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 research effort is HTBoost (Hybrid Tree Boosting, github.com/PaoloGiordani/HybridTreeBoosting.jl), an extension of gradient boosting machines with superior accuracy in approximating smooth and partially smooth functions (and approximately equal accuracy for irregular functions). 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, and Topics in Financial Econometrics for the PhD in finance.

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