Luca Merlo

Department of Human Sciences

European University of Rome

Rome (RM), Italy

I obtained my PhD in Methodological Statistics at the Sapienza University of Rome (Italy) with a thesis on quantile regression models for the analysis of multivariate data. I actively collaborate with national and international institutions, including the Harvard T.H. Chan School of Public Health (US), the University of Southampton (United Kingdom) and the University of Pisa (Italy). As of September 2022, I am a researcher in statistics at the European University of Rome (Italy). My research interests lie in quantile regression, latent variable models, finite mixture models with applications to longitudinal, time series and correlated data.

News

Feb 7, 2024	Our paper with Valeria Bignozzi and Lea Petrella, "Inter-order relations between equivalence for 𝐿𝑝-quantiles of the Student’s 𝑡 distribution" has been published on Insurance: Mathematics and Economics. Check it out here.
Jan 13, 2024	Our paper with Beatrice Foroni and Lea Petrella, “Expectile hidden Markov regression models for analyzing cryptocurrency returns” has been published on Statistics and Computing. Check it out here.
Dec 18, 2023	I will be chairing the session “Recent advances in quantile regression models” at the 16th international CMStatistics 2023 conference hosted by HTW Berlin, University of Applied Sciences, Berlin.

Selected Publications

2024

Expectile hidden Markov regression models for analyzing cryptocurrency returns

Beatrice Foroni, Luca Merlo, and Lea Petrella

Statistics and Computing, 2024

2023

Unified unconditional regression for multivariate quantiles, M-quantiles and expectiles

Luca Merlo, Lea Petrella, Nicola Salvati, and 1 more author

Journal of the American Statistical Association, 2023

2022

Quantile mixed hidden Markov models for multivariate longitudinal data: An application to children’s Strengths and Difficulties Questionnaire scores

Luca Merlo, Lea Petrella, and Nikos Tzavidis

Journal of the Royal Statistical Society Series C: Applied Statistics, 2022
Marginal M-quantile regression for multivariate dependent data

Luca Merlo, Lea Petrella, Nicola Salvati, and 1 more author

Computational Statistics & Data Analysis, 2022
Quantile hidden semi-Markov models for multivariate time series

Luca Merlo, Antonello Maruotti, Lea Petrella, and 1 more author

Statistics and Computing, 2022

2021

Forecasting VaR and ES using a joint quantile regression and its implications in portfolio allocation

Luca Merlo, Lea Petrella, and Valentina Raponi

Journal of Banking & Finance, 2021