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贝叶斯推断与相关熵在模糊条件下的资产定价

文章 arXiv papers · 作者: Farouq Abdulaziz Masoudy
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总结

论文提出一种贝叶斯推断与相关熵技术，用于建模市场信息中的不确定性和模糊性，并评估资产价格。作者将该方法应用于基于消费的资产定价模型，用贝叶斯网络表示消费变化，并利用数据考察价格动态。

报告的动态特征包括顺周期价格偏离、逆周期股权溢价和波动率、杠杆效应，以及超额收益均值回归。作者称，运用该技术建模资产信息能够有效估计市场价格变化。所提供的描述未说明数据、基准方法、估计流程或量化评估，因此仅凭这些内容无法判断其准确性和普适性。

核心观点

  • 所提 BIC 技术结合贝叶斯推断与相关熵，以表示不确定性和模糊性。
  • 基于消费的资产定价模型使用贝叶斯网络表示消费变化。
  • 分析考察价格偏离、股权溢价、波动率、杠杆效应和超额收益均值回归。
  • 论文报告称该方法能有效估计价格变化，但所提供描述未给出指标或基准比较。

标签

统计学机器学习股票波动率均值回归
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# Accurate Evaluation of Asset Pricing Under Uncertainty and Ambiguity of Information


# Accurate Evaluation of Asset Pricing Under Uncertainty and Ambiguity of Information









Since exchange economy considerably varies in the market assets, asset prices have become an attractive research area for investigating and modeling ambiguous and uncertain information in today markets. This paper proposes a new generative uncertainty mechanism based on the Bayesian Inference and Correntropy (BIC) technique for accurately evaluating asset pricing in markets. This technique examines the potential processes of risk, ambiguity, and variations of market information in a controllable manner. We apply the new BIC technique to a consumption asset-pricing model in which the consumption variations are modeled using the Bayesian network model with observing the dynamics of asset pricing phenomena in the data. These dynamics include the procyclical deviations of price, the countercyclical deviations of equity premia and equity volatility, the leverage impact and the mean reversion of excess returns. The key findings reveal that the precise modeling of asset information can estimate price changes in the market effectively.

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。