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Machine Learning to Compute Implied Volatility from European/American Options Considering Dividend Yield
(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 ...
Deep Learning-Based Method for Computing Initial Margin †
(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, ...
Quantum Arithmetic for Directly Embedded Arrays
(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, ...
A Survey on Quantum Computational Finance for Derivatives Pricing and VaR
(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 ...
A Modular Framework for Generic Quantum Algorithms
(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 ...
On a Neural Network to Extract Implied Information from American Options
(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 ...
The stochastic θ-SEIHRD model: Adding randomness to the COVID-19 spread
(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 ...