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

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  • Rei Iwafuchi
  • Yasumasa Matsuda

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.

Suggested Citation

  • Rei Iwafuchi & Yasumasa Matsuda, 2024. "Deep learning for multivariate volatility forecasting in high-dimensional financial time series," DSSR Discussion Papers 141, Graduate School of Economics and Management, Tohoku University.
    • Handle: RePEc:toh:dssraa:141
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    File URL: http://hdl.handle.net/10097/0002001327
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