Head of Quantitative Modelling and Data Analytics
Aitor’s journey in finance began as an undergraduate exchange student studying maths at the University of Texas, where he became fascinated by the bridge between stochastic calculus and finance.
Next, he pursued an MSc in Mathematical Finance at Imperial College London for which he was awarded the Natixis Foundation for Quantitative Research 2017 prize for best Master’s thesis (across EU and UK). Aitor has since been an active member of the research community publishing several peer-reviewed papers.
Aitor went on to earn a PhD in mathematics from Imperial College, and his research areas include stochastic volatility modelling, machine learning, and AI in finance. He is an expert in Monte Carlo simulation methods and is skilled in C#, C++, and Python.
Recently, Risk Magazine awarded Aitor the 2020 rising star award in quantitative finance for his seminal paper, "Deep Learning Volatility" SSRN:3322085. Remarkably, this work shows how to bypass the black-box paradigm when applying deep learning methods to calibrate financial models.
It is not rare to find Aitor on the speaker list of conferences such as Quantminds, V-Fi Europe and MathFinance, where he has shared his thinking on topics that include Machine Learning, Monte Carlo Methods, and Volatility derivatives. If you see him around, he will be more than happy to discuss any of these topics (and many more!) over a cup of coffee.
Before joining Kaiju Capital Management, Aitor served as a Quantitative Research Analyst at Natixis, where he worked in the equities division. At Kaiju, Aitor makes sure that our quantitative models continuously remain state-of-the-art. He is a firm believer in the value of using AI and Machine Learning to improve our decision-making process.