Matrix-based Prediction Approach for Intraday Instantaneous Volatility Vector

39 Pages Posted: 1 Apr 2024

See all articles by Sung Hoon Choi

Sung Hoon Choi

University of Connecticut

Donggyu Kim

College of Business, Korea Advanced Institute of Science and Technology (KAIST)

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Date Written: March 5, 2024

Abstract

In this paper, we introduce a novel method for predicting intraday instantaneous volatility based on Itô 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.

Keywords: Diffusion process, high-frequency financial data, low-rank matrix, semiparametric factor models

JEL Classification: C38, C58

Suggested Citation

Choi, Sung Hoon and Kim, Donggyu, Matrix-based Prediction Approach for Intraday Instantaneous Volatility Vector (March 5, 2024). Available at SSRN: https://ssrn.com/abstract=4747889 or http://dx.doi.org/10.2139/ssrn.4747889

Sung Hoon Choi (Contact Author)

University of Connecticut ( email )

Donggyu Kim

College of Business, Korea Advanced Institute of Science and Technology (KAIST) ( email )

85 Hoegiro Dongdaemun-Gu
Seoul 02455
Korea, Republic of (South Korea)

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