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We present a parallel algorithm for solving backward stochastic differentialequations (BSDEs in short) which are very useful theoretic tools to deal withmany financial problems ranging from option pricing option to risk management.Our algorithm based on Gobet and Labart (2010) exploits the link between BSDEsand non linear partial differential equations (PDEs in short) and hence enablesto solve high dimensional non linear PDEs. In this work, we apply it to thepricing and hedging of American options in high dimensional local volatilitymodels, which remains very computationally demanding. We have tested ouralgorithm up to dimension 10 on a cluster of 512 CPUs and we obtained linearspeedups which proves the scalability of our implementation

A Parallel Algorithm for solving BSDEs - Application to the pricing and hedging of American options