Previsão de risco caudal de indicadores macroeconômicos utilizando regressão quantílica baseada em D-vine cópula - Silva, João Marcos Soares da (2025)
This study compares D-vine copula-based quantile regression with traditional quantile regression models that incorporate some form of shrinkage, in forecasting conditional quantiles and, in particular, tail risks of macroeconomic variables. The D-vine copula-based quantile regression approach stands out for being parsimonious, with automatic covariate selection, and flexible, by capturing deviations from linearity in the quantiles. Using U.S. macroeconomic data (GDP, inflation, and unemployment), the results indicate that, for GDP growth rate, quantile regressions with Lasso and Ridge penalties perform better at short horizons, while the D-vine regression shows superior performance at longer horizons. For the unemployment rate, the D-vine regression outperforms all considered methods across all horizons. As for inflation, the D-vine method did not show significant gains.