@proceedings {1091,
	title = {Quantile Regression Forest for Value-at-Risk Forecasting Via Mixed-Frequency Data},
	year = {2022},
	publisher = {Springer, Cham},
	doi = {https://doi.org/10.1007/978-3-030-99638-3},
	author = {Mila Andreani and Candila, Vincenzo and Petrella, Lea}
}
@article {300,
	title = {Multivariate Analysis of Energy Commodities during the COVID-19 Pandemic: Evidence from a Mixed-Frequency Approach},
	journal = {Risks},
	volume = {9},
	year = {2021},
	pages = {144},
	issn = {2227-9091},
	doi = {10.3390/risks9080144},
	url = {https://www.mdpi.com/2227-9091/9/8/144},
	author = {Mila Andreani and Candila, Vincenzo and Morelli, Giacomo and Petrella, Lea}
}
@proceedings {299,
	title = {Quantile Regression Forest with mixed-frequency data},
	volume = {Book of Short Papers SIS 2021},
	year = {2021},
	month = {06/2021},
	publisher = {Pearson},
	issn = {9788891927361},
	url = {https://it.pearson.com//docenti/universita/partnership/sis.html},
	author = {Mila Andreani and Vincenzo Candila and Lea Petrella}
}
@proceedings {288,
	title = {Dynamic Quantile Regression Forest},
	volume = {Book of Short Papers SIS 2020},
	year = {2020},
	month = {10/2020},
	publisher = {Pearson},
	issn = {9788891910776},
	url = {https://it.pearson.com/content/dam/region-core/italy/pearson-italy/pdf/Docenti/Universit\%C3\%A0/Pearson-SIS-2020-atti-convegno.pdf},
	author = {Mila Andreani and Lea Petrella}
}
