Deep asymptotics
Introducing a new technique to control the behaviour of neural networks
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Artificial neural networks have recently been proposed as accurate and fast approximators in various derivatives pricing applications. Their extrapolation behaviour cannot be controlled due to the complex functional forms typically involved. Alexandre Antonov, Michael Konikov and Vladimir Piterbarg overcome this significant limitation and develop a new type of neural network that incorporates large-value asymptotics, allowing explicit control over extrapolation
Artif
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