The Implicit Function Theorem, or IFT, is a powerful tool for calculating derivatives of functions that solve inverse, i.e. calibration, problems prevalent in financial applications. It is commonly believed that a degree of manual intervention is required to enable financial code to take...

We explain how to compute gradients of functions of the form $G = \frac{1}{2} \sum_{i=1}^{m} (E y_i - C_i)^2$, which often appear in the calibration of stochastic models, using Automatic Adjoint Differentiation and parallelization. We expand on the work of arXiv:1901.04200 and give faster and...

We introduce a new approach to denoising correlation matrices that imposes a block structure with a fixed block-dependent pair-wise correlation within each block and a constant correlation specified for each pair of blocks. We characterize the eigenvalue spectrum and modify the Marchenko-Pastur...