Examinando por Materia "Cartera multiactivos"
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Publicación Maf Challenge - Retornos de equilibrio para multiactivos(Universidad EAFIT, 2025-11-27) Giraldo García, María Alejandra; Aristizábal Arango, Daniel; Giraldo Restrepo, María Alejandra; Mejía Escobar, Juan Camilo; Grajales Correa, Carlos Alexander; Durango Gutiérrez, María PatriciaThis study develops a quantitative model to estimate equilibrium returns in multi-asset portfolios by integrating the Arbitrage Pricing Theory (APT) with the machine learning algorithm Extreme Gradient Boosting (XGBoost). The model incorporates monetary, real, financial, and expectation-based macroeconomic factors—such as inflation, employment, monetary policy, liquidity, credit spreads, and consumer sentiment—classified as leading, coincident, or lagging. The inclusion of lag structures captures the gradual transmission of macroeconomic shocks, while XGBoost identifies nonlinear patterns and complex interactions among variables. This hybrid framework allows for the estimation of factor sensitivities (betas) and risk premia (λ), producing return estimates consistent with a multifactor equilibrium. The methodology is applied to liquidity instruments, sovereign bonds, high-yield credit, emerging market debt, REITs, and global equities. Results show that both macroeconomic fundamentals and investors’ expectations play a central role in determining expected returns, highlighting the relevance of combining economic theory with modern machine learning techniques.