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使用GARCH-Itô过程建模隔夜与日内波动率

文章 arXiv papers · 作者: Donggyu Kim et al.
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总结

本文指出波动率建模中的一项缺口：收盘至次日开盘之间没有日内高频数据，因此模型可能遗漏重要的隔夜动态。研究提出GARCH-Itô框架，用不同的瞬时波动率过程分别表示开盘至收盘和收盘至开盘时段，从而纳入交易日的两个部分。

作者使用加权最小二乘法估计两个时段的参数，并考察该方法的渐近性质。研究报告了用于评估有限样本行为的模拟研究，并将该方法应用于真实交易数据。所提供的说明未指出具体资产、实证估计值或与其他模型的比较表现，因此无法据此判断该框架在实践中能在多大程度上改善预测。该研究的贡献在于提出一种建模与估计方法，在纳入日内动态的同时保留隔夜波动率信息。

核心观点

  • 该模型将开盘至收盘与收盘至开盘的波动率过程分开处理。
  • 研究使用加权最小二乘法估计两个时段的参数。
  • 研究考察渐近性质、模拟结果和真实交易数据。
  • 说明中未报告具体实证结果或预测表现的比较优势。

标签

波动率统计学股票
全文
# Overnight GARCH-Itô Volatility Models


# Overnight GARCH-Itô Volatility Models









Various parametric volatility models for financial data have been developed to incorporate high-frequency realized volatilities and better capture market dynamics. However, because high-frequency trading data are not available during the close-to-open period, the volatility models often ignore volatility information over the close-to-open period and thus may suffer from loss of important information relevant to market dynamics. In this paper, to account for whole-day market dynamics, we propose an overnight volatility model based on Itô diffusions to accommodate two different instantaneous volatility processes for the open-to-close and close-to-open periods. We develop a weighted least squares method to estimate model parameters for two different periods and investigate its asymptotic properties. We conduct a simulation study to check the finite sample performance of the proposed model and method. Finally, we apply the proposed approaches to real trading data.

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。