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Estimating and forecasting dynamic correlation matrices: A nonlinear common factor approach

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  • Zhang, Yongli
  • Rolling, Craig
  • Yang, Yuhong

Abstract

In economic and business data, the correlation matrix is a stochastic process that fluctuates over time and exhibits seasonality. The most widely-used approaches for estimating and forecasting the correlation matrix (e.g., multivariate GARCH) often are hindered by computational difficulties and require strong assumptions. In this paper we propose a method for modeling and forecasting correlation matrices that allows the correlation to be driven nonlinearly by common factors. Our nonlinear common factor (NCF) method simplifies estimation and provides more flexibility than previous factor-based methods. We illustrate its use on energy prices in Boston.

Suggested Citation

  • Zhang, Yongli & Rolling, Craig & Yang, Yuhong, 2021. "Estimating and forecasting dynamic correlation matrices: A nonlinear common factor approach," Journal of Multivariate Analysis, Elsevier, vol. 183(C).
    • Handle: RePEc:eee:jmvana:v:183:y:2021:i:c:s0047259x20302918
    DOI: 10.1016/j.jmva.2020.104710
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    References listed on IDEAS

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    Cited by:

    1. Alejandro Rodriguez Dominguez, 2025. "Causal Portfolio Optimization: Principles and Sensitivity-Based Solutions," Papers 2504.05743, arXiv.org, revised Apr 2025.

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