🤖 AI Summary
This study addresses a critical gap in the literature by developing a computational model capable of capturing the full value chain of Brazilian Real Estate Investment Trusts (FIIs) and their interaction with macroeconomic dynamics, which has previously hindered effective policy analysis and mechanism design. The authors propose an agent-based artificial market that, for the first time, integrates within a unified framework the entire FII process—from property income generation and dividend distribution to trading of shares by heterogeneous investors via a double-auction order book. The model endogenously incorporates key macroeconomic variables such as the Selic interest rate and inflation, alongside behaviorally heterogeneous agents whose decisions are driven by financial literacy. It successfully replicates key stylized facts of the IFIX index, achieving over 75% coverage of calibrated moments and producing simulated trajectories statistically indistinguishable from empirical data in 96% of cases, while spontaneously generating power-law decay in absolute return autocorrelations and aggregate Gaussianity.
📝 Abstract
This article presents the development and validation of an artificial market for Brazilian Real Estate Investment Trusts (REITs), known as Fundos de Investimento Imobiliario (FIIs), using agent-based modeling methodology. The central contribution of this work is the integration, within a single multi-agent system, of the FII value chain, from the generation of real estate revenues subject to vacancy and operational costs, through dividend distribution, to the trading of shares by heterogeneous investors mediated by a double auction mechanism with an order book. The model incorporates endogenous macroeconomic variables, such as the Selic, the Brazilian benchmark interest rate, and inflation, and represents agent heterogeneity through a behavioral decomposition into fundamentalist, speculator, and noise trader components, modulated by individual financial literacy levels. The model was calibrated using the Method of Simulated Moments applied to the historical series of the IFIX index, the Brazilian REIT market index, between 2021 and 2025. The validation results, obtained using two distinct methods, demonstrate that the model reproduces the main stylized facts observed in the real market: (i) the coverage rate of calibrated moments exceeds 75 percent; (ii) 96 percent of simulated trajectories are structurally indistinguishable from real IFIX periods according to the nearest-neighbor criterion; and (iii) stylized facts such as the power law of autocorrelations of absolute returns and aggregational Gaussianity emerge spontaneously, without being incorporated into the calibration objective function. The results of the validation process indicate that the artificial market captures structural dynamics of the FII market, opening perspectives for its use as a computational laboratory for the analysis of regulatory policies and pricing mechanisms.