Paolo Giordani

I am professor of Financial Econometrics and former quant. My main research interests are statistical machine learning, Bayesian inference, and volatility. I am particularly interested in problems related to modeling the entire time-varying probability distribution of financial assets, and to detecting and modeling various forms of concept drift (changes in the relation between inputs and output). My latest effort is SMARTboost, a machine learning tool designed specifically for the challenges posed by financial data (github.com/PaoloGiordani/SMARTboost.jl). My favorite financial applications are option strategies and position sizing.
Before joining the faculty of BI in 2018, I was a senior advisor at the research division of the Swedish Central Bank (developing statistical models and forecasting tools) and a quant in two hedge funds (developing option strategies). At BI I teach Data Science for Finance and Quantitative Risk and Asset Management for the Msc in Quantitative Finance.

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