A0829
Title: Matrix-based prediction approach for intraday instantaneous volatility vector
Authors: Sung Hoon Choi - University of Connecticut (United States) [presenting]
Donggyu Kim - KAIST (Korea, South)
Abstract: A novel method is introduced 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, an interday-by-intraday instantaneous volatility matrix process is proposed that can be decomposed into low-rank conditional expected instantaneous volatility and noise matrices. To predict the low-rank conditional expected instantaneous volatility matrix, the Two-sIde Projected-PCA (TIP-PCA) procedure is proposed. Asymptotic properties of the proposed estimators are established, and a simulation study is conducted to assess the finite sample performance of the proposed prediction method. Finally, the TIP-PCA method is applied 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.
