Detection of Uncertainty Events in the Brazilian Economic and Financial Time Series

Cristiane Gea, Luciano Vereda and Eduardo Ogasawara ()
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Cristiane Gea: Federal Center for Technological Education of Rio de Janeiro (CEFET/RJ)
Luciano Vereda: Fluminense Federal University (UFF)
Eduardo Ogasawara: Federal Center for Technological Education of Rio de Janeiro (CEFET/RJ)
Authors registered in the RePEc Author Service: Luciano Vereda Oliveira

Computational Economics, 2024, vol. 64, issue 3, No 7, 1507-1538

Abstract: Abstract Economic policy uncertainty shocks change how the economy behaves, moving it away from its pattern. Therefore, these effects can be understood as an event. Given this, the problem of event detection becomes particularly relevant for a more accurate understanding of how uncertainty affects the behavior of economic and financial time series. Thus, the present work aims to answer the following questions: (1) What events do economic policy uncertainty shocks cause in the economic and financial time series? (2) What is the most suitable method for detecting such events? (3) Does applying the ensemble methodology contribute to a more accurate detection? We studied various Brazilian financial time series to answer these questions. The findings indicate that (1) the trend anomaly and the change point are the most prominent types of events for the Brazilian case; (2) in most cases analyzed, the group of financial series presents the highest values observed in the metrics used to evaluate event detection methods; and (3) the application of the ensemble methodology contributes to more accurate event detection, compared to the performance of individual methods.

Keywords: Economic policy uncertainty; Anomaly detection; Event detection; Trend anomaly; Volatility anomaly; Change point (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10614-023-10483-3

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