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Large Volatility Matrix Prediction with High-Frequency Data

Xinyu Song

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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.

New Economics Papers: this item is included in nep-ecm, nep-ets and nep-rmg
Date: 2019-07, Revised 2019-09
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Handle: RePEc:arx:papers:1907.01196