Abstract
A series of papers have explored the use of Deep Neural Nets to substantially speed up the calibration of pricing models. This paper uses Chebyshev Tensors for the same purpose. In particular, it shows how the computational bottleneck in the calibration of the rough Bergomi volatility model can be alleviated using Chebyhsev Tensors. The calibration speed and accuracy obtained in this paper is comparable to when Deep Neural Nets are used. Building efforts, however, are up to 100 times lower, allowing for much faster pricing model proxy update - a feature of particular importance at times of market distress. This constitutes a further enhancement over the already sizable improvement provided by Deep Neural Nets.
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