CV Lattes
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Universidade Estadual de Campinas (UNICAMP). Instituto de Matemática, Estatística e Computação Científica (IMECC) (Institutional affiliation from the last research proposal) Birthplace: Peru
Carlos is assistant professor at Federal University of Rio de Janeiro in Brazil.He holds a Bachelor degree in Statistics from the National University of San Marcos (Perú), a Master degree in Statistics from University of Campinas (Brazil). Carlos holds his PhD in Statistics from University of Campinas working under supervision of Luiz K. Hotta (University of Campinas) and Esther Ruiz (University Carlos III of Madrid). He was postdoctoral researcher at Sao Paulo School of Economics, FGV and also visiting posdoc at the European Center for Advanced Research in Economics and Statistics (ECARES, ULB) working under supervision of Marc Hallin.Carlos is currently working on new procedures for modelling and forecasting volatlity in high-dimensional and high-frequency dataHis main research fields are: bootstrap procedures for dependet data, forecast densities, modelling and forecasting volatility in high-dimensional data, robust statistics, dimension reduction techniques and statistical learning. Currently, He is an Assintant Professor at University of Capinas, ISI Elected Member (since 10/2024) and Chairperson Elect of the Latin American Regional Section (LARS) of the International Association for Statistical Computing (IASC). (Source: Lattes Curriculum)
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Big Data is characterized by volume, velocity, variety, and veracity of the data used, as well as the value provided after an appropriate data analysis. In a context of time series, Big Data implies analysing a large number of time series jointly (high dimension) in fine granularities (high frequency), and can even use data from external sources, such as social networks, newspapers and sp…
This project aims to develop and apply hybrid methodologies for modeling and forecasting financial time series. The focus of the project is to use both features extracted through classical models such as (V)ARMA, M(GARCH), M(SV), etc., as well as machine learning models such as neural networks, ensemble models, machine vector support, among others. By studying both approaches, an improvem…
Autoregressive models are widely used in time series. However, these models are based on the linear relationship of the series with its own past, a restriction that does not allow exploring non-linear relationships in the dynamics of the data. Aiming at greater flexibility, non-linear autoregressive models were proposed and competitive results in terms of prediction have been seen in seve…
Pairs-trading strategies combine long positions with short positions of two financial assets. Despite being a concept from the mid-1980s and there are several pairs-trading strategies proposed in the literature, only few of them are widely known and applied by practitioners. Thus, aiming to know and apply different pair-trading strategies, contribute to the formation of human resources th…
The aim of the research project is to propose models and forecasting for the volatility for high dimensional financial time series. It will consider methods to obtain point and interval forecasts and density forecast. Comparisons with existing methods will be considered. Monte Carlo experiments will be carried out to evaluate the performance of the proposed procedures. It will also evalua…
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Dimension reduction techniques are being used in the economic and financial literature as an alternative way to modelling and forecasting the volatility and circumvent the curse of dimensionality. Most of procedures proposed in the literature are developed to extract the main features of returns and assume that the principal/common component for volatilities coincide with the volatility …
The project to be developed at the Department of Statistics of the Universidad Carlos III de Madrid, Spain, aims to discuss the gains of obtain bootstrap prediction intervals for forecast portfolio value-at-risk using a multivariate model of the family MGARCH. The forecast intervals will be compared with bootstrap prediction intervals for forecast portfolio value-at-risk using a univariat…
| 1 / 1 | Completed research grants |
| 1 / 1 | Ongoing scholarships in Brazil |
| 4 / 4 | Completed scholarships in Brazil |
| 2 / 2 | Completed scholarships abroad |
| 8 / 8 | All research grants and scholarships |
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