Listar por autor "Leitao, Álvaro"
Mostrando ítems 1-11 de 11
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A Modular Framework for Generic Quantum Algorithms
Manzano, Alberto; Musso, Daniele; Leitao, Álvaro; Gómez, Andrés; Vázquez, Carlos; Ordóñez, Gustavo; Rodríguez Nogueiras, María (MDPI, 2022)[Abstract] We describe a general-purpose framework to design quantum algorithms. This framework relies on two pillars: a basic data structure called quantum matrix and a modular structure based on three quasi-independent ... -
A Survey on Quantum Computational Finance for Derivatives Pricing and VaR
Gómez, Andrés; Leitao, Álvaro; Manzano, Alberto; Musso, Daniele; Nogueiras, María R.; Ordóñez, Gustavo; Vázquez, Carlos (Springer, 2022-10)[Abstract]: We review the state of the art and recent advances in quantum computing applied to derivative pricing and the computation of risk estimators like Value at Risk. After a brief description of the financial ... -
Boundary-safe PINNs extension: Application to non-linear parabolic PDEs in counterparty credit risk
Pérez Villarino, Joel; Leitao, Álvaro; García Rodríguez, José Antonio (Elsevier B.V., 2023)[Abstract]: The goal of this work is to develop a novel strategy for the treatment of the boundary conditions for multi-dimension nonlinear parabolic PDEs. The proposed methodology allows to get rid of the heuristic choice ... -
Deep Learning-Based Method for Computing Initial Margin †
Pérez Villarino, Joel; Leitao, Álvaro (MDPI, 2021)[Abstract] Following the guidelines of the Basel III agreement (2013), large financial institutions are forced to incorporate additional collateral, known as Initial Margin, in their transactions in OTC markets. Currently, ... -
Machine Learning to Compute Implied Volatility from European/American Options Considering Dividend Yield
Liu, Shuaiqiang; Leitao, Álvaro; Borovykh, Anastasia; Oosterlee, Cornelis (MDPI AG, 2020-09-15)[Abstract] Computing implied volatility from observed option prices is a frequent and challenging task in finance, even more in the presence of dividends. In this work, we employ a data-driven machine learning approach ... -
On a Neural Network to Extract Implied Information from American Options
Liu, Shuaiqiang; Leitao, Álvaro; Borovykh, Anastasia; Oosterlee, Cornelis (Routledge, 2022)[Abstract] Extracting implied information, like volatility and dividend, from observed option prices is a challenging task when dealing with American options, because of the complex-shaped early-exercise regions and the ... -
Quantum Arithmetic for Directly Embedded Arrays
Manzano, Alberto; Musso, Daniele; Leitao, Álvaro; Gómez, Andrés; Vázquez, Carlos; Ordóñez, Gustavo; Rodríguez Nogueiras, María (MDPI, 2021)[Abstract] We describe a general-purpose framework to implement quantum algorithms relying upon an efficient handling of arrays. The cornerstone of the framework is the direct embedding of information into quantum amplitudes, ... -
Real quantum amplitude estimation
Manzano, Alberto; Musso, Daniele; Leitao, Álvaro (Springer, 2023)[Abstract]: 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 iterative algorithm which ... -
Spline local basis methods for nonparametric density estimation
Kirkby, Justin Lars; Leitao, Álvaro; Nguyen, Duy (Institute of Mathematical Statistics, 2023)[Abstract]: This work reviews the literature on spline local basis methods for non-parametric density estimation. Particular attention is paid to B-spline density estimators which have experienced recent advances in both ... -
The stochastic θ-SEIHRD model: Adding randomness to the COVID-19 spread
Leitao, Álvaro; Vázquez, Carlos (Elsevier, 2022)[Abstract]: In this article we mainly extend a newly introduced deterministic model for the COVID-19 disease to a stochastic setting. More precisely, we incorporated randomness in some coefficients by assuming that they ... -
VI Congreso XoveTIC: impulsando el talento científico
Lagos Rodríguez, Manuel; Leitao, Álvaro; Varela Rodeiro, Tirso; Pereira-Loureiro, Javier; Penedo, Manuel (Universidade da Coruña, Servizo de Publicacións, 2023)