Marcelo Cunha Medeiros

About

Marcelo Cunha Medeiros is a distinguished professor at the University of Illinois, holding the Jorge Paulo Lemann Distinguished Chair in Economics. He serves as the Assistant Head for Faculty Development in the Economics department and is also a professor in the fields of Economics and Finance. Additionally, he is affiliated with the Center for Latin American and Caribbean Studies. Medeiros's research expertise centers on advanced econometric and statistical methods, particularly in time series modeling, autoregressive models, and nonlinear models. His work extensively covers topics such as realized volatility, factor models, and generalized autoregressive conditional heteroscedasticity (GARCH). He has contributed significantly to the development of shrinkage estimators and model selection techniques, with a focus on applications in economics and finance. Medeiros's scholarly output includes numerous peer-reviewed articles that explore the intersection of econometrics, forecasting, and applied economics, demonstrating a strong emphasis on methodological innovation and empirical analysis.

Research topics

• Computer Science
• Artificial Intelligence
• Statistics
• Mathematics
• Econometrics
• Economics
• Psychology
• Social psychology
• Algorithm

Selected publications

• Bridging factor and sparse models

  The Annals of Statistics · 2023-08-01 · 44 citations

  articleSenior author

  Factor and sparse models are widely used to impose a low-dimensional structure in high-dimensions. However, they are seemingly mutually exclusive. We propose a lifting method that combines the merits of these two models in a supervised learning methodology that allows for efficiently exploring all the information in high-dimensional datasets. The method is based on a flexible model for high-dimensional panel data with observable and/or latent common factors and idiosyncratic components. The mode…

  PublisherDOI

• Forecasting Large Realized Covariance Matrices: The Benefits of Factor Models and Shrinkage

  Journal of Financial Econometrics · 2023-05-11 · 9 citations

  article

  Abstract We propose a model to forecast large realized covariance matrices of returns, applying it to the constituents of the S&P 500 daily. To address the curse of dimensionality, we decompose the return covariance matrix using standard firm-level factors (e.g., size, value, and profitability) and use sectoral restrictions in the residual covariance matrix. This restricted model is then estimated using vector heterogeneous autoregressive models with the least absolute shrinkage and selectio…

  PublisherDOI

• Forecasting Large Realized Covariance Matrices: The Benefits of Factor Models and Shrinkage

  arXiv (Cornell University) · 2023-03-22 · 4 citations

  preprintOpen access

  We propose a model to forecast large realized covariance matrices of returns, applying it to the constituents of the S\&P 500 daily. To address the curse of dimensionality, we decompose the return covariance matrix using standard firm-level factors (e.g., size, value, and profitability) and use sectoral restrictions in the residual covariance matrix. This restricted model is then estimated using vector heterogeneous autoregressive (VHAR) models with the least absolute shrinkage and selection…

  PublisherOA PDFDOI

• Forecasting inflation using disaggregates and machine learning

  arXiv (Cornell University) · 2023-08-22 · 3 citations

  preprintOpen accessSenior author

  This paper examines the effectiveness of several forecasting methods for predicting inflation, focusing on aggregating disaggregated forecasts - also known in the literature as the bottom-up approach. Taking the Brazilian case as an application, we consider different disaggregation levels for inflation and employ a range of traditional time series techniques as well as linear and nonlinear machine learning (ML) models to deal with a larger number of predictors. For many forecast horizons, the ag…

  PublisherOA PDFDOI

• Modeling and Forecasting Intraday Market Returns: a Machine Learning Approach

  arXiv (Cornell University) · 2021-12-30 · 2 citations

  preprintOpen accessSenior author

  In this paper we examine the relation between market returns and volatility measures through machine learning methods in a high-frequency environment. We implement a minute-by-minute rolling window intraday estimation method using two nonlinear models: Long-Short-Term Memory (LSTM) neural networks and Random Forests (RF). Our estimations show that the CBOE Volatility Index (VIX) is the strongest candidate predictor for intraday market returns in our analysis, specially when implemented through t…

  PublisherOA PDFDOI

Frequent coauthors

• Michael McAleer

  Tinbergen Institute

  49 shared

• Ricardo Masini

  University of California, Davis

  42 shared

• Álvaro Veiga

  Pontifical Catholic University of Rio de Janeiro

  26 shared

• Eduardo Mendes

  Institut polytechnique de Grenoble

  22 shared

• Márcio Garcia

  Pontifical Catholic University of Rio de Janeiro

  17 shared

• Gabriel Vasconcelos

  Brazilian Development Bank

  17 shared

• Eric Hillebrand

  Lancaster University

  13 shared

• Carlos E. Pedreira

  Universidade Federal do Rio de Janeiro

  10 shared

Education

• PhD, Electrical Engineering

  Pontifícia Universidade Católica do Rio de Janeiro

  2000

• Master of Science, Electrical Engineering

  Pontifícia Universidade Católica do Rio de Janeiro

  1998

• Bachelor, Electrical Engineering

  Pontifícia Universidade Católica do Rio de Janeiro

  1996

Awards & honors

• Elected Fellow of the Society for Financial Econometrics (So…
• Associate Editor for the Journal of Financial Econometrics
• Associate Editor for the Quarterly Review of Economics and F…
• Associate Editor for the Journal of the American Statistical…
• Member of the editorial board of the Annals Financial Econom…

Similar researchers at University of Illinois Urbana-Champaign

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• Anil K Berah-23
• Stefan Krasah-20