Título :	Multi-agent based modeling applied to portfolio selection in the doom-loop of sovereign debt context*
Autor :	Rosa, Paulo Sérgio Gartner, Ivan Ricardo Ralha, Célia Ghedini
metadata.dc.identifier.orcid:	http://orcid.org/0000-0003-4481-1916
Assunto::	Investimentos Modelo Baseado em Agente (MBA) Dívida pública Ciclo de destruição Risco sistêmico
Fecha de publicación :	2019
Editorial :	Sociedade Brasileira de Pesquisa Operacional
Citación :	ROSA, Paulo Sérgio; GARTNER, Ivan Ricardo; RALHA, Célia Ghedin. Multi-agent based modeling applied to portfolio selection in the doom-loop of sovereign debt context*. Pesquisa Operacional, v. 39, n. 1, p. 57-84, 2019. DOI: https://doi.org/10.1590/0101-7438.2019.039.01.0057. Disponível em: http://scielo.br/scielo.php?script=sci_arttext&pid=S0101-74382019000100003. Acesso em: 23 jan. 2020.
Abstract:	This study explores the self-fulfilling dynamic between sovereign debt risk and rational choices of neutral, risk-seeking and risk-averse investors, with implications to the systemic risk emergence. The agent-based model parameterization includes investment strategy (randomly selected assets, stock exchange participation, economic segment, and technical analysis), portfolio rebalance period, and stop gain/loss option. We use Brazilian markets data from 2006 to 2017 to simulate stochastic distributions of investments by a set of 3,000 agents in both stages of model verification and validation (robustness check). Using the Capital Asset Pricing Model, we confirmed our proposition that the optimal rational risk attitude (less risk appetite) constitutes a trigger for the self-fulfilling dynamic, having its foundation on government securities yield and in the debt dynamics. This finding is contrary to the equity premium puzzle in the Brazilian case. The findings have implications to policymakers regarding systemic risk issues, among other public policies.
metadata.dc.description.unidade:	Faculdade de Economia, Administração, Contabilidade e Gestão de Políticas Públicas (FACE)
metadata.dc.description.ppg:	Programa de Pós-Graduação em Administração
Licença::	(CC BY)
DOI:	https://doi.org/10.1590/0101-7438.2019.039.01.0057
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