14 citations · 16 across the 7 of their papers we have counts for
8 papers
Quantum computing for multidimensional option pricing: End-to-end pipeline
Julien Hok, Álvaro Leitao
This work introduces an end-to-end framework for multi-asset option pricing that combines market-consistent risk-neutral density recovery with quantum-accelerated numerical integra…
Parametric Numerical Integration with (Differential) Machine Learning
Álvaro Leitao, Jonatan Ráfales
In this work, we introduce a machine/deep learning methodology to solve parametric integrals. Besides classical machine learning approaches, we consider a differential learning fra…
Static and dynamic SABR stochastic volatility models: calibration and option pricing using GPUs
J. L. Fernández, A. M. Ferreiro, J. A. García +3
For the calibration of the parameters in static and dynamic SABR stochastic volatility models, we propose the application of the GPU technology to the Simulated Annealing global op…
On Deep Learning for computing the Dynamic Initial Margin and Margin Value Adjustment
Joel P. Villarino, Álvaro Leitao
The present work addresses the challenge of training neural networks for Dynamic Initial Margin (DIM) computation in counterparty credit risk, a task traditionally burdened by the…
Boundary-safe PINNs extension: Application to non-linear parabolic PDEs in counterparty credit risk
Joel P. Villarino, Álvaro Leitao, José A. García-Rodríguez
The goal of this work is to develop deep learning numerical methods for solving option XVA pricing problems given by non-linear PDE models. A novel strategy for the treatment of th…
Real Quantum Amplitude Estimation
Alberto Manzano, Daniele Musso, Álvaro Leitao
We introduce the Real Quantum Amplitude Estimation (RQAE) algorithm, an extension of Quantum Amplitude Estimation (QAE) which is sensitive to the sign of the amplitude. RQAE is an…
Quantum Arithmetic for Directly Embedded Arrays
Alberto Manzano, Daniele Musso, Álvaro Leitao +4
We describe a general-purpose framework to design quantum algorithms relying upon an efficient handling of arrays. The corner-stone of the framework is the direct embedding of info…
On Calibration Neural Networks for extracting implied information from American options
Shuaiqiang Liu, Álvaro Leitao, Anastasia Borovykh +1
Extracting implied information, like volatility and/or dividend, from observed option prices is a challenging task when dealing with American options, because of the computational…