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Robust dynamic dimension reduction techniques for volatilities

Grant number: 18/03012-3
Support type:Scholarships abroad - Research Internship - Post-doctor
Effective date (Start): July 31, 2018
Effective date (End): November 29, 2018
Field of knowledge:Applied Social Sciences - Economics - Quantitative Methods Applied to Economics
Principal researcher:Pedro Luiz Valls Pereira
Grantee:Carlos Cesar Trucios Maza
Supervisor abroad: Marc Hallin
Home Institution: Escola de Economia de São Paulo (EESP). Fundação Getúlio Vargas (FGV). São Paulo , SP, Brazil
Research place: Université Libre de Bruxelles (ULB), Belgium  
Associated to the scholarship:16/18599-4 - Modeling and forecasting volatility of high dimensional financial series, BP.PD


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 of the principal/common components returns. These procedures are usually outperformed by other methodologies including simpler multivariate volatility models such as EWMA, ORE and RiskMetrics. On the other hand, there are a few proper procedures to extract the main features of the volatility process. However, all of them are extremely sensitive the possible presence of outliers. This project aims to discuss dimension reduction techniques for volatilities to model and forecast the conditional covariance matrix in a large panel of financial time series as well as to propose a proper robust dynamic dimension reduction technique for volatilities. The new procedure will be compared with other procedures proposed in the literature.

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Scientific publications (4)
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
TRUCIOS, CARLOS; MAZZEU, JOAO H. G.; HOTTA, LUIZ K.; VALLS PEREIRA, PEDRO L.; HALLIN, MARC. Robustness and the general dynamic factor model with infinite-dimensional space: Identification, estimation, and forecasting. INTERNATIONAL JOURNAL OF FORECASTING, v. 37, n. 4, p. 1520-1534, OCT-DEC 2021. Web of Science Citations: 0.
TRUCIOS, CARLOS. Forecasting Bitcoin risk measures: A robust approach. INTERNATIONAL JOURNAL OF FORECASTING, v. 35, n. 3, p. 836-847, JUL-SEP 2019. Web of Science Citations: 3.
TRUCIOS, CARLOS; HOTTA, LUIZ K.; VALLS PEREIRA, PEDRO L. On the robustness of the principal volatility components. JOURNAL OF EMPIRICAL FINANCE, v. 52, p. 201-219, JUN 2019. Web of Science Citations: 1.
TRUCIOS, CARLOS; ZEVALLOS, MAURICIO; HOTTA, LUIZ K.; SANTOS, ANDRE A. P. Covariance Prediction in Large Portfolio Allocation. ECONOMETRICS, v. 7, n. 2 JUN 2019. Web of Science Citations: 1.

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