  We present a novel Monte Carlo based LSV calibration algorithm that applies
to all stochastic volatility models, including the non-Markovian rough
volatility family. Our framework overcomes the limitations of the particle
method proposed by Guyon and Henry-Labord\`ere (2012) and theoretically
guarantees a variance reduction without additional computational complexity.
Specifically, we obtain a closed-form and exact calibration method that allows
us to remove the dependency on both the kernel function and bandwidth
parameter. This makes the algorithm more robust and less prone to errors or
instabilities in a production environment. We test the efficiency of our
algorithm on various hybrid (rough) local stochastic volatility models.
