We propose a fully data-driven approach to calibrate local stochastic volatility (LSV) models,
circumventing in particular the ad hoc interpolation of the volatility surface. To achieve this …

Mathematical modelling is ubiquitous in the financial industry and drives key decision
processes. Any given model provides only a crude approximation to reality and the risk of …

In this paper we propose an efficient Monte Carlo scheme for simulating the stochastic
volatility model of Heston (1993) enhanced by a nonparametric local volatility component …

Modern data science techniques are opening the door to data-driven model selection
mechanisms. However, most machine learning models are “black boxes”, as individual …

We recommend the addition of a deterministic displacement to multi-factor affine models to
calibrate and hedge SPX and VIX derivatives jointly. The proposed model, labeled …

We study two-dimensional stochastic differential equations (SDEs) of McKean–Vlasov type
in which the conditional distribution of the second component of the solution given the first …

We depart from the classic bundling literature on single-unit purchases and develop a multi-
unit demand model in which customers decide both the variety and volume of their …

In this paper, we study a semi‐martingale optimal transport problem and its application to
the calibration of local‐stochastic volatility (LSV) models. Rather than considering the …

In this paper, we develop a high-order radial basis function finite difference (RBF-FD)
approximation on a five-point stencil for pricing options under the regime-switching …

We tackle the calibration of the Stochastic Local-Volatility (SLV) model. This is the class of
financial models that combines the local volatility and stochastic volatility features and has …