EconPapers    
Economics at your fingertips  
 

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.

New Economics Papers: this item is included in nep-ecm, nep-ets and nep-rmg
Date: 2019-07, Revised 2019-09
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed

Downloads: (external link)
http://arxiv.org/pdf/1907.01196 Latest version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1907.01196

Access Statistics for this paper

More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().

 
Page updated 2019-09-26
Handle: RePEc:arx:papers:1907.01196