Mila Andreani
Data Science Ph.D. Student (Cycle 36)
Bio:
Mila Andreani was born in Rome, Italy in 1997. In 2018 she obtained her BSc in Business Sciences cum laude at La Sapienza university and was appointed as the best 2018 graduate student in Business Sciences by the Italian Polygraphic Institute and State Mint and La Sapienza Faculty of Economics.
In July 2020, she graduated cum laude in MSc Quantitative Finance at La Sapienza university with a thesis in time series analysis. Meanwhile, she was an honor student at the Sapienza School for Advanced Studies (SSAS) and won a 10-month scholarship at La Sapienza university on the topic of sustainable finance funded by the MIUR.
Before joining the PhD in Data Science, she was a trainee at the European Central Bank in the Directorate General Information Systems.
Research Interest:
Machine Learning, Time Series, Quantile Regression, Financial Risk Measures, Mixed-Frequency Data
Affiliation:
Email:
Bibliography
2022
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, “Quantile Regression Forest for Value-at-Risk Forecasting Via Mixed-Frequency Data”, Mathematical and Statistical Methods for Actuarial Sciences and Finance. Springer, Cham, 2022.
2021
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, “Multivariate Analysis of Energy Commodities during the COVID-19 Pandemic: Evidence from a Mixed-Frequency Approach”, Risks, vol. 9, p. 144, 2021.
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, “Quantile Regression Forest with mixed-frequency data”, vol. Book of Short Papers SIS 2021. Pearson, 2021.
2020
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, “Dynamic Quantile Regression Forest”, SIS 2020 - 50th Conference of the Italian Statistical Society, vol. Book of Short Papers SIS 2020. Pearson, 2020.
