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

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. (C) 2020 Elsevier Inc. All rights reserved.

Automated summary

In this paper, a new method is proposed for modeling and forecasting correlation matrices, allowing correlations to be nonlinearly driven by common factors. The nonlinear common factor (NCF) method simplifies estimation and provides more flexibility than previous methods. This method is demonstrated using energy prices in Boston.