MULTI-AGENT BASED MODELING APPLIED TO PORTFOLIO SELECTION IN THE DOOM-LOOP OF SOVEREIGN DEBT CONTEXT*

Detalhes bibliográficos
Autor(a) principal: Rosa,Paulo Sérgio
Data de Publicação: 2019
Outros Autores: Gartner,Ivan Ricardo, Ralha,Célia Ghedini
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Pesquisa operacional (Online)
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-74382019000100003
Resumo: 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.
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spelling MULTI-AGENT BASED MODELING APPLIED TO PORTFOLIO SELECTION IN THE DOOM-LOOP OF SOVEREIGN DEBT CONTEXT*portfolio selectionagent-based modelsovereign debtdoom-loopsystemic riskABSTRACT 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.Sociedade Brasileira de Pesquisa Operacional2019-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-74382019000100003Pesquisa Operacional v.39 n.1 2019reponame:Pesquisa operacional (Online)instname:Sociedade Brasileira de Pesquisa Operacional (SOBRAPO)instacron:SOBRAPO10.1590/0101-7438.2019.039.01.0057info:eu-repo/semantics/openAccessRosa,Paulo SérgioGartner,Ivan RicardoRalha,Célia Ghedinieng2019-05-07T00:00:00Zoai:scielo:S0101-74382019000100003Revistahttp://www.scielo.br/popehttps://old.scielo.br/oai/scielo-oai.php||sobrapo@sobrapo.org.br1678-51420101-7438opendoar:2019-05-07T00:00Pesquisa operacional (Online) - Sociedade Brasileira de Pesquisa Operacional (SOBRAPO)false
dc.title.none.fl_str_mv MULTI-AGENT BASED MODELING APPLIED TO PORTFOLIO SELECTION IN THE DOOM-LOOP OF SOVEREIGN DEBT CONTEXT*
title MULTI-AGENT BASED MODELING APPLIED TO PORTFOLIO SELECTION IN THE DOOM-LOOP OF SOVEREIGN DEBT CONTEXT*
spellingShingle MULTI-AGENT BASED MODELING APPLIED TO PORTFOLIO SELECTION IN THE DOOM-LOOP OF SOVEREIGN DEBT CONTEXT*
Rosa,Paulo Sérgio
portfolio selection
agent-based model
sovereign debt
doom-loop
systemic risk
title_short MULTI-AGENT BASED MODELING APPLIED TO PORTFOLIO SELECTION IN THE DOOM-LOOP OF SOVEREIGN DEBT CONTEXT*
title_full MULTI-AGENT BASED MODELING APPLIED TO PORTFOLIO SELECTION IN THE DOOM-LOOP OF SOVEREIGN DEBT CONTEXT*
title_fullStr MULTI-AGENT BASED MODELING APPLIED TO PORTFOLIO SELECTION IN THE DOOM-LOOP OF SOVEREIGN DEBT CONTEXT*
title_full_unstemmed MULTI-AGENT BASED MODELING APPLIED TO PORTFOLIO SELECTION IN THE DOOM-LOOP OF SOVEREIGN DEBT CONTEXT*
title_sort MULTI-AGENT BASED MODELING APPLIED TO PORTFOLIO SELECTION IN THE DOOM-LOOP OF SOVEREIGN DEBT CONTEXT*
author Rosa,Paulo Sérgio
author_facet Rosa,Paulo Sérgio
Gartner,Ivan Ricardo
Ralha,Célia Ghedini
author_role author
author2 Gartner,Ivan Ricardo
Ralha,Célia Ghedini
author2_role author
author
dc.contributor.author.fl_str_mv Rosa,Paulo Sérgio
Gartner,Ivan Ricardo
Ralha,Célia Ghedini
dc.subject.por.fl_str_mv portfolio selection
agent-based model
sovereign debt
doom-loop
systemic risk
topic portfolio selection
agent-based model
sovereign debt
doom-loop
systemic risk
description 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.
publishDate 2019
dc.date.none.fl_str_mv 2019-01-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-74382019000100003
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-74382019000100003
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/0101-7438.2019.039.01.0057
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv text/html
dc.publisher.none.fl_str_mv Sociedade Brasileira de Pesquisa Operacional
publisher.none.fl_str_mv Sociedade Brasileira de Pesquisa Operacional
dc.source.none.fl_str_mv Pesquisa Operacional v.39 n.1 2019
reponame:Pesquisa operacional (Online)
instname:Sociedade Brasileira de Pesquisa Operacional (SOBRAPO)
instacron:SOBRAPO
instname_str Sociedade Brasileira de Pesquisa Operacional (SOBRAPO)
instacron_str SOBRAPO
institution SOBRAPO
reponame_str Pesquisa operacional (Online)
collection Pesquisa operacional (Online)
repository.name.fl_str_mv Pesquisa operacional (Online) - Sociedade Brasileira de Pesquisa Operacional (SOBRAPO)
repository.mail.fl_str_mv ||sobrapo@sobrapo.org.br
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