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Gaussian mixture model dynamically controlled kernel estimation (GMM-DCKE), a purely data-driven and model-agnostic method to compute conditional expectations, is introduced. Joerg Kienitz applies it to the pricing and hedging of (multi-dimensional) exotic Bermudan options and to calibration and pricing within stochastic local volatility models
Fast and accurate approximations of conditional expectations and their respective distributions are essential for many
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