Networked Volatility: Linking Oil, Petrobrás and VALE in times of crisis
DOI:
https://doi.org/10.5007/2175-8077.2026.e107140Keywords:
spillover, vector autoregression, GFEVD, oil priceAbstract
Goal: This study analyzes the influence of international oil prices on Petrobrás (PETR4) and VALE (VALE3) stocks, also considering the impact of the S&P 500 index from January 2021 to January 2023, a period marked by exogenous events such as the COVID-19 pandemic and the Russia-Ukraine conflict.
Methodology/approach: The research uses daily data extracted via the yfR package from Yahoo Finance and applies a Vector Autoregressive (VAR) model, using ADF, KPSS, BIC, AIC, and HQ tests to check stationarity and determine lag selection. Additional tests of normality, autocorrelation, and heteroskedasticity were performed, along with the Generalized Forecast Error Variance Decomposition (GFEVD) method proposed by Diebold and Yilmaz (2012) to measure spillovers, and Granger causality testing (1969).
Originality/relevance: The study’s originality lies in applying GFEVD to a recent and globally volatile sample, focusing on key Brazilian companies in the international commodities market.
Main results: Oil (CL1) influenced 4.81% of PETR4 and 7.72% of VALE3 variance over a 10-day horizon. The spillover index dropped during COVID-19 peaks and the start of the war, indicating temporary disruptions.
Theoretical/methodological contributions: The findings support the VAR-GFEVD model as a robust tool for analyzing asset interconnectivity.
Managerial contributions: Results offer guidance for investors and policymakers in understanding how Brazilian stocks respond to external shocks, enhancing strategic decisions in volatile environments.
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