Vladimir specialises in quantitative risk management, econometrics and machine learning. His expertise spans credit risk, market risk and VaR, stress testing, Monte Carlo simulation, and the design and validation of risk models.

He combines hands-on quantitative modelling with an academic career as a professor of financial econometrics at ESADE, and has authored his own capital-structure risk model. He is MIT-trained in finance and data science and holds the CQF and PRM certifications.

Education: MIT MicroMasters in Finance and in Statistics & Data Science; M.Sc. Finance, UniversitatPompeuFabra.

Expertise: Risk modelling · VaR · credit risk · stress testing · Monte Carlo · Python · R · MATLAB· machine learning