Sandra Ranilla-Cortina- Doctor of Philosophy
- Assistant Professor at University of Oviedo
Sandra Ranilla-Cortina
- Doctor of Philosophy
- Assistant Professor at University of Oviedo
Assistant Professor - PhD Mathematics - Quant Modeler / Quant AI Developer
About
9
Publications
245
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21
Citations
Introduction
I hold a PhD in Mathematics from Universidad Complutense de Madrid (UCM) and Universidad Politécnica de Madrid (UPM), with a specialization in the convergence of mathematics, quantum computing, finance, and artificial intelligence. My most recent position in the industry was as AVP Quant Modeler at Credit Suisse (UBS). Currently, I am serving as an Assistant Professor at the Universidad de Oviedo (UniOvi), where I continue to advance both academic research and industry-driven applications.
Current institution
Additional affiliations
July 2022 - December 2024
Credit Suisse (UBS)
Position
- AVP Quant Modeler
Description
- Development, implementation and optimization of monitoring and backtesting methodologies for IRB models, within existing and new strategic risk systems (EBA requirements).
Education
April 2022 - September 2023
Instituto BME
Field of study
- Artificial Intelligence applied to Financial Markets (mIA-X)
October 2019 - June 2024
October 2018 - June 2019
Publications
Publications (9)
We consider the non-adapted version of a simple problem of portfolio optimization in a financial market that results from the presence of insider information. We analyze it via anticipating stochastic calculus and compare the results obtained by means of the Russo-Vallois forward, the Ayed-Kuo, and the Hitsuda-Skorokhod integrals. We compute the op...
In this work, we study the zero forcing problem and some of its variants regarding the connectivity of the subgraph generated by the zero forcing set. A set Z of vertices from a graph G is said to be a zero forcing set of G if iteratively adding to it unique neighboring vertices of those vertices V(G)∖Z already in Z results in the entire vertex set...
We consider the non-adapted version of the classical portfolio optimization problem in a financial market that results from the presence of an insider trader, where there exists a delay in the insider information flow. We analyze it via anticipating stochastic calculus, specifically by means of the Russo-Vallois forward stochastic integral, and wit...
In this work we study the presence of insider information in a non-adapted version of the simple problem of portfolio optimization in a financial market. We consider three different anticipating stochastic integration theories: the Russo-Vallois Forward, the Ayed-Kuo, and the Hitsuda-Skorokhod integrals. We analyze a specific formulation of the ins...
We consider the non-adapted version of a simple problem of portfolio optimization in a financial market that results from the presence of insider information. We analyze it via anticipating stochastic calculus and compare the results obtained by means of the Russo-Vallois forward, the Ayed-Kuo, and the Hitsuda-Skorokhod integrals. We compute the op...
