基于矩阵的日内瞬时波动率向量预测方法
Matrix-based Prediction Approach for Intraday Instantaneous Volatility Vector
AI总结：
针对高频金融数据的日内瞬时波动率预测，本文构建可分解为低秩条件期望与噪声的日间×日内波动率矩阵，并提出TIP-PCA方法，证明其渐近性质且通过模拟与S&P 500数据验证。
摘要：
本文提出一种利用高频金融数据、基于Itô半鞅模型预测日内瞬时波动率的新方法。已有多项研究强调了波动率时间序列的典型特征，例如日间自回归动态和日内U形模式。为容纳这些波动率特征，我们提出一个日间×日内瞬时波动率矩阵过程，该过程可分解为低秩条件期望瞬时波动率矩阵与噪声矩阵。为预测低秩条件期望瞬时波动率矩阵，我们提出双侧投影PCA（Two-sIde Projected-PCA，TIP-PCA）程序。我们建立了所提估计量的渐近性质，并开展模拟研究以评估该预测方法的有限样本表现。最后，我们使用来自S&P 500指数和11只行业指数基金的高频数据，将TIP-PCA方法应用于样本外瞬时波动率向量预测研究。
英文摘要：
In this paper, we introduce a novel method for predicting intraday instantaneous volatility based on Ito semimartingale models using high-frequency financial data. Several studies have highlighted stylized volatility time series features, such as interday auto-regressive dynamics and the intraday U-shaped pattern. To accommodate these volatility features, we propose an interday-by-intraday instantaneous volatility matrix process that can be decomposed into low-rank conditional expected instantaneous volatility and noise matrices. To predict the low-rank conditional expected instantaneous volatility matrix, we propose the Two-sIde Projected-PCA (TIP-PCA) procedure. We establish asymptotic properties of the proposed estimators and conduct a simulation study to assess the finite sample performance of the proposed prediction method. Finally, we apply the TIP-PCA method to an out-of-sample instantaneous volatility vector prediction study using high-frequency data from the S&P 500 index and 11 sector index funds.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
