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住房价格周期与波动率的基于智能体建模

文章 arXiv papers · 作者: Kirill S. Glavatskiy et al.
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总结

本研究使用大规模基于智能体的模型，考察城市住房市场价格上涨后为何会出现急剧回调。模型将家庭表示为异质智能体，其决策仍符合经济理性，同时允许其采取趋势跟随行为。模型利用大悉尼地区的人口、经济和金融数据进行校准，覆盖2006年以来三个不同阶段。

报告的模拟结果再现了这些时期的价格动态，包括2017市场峰值前价格波动性的急剧上升。作者将这一现象与家庭趋势跟随行为（被称为理性羊群行为）和借贷倾向的相互作用联系起来。该论述提出了一种机制，说明杠杆和模仿如何放大住房市场周期。证据仅适用于所建模的地区和时期，简要描述也未提供模型细节或样本外验证。研究发现可能有助于拓展对资产市场波动的理解，但并未证明相同动态也适用于可交易证券。

核心观点

  • 异质家庭智能体可以在保留经济理性决策的同时模拟住房市场动态。
  • 趋势跟随行为对于再现模型中的周期性价格上涨和回调很重要。
  • 模型使用大悉尼地区2006年以来三个时期的人口、经济和金融数据。
  • 模型再现了2017市场峰值前价格波动性的上升，并将其与趋势跟随和借贷倾向联系起来。

标签

多资产趋势跟踪波动率
全文
# 2004.07571


# Explaining herding and volatility in the cyclical price dynamics of urban housing markets using a large scale agent-based model









Urban housing markets, along with markets of other assets, universally exhibit periods of strong price increases followed by sharp corrections. The mechanisms generating such non-linearities are not yet well understood. We develop an agent-based model populated by a large number of heterogeneous households. The agents' behavior is compatible with economic rationality, with the trend-following behavior found to be essential in replicating market dynamics. The model is calibrated using several large and distributed datasets of the Greater Sydney region (demographic, economic and financial) across three specific and diverse periods since 2006. The model is not only capable of explaining price dynamics during these periods, but also reproduces the novel behavior actually observed immediately prior to the market peak in 2017, namely a sharp increase in the variability of prices. This novel behavior is related to a combination of trend-following aptitude of the household agents (rational herding) and their propensity to borrow.

在遵守原作品许可的前提下，附作者信息全文展示。 许可协议: abstract CC0

此摘要由 Stratmill 研究智能体根据原文撰写，并非原文副本。