Deep learning for multivariate volatility forecasting in high-dimensional financial time series

Rei Iwafuchi and Yasumasa Matsuda

No 141, DSSR Discussion Papers from Graduate School of Economics and Management, Tohoku University

Abstract: The market for investment trusts of large-scale portfolios, including index funds, continues to grow, and high-dimensional volatility estimation is essential for assessing the risks of such portfolios. However, multivariate volatility models suitable for high-dimensional data have not been extensively studied. This paper introduces a new framework based on the Spatial AR model, which provides fast and stable estimation, and demonstrates its application through simulations using historical data from the S&P 500.

Pages: 9 pages
Date: 2024-05
New Economics Papers: this item is included in nep-cmp, nep-ecm and nep-rmg
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10097/0002001327

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:toh:dssraa:141

Access Statistics for this paper

More papers in DSSR Discussion Papers from Graduate School of Economics and Management, Tohoku University Contact information at EDIRC.
Bibliographic data for series maintained by Tohoku University Library ().