Derivative pricing with a neural network based on the binomial tree

S. G. Shorokhov

Abstract


Based on the Cox-Ross-Rubinstein option pricing model, forward-propagation neural network architectures are built to approximate the value of European and American put options. For the European put option, the first hidden layer of the neural network is the dense layer with the ReLU activation function, the subsequent layers are convolutional with a 1D filter of dimension two and the identity activation function. For the American put option, the first hidden layer of the neural network is also dense with a ReLU activation function, subsequent layers have a maxout activation function and depend on both the output of the previous layer and the output of the input layer (the strike price value). The layers of both neural networks have the number of neurons decreasing by one with each subsequent layer up to one neuron in the output layer. It is shown that the neural network for the European put option can be simplified to a two-layer feed-forward neural network.

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References


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