Large Volatility Matrix Prediction with High-Frequency Data

Xinyu Song

Papers from arXiv.org

Abstract: We provide a novel method for large volatility matrix prediction with high-frequency data by applying eigen-decomposition to daily realized volatility matrix estimators and capturing eigenvalue dynamics with ARMA models. Given a sequence of daily volatility matrix estimators, we compute the aggregated eigenvectors and obtain the corresponding eigenvalues. Eigenvalues in the same relative magnitude form a time series and the ARMA models are further employed to model the dynamics within each eigenvalue time series to produce a predictor. We predict future large volatility matrix based on the predicted eigenvalues and the aggregated eigenvectors, and demonstrate the advantages of the proposed method in volatility prediction and portfolio allocation problems.

Date: 2019-07, Revised 2019-09
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-rmg
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1907.01196

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