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As is well known, the centerpiece of model calibration is regularization, which plays an important role in transforming an ill-posed calibration problem into a stable and well-formulated one. This realm of research has not been explored empirically in much detail in the literature. The goal of this paper is to understand and give an answer to a question concerning pricing accuracy using the parameters resulting from a correctly posed calibration problem in comparison with those inferred from a relaxed calibration. Our empirical findings indicate that regularized calibration is only to be recom...

In this paper the authors formulate the one-dimensional RMQ and d-dimensional PMQ algorithms as standard vector quantization problems by deriving the density,

This paper compares two methods to calibrate two popular models that are widely used for stochastic volatility modeling (ie, the SABR and Heston models) with

Optimal transport theory offers a data-driven way to calibrate derivatives pricing models

The paper argues for the need for and importance of the dual calibration of a probability of default (PD) model (ie, calibration to both point-in-time and