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

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DataCite Commons2020-08-27 更新2024-07-27 收录
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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.

摘要 本研究探讨了主权债务风险与中性、风险追逐型及风险厌恶型投资者的理性选择之间的自我实现动态机制,并对系统性风险的产生具有重要启示。本研究采用的基于智能体(agent)的模型参数化设置涵盖投资策略(随机选取标的资产、证券交易所参与、经济板块与技术分析)、投资组合再平衡周期以及止盈止损期权。我们使用2006年至2017年的巴西市场数据,在模型验证与确认(稳健性检验)两个阶段中,针对3000名智能体的投资随机分布展开模拟。借助资本资产定价模型(Capital Asset Pricing Model),我们验证了研究命题:最优理性风险态度(更低的风险偏好)是自我实现动态机制的触发因素,其理论根基立足于政府证券收益率与债务动态变化。该研究结论与巴西市场情境下的股权溢价之谜相悖。本研究发现对于政策制定者应对系统性风险及其他公共政策议题具有参考意义。

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SciELO journals
创建时间:
2019-05-15
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