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

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

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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Main Authors: Rosa,Paulo Sérgio, Gartner,Ivan Ricardo, Ralha,Célia Ghedini
Format: Digital revista
Language:English
Published: Sociedade Brasileira de Pesquisa Operacional 2019
Online Access:http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-74382019000100003
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spelling oai:scielo:S0101-743820190001000032019-05-07MULTI-AGENT BASED MODELING APPLIED TO PORTFOLIO SELECTION IN THE DOOM-LOOP OF SOVEREIGN DEBT CONTEXT*Rosa,Paulo SérgioGartner,Ivan RicardoRalha,Célia Ghedini portfolio selection agent-based model sovereign debt doom-loop systemic risk 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.info:eu-repo/semantics/openAccessSociedade Brasileira de Pesquisa OperacionalPesquisa Operacional v.39 n.1 20192019-01-01info:eu-repo/semantics/articletext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-74382019000100003en10.1590/0101-7438.2019.039.01.0057
institution SCIELO
collection OJS
country Brasil
countrycode BR
component Revista
access En linea
databasecode rev-scielo-br
tag revista
region America del Sur
libraryname SciELO
language English
format Digital
author Rosa,Paulo Sérgio
Gartner,Ivan Ricardo
Ralha,Célia Ghedini
spellingShingle Rosa,Paulo Sérgio
Gartner,Ivan Ricardo
Ralha,Célia Ghedini
MULTI-AGENT BASED MODELING APPLIED TO PORTFOLIO SELECTION IN THE DOOM-LOOP OF SOVEREIGN DEBT CONTEXT*
author_facet Rosa,Paulo Sérgio
Gartner,Ivan Ricardo
Ralha,Célia Ghedini
author_sort Rosa,Paulo Sérgio
title MULTI-AGENT BASED MODELING APPLIED TO PORTFOLIO SELECTION IN THE DOOM-LOOP OF SOVEREIGN DEBT CONTEXT*
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*
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.
publisher Sociedade Brasileira de Pesquisa Operacional
publishDate 2019
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-74382019000100003
work_keys_str_mv AT rosapaulosergio multiagentbasedmodelingappliedtoportfolioselectioninthedoomloopofsovereigndebtcontext
AT gartnerivanricardo multiagentbasedmodelingappliedtoportfolioselectioninthedoomloopofsovereigndebtcontext
AT ralhaceliaghedini multiagentbasedmodelingappliedtoportfolioselectioninthedoomloopofsovereigndebtcontext
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