Giorgio Mirone is based at Aarhus University 2016-2019, and his research project is Identifying the Structure of Volatility Using High-Frequency and News Data (WP3)
“Developing new tools to draw inference from high-frequency data and news is of extreme interest for the academia, while having several important applications for the financial industry”
I moved from my home town when I was 18 to get my bachelor in Economics and Finance and then an MSc in Finance in Siena. I have been travelling around since then. First as an exchange student in Barcelona and again as an exchange in Melbourne during my Master.
My Master thesis developed around volatility estimations using HF data. That was my first approach with the BigData world. Even though I started working full time in the industry before obtaining my degree, I kept building up on my thesis project in the months following my graduation, pushed by curiosity for the field. This interest made me realize that I wanted to pursue a PhD in Financial Econometrics and, the moment I saw the BigDataFinance project, I knew I had to join the program.
Enthusiasm for the BigDataFinance project comes from the goals the program has set. Among the others, the idea of developing new tools to draw inference from high-frequency data and news is of extreme interest for the academia, while having several important applications for the financial industry. No doubts market practitioners will benefit from the tools developed within the BigDataFinance project.