Alternatives to deep neural networks in finance

Two methods to approximate complex functions in an explainable way are presented


Alexandre V. Antonov and Vladimir V. Piterbarg develop two methods for approximating slow-to-calculate functions and for conditional expected value calculations: the generalised stochastic sampling (gSS) method and the functional tensor train (fTT) method, respectively. These are proposed as high-performing alternatives to the generic deep neural networks (DNNs) currently routinely recommended in derivatives pricing and other quantitative finance applications. The

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