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Why and how should we utilize deep learning methods to calculate implied vol of options? I've also heard that finding the fair price of the option is not nearly as important as finding a numerical method to accurately measure IV, is this true? Is this a big point of research in Quant Fin?

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    $\begingroup$ It doesn't really sound like a problem suitable for deep learning. There exists fast and accurate numerical methods to calculate implied volatility. I don't see the benefit of deep learning there. On the other hand, in more complicated problems, I guess such techniques could be useful. $\endgroup$ Oct 19 '20 at 6:01
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As @Jesper Tidblom already stated in his comment, the quant finance problem is not in inverting observed prices to estimate the implied volatility; this is a well understood and, admittedly, simple problem these days.

Finding (model) prices for very complex derivatives products is a potential field for applied ML. Especially in counterparty and market risk applications, improvements of computation times are sought after. Using ML, you would be 'swapping' training time (offline) for online calculation speed, at the cost of potentially reduced accuracy.

The intricacy here is that in order to apply an ML model, you need data... And in terms of (complex) derivatives valuation, there is usually not sufficient observable data in the markets, e.g. from Bloomberg and such; thus you'd have to run your valuation model (costly) to train your ML model (costly) to have better intraday online performance. To add to that, there already exist known methods to arrive at approximate prices, e.g. Taylor expansions, (batch/overnight) pre-computation of price for various parameter combinations ...

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