We use a supervised deep convolution neural network to replicate the calibration of the Heston model to equity volatility surfaces. For this purpose we treat the implied volatility surface together with some auxiliary data, namely the strikes and moneyness of the corresponding options and the...

In a recent paper "Deep Learning Volatility" a fast 2-step deep calibration algorithm for rough volatility models was proposed: in the first step the time consuming mapping from the model parameter to the implied volatilities is learned by a neural network and in the second step standard solver...

We use temporally adapted neural networks to solve a generalization of the optimal exercise problem for a discrete set of possible exercise times. Versions based on convolutional and attention layers were implemented, tested and found to produce state of the art results on the fractional...