In the derivatives world, zillion computations need to be done on a daily basis. Models need to be calibrated. Derivative instruments need to be priced. Hedge positions need to be calculated. Risk management indicators need to be determined. In our whitepaper, we show how one can deploy machine learning techniques in the context of traditional quant problems. We illustrate that for many classical problems, we can arrive to speed-ups of several orders of magnitude by deploying machine learning techniques based on Gaussian process regression. The price we have to pay for this extra speed is some loss of accuracy. However, we show that this reduced accuracy is often well within reasonable limits (for example within bid-ask spread) and hence very acceptable from a practical point of view.

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