Dynamically controlled kernel estimation
An accurate data-driven and model-agnostic method to compute conditional expectations is presented
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Gordon Lee et al introduce a data-driven and model-agnostic approach for computing conditional expectations. The new method combines classical techniques with machine learning methods, in particular Gaussian process regression and kernel density estimation stabilised with a control variate. The method is applied to pricing and hedging of (multidimensional) exotic
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