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用扩展夏雷拉模型估计趋势与价值的相互作用

文章 arXiv papers · 作者: Adam Majewski et al.
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总结

本研究采用异质主体框架，考察趋势跟踪和基本面估值如何在金融市场中共存。研究在夏雷拉模型中加入噪声交易者和基本面交易者的非线性需求响应。贝叶斯滤波用于根据多个资产类别的历史价格序列校准模型，同时在模型内部估计基本价值，而非依赖外部估值模型。

据报告，校准后的模型能够再现实证规律，包括过去趋势与后续收益之间的非单调关系。作者还发现，趋势跟踪可能使模型中的错误定价分布由单峰变为双峰，这与市场长期处于高估或低估状态相符。摘录未说明具体资产、滤波细节、估计的不确定性或样本外表现。研究结果描述的是拟合模型的行为，并未证明这些机制能解释每个市场或时期。

核心观点

  • 该模型在异质主体框架中结合了趋势跟踪者、基本面交易者和噪声交易者。
  • 校准过程中估计基本价值，而非由外部定价模型提供。
  • 研究对涵盖多个资产类别的长期价格序列应用了贝叶斯滤波。
  • 模型呈现过去趋势与未来收益之间的非单调关系。
  • 在模型中，趋势跟踪可能导致错误定价呈双峰分布。

标签

趋势跟踪均值回归多资产机器学习统计学
全文
# Co-existence of Trend and Value in Financial Markets: Estimating an Extended Chiarella Model


# Co-existence of Trend and Value in Financial Markets: Estimating an Extended Chiarella Model









Trend and Value are pervasive anomalies, common to all financial markets. We address the problem of their co-existence and interaction within the framework of Heterogeneous Agent Based Models (HABM). More specifically, we extend the Chiarella (1992) model by adding noise traders and a non-linear demand of fundamentalists. We use Bayesian filtering techniques to calibrate the model on time series of prices across a variety of asset classes since 1800. The fundamental value is an output of the calibration, and does not require the use of an external pricing model. Our extended model reproduces many empirical observations, including the non-monotonic relation between past trends and future returns. The destabilizing activity of trend-followers leads to a qualitative change of mispricing distribution, from unimodal to bimodal, meaning that some markets tend to be over- (or under-) valued for long periods of time.

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。