We introduce the notion of rough local stochastic volatility models , extending the classical concept to the case where volatility is driven by some Volterra process. In this setting, we show that the pricing function is the solution to a path-dependent PDE , for which we develop a numerical scheme based on Deep Learning techniques. Numerical simulations suggest that the latter is extremely efficient, and provides a good alternative to classical Monte Carlo simulations.
We introduce a new deep-learning based algorithm to evaluate options in affine rough stochastic volatility models . We show that the pricing function is the solution to a curve-dependent PDE (CPDE), depending on forward curves rather than the whole path of the process, for which we develop a numerical scheme based on deep learning techniques. Numerical simulations suggest that the latter is extremely efficient, and provides a good alternative to classical Monte Carlo simulations.
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the notion of rough local
a new deep-learning based algorithm to evaluate options in affine rough
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, extending the classical concept to the case where volatility is driven by some Volterra process. In this setting, we
. We
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path-dependent PDE ,
curve-dependent PDE (CPDE), depending on forward curves rather than the whole path of the process,
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Deep Learning
deep learning