Recent Submissions

  • 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, ...
  • On the Adaptive Numerical Solution to the Darcy–Forchheimer Model † 

    González Taboada, María; Varela Rodríguez, Hiram (MDPI, 2021)
    [Abstract] We considered a primal-mixed method for the Darcy–Forchheimer boundary value problem. This model arises in fluid mechanics through porous media at high velocities. We developed an a posteriori error analysis of ...
  • 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, ...
  • European and American Options Valuation by Unsupervised Learning with Artificial Neural Networks 

    Salvador, Beatriz; Oosterlee, Cornelis W.; Meer, Remco van der (MDPI AG, 2020-08-19)
    [Abstract] Artificial neural networks (ANNs) have recently also been applied to solve partial differential equations (PDEs). In this work, the classical problem of pricing European and American financial options, based ...
  • 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 ...

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