The development of the multi-curve framework has mainly concentrated on swaps and related products. By opposition, this contribution focuses on STIR futures and their options. They are analysed in a stochastic multiplicative spread multi-curve framework which allows a simultaneous modelling of...

Algorithmic Differentiation (AD) is an efficient way to compute derivatives of a value with respect to the data inputs. In finance the model calibration to market data can be an important part of the valuation process. In presence of calibration, when obtained through exact equation solving or...

Adjoint Algorithmic Differentiation is an efficient way to obtain financial instrument price derivatives with respect to the data inputs. Often the differentiation does not cover the full pricing process when a model calibration is performed. Thanks to the implicit function theorem, the...

This note is dedicated to the impact of collateral on the multi-curve framework. The pricing formulas in presence of collateral are described in a generic way encompassing several financial realities. The collateral cases covered include cash collateral, foreign currency collateral, collateral...