can always use future market data that hasn't happened yet to feed in to your model to get an edge. Proven by the filtration $$F_{t}$$.