# Option Valuation through Deep Learning of Transition Probability Density

@article{Su2021OptionVT, title={Option Valuation through Deep Learning of Transition Probability Density}, author={Haozhe Su and M. V. Tretyakov and D. Newton}, journal={ArXiv}, year={2021}, volume={abs/2105.10467} }

Transition probability densities are fundamental to option pricing. Advancing recent work in deep learning, we develop novel transition density function generators through solving backward Kolmogorov equations in parametric space for cumulative probability functions, using neural networks to obtain accurate approximations of transition probability densities, creating ultra-fast transition density function generators offline that can be trained for any underlying. These are “single solve”, so… Expand

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