Forecasting and Managing Correlation Risks
Tim Bollerslev, Sophia Zhengzi Li, Yushan Tang
Management Science · Fri
Abstract
We propose a novel and easy-to-implement framework for forecasting time-varying correlations based on a large set of salient realized correlation features and the sparsity-encouraging Least Absolute Shrinkage and Selection Operator technique. Considering the universe of S&P 500 stocks, we find that the new approach manifests in statistically superior out-of-sample forecasts compared with commonly used procedures. We further demonstrate how the forecasts translate into significant economic gains in the form of higher pairs trading profits, better equity premium predictions, more accurate portfolio risk targeting, and superior overall risk control and minimization. This paper was accepted by Kay Giesecke, finance. Funding: S. Z. Li received financial support from the Rutgers Business School Dean’s Research Seed Fund. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2024.08294 .
Keywords
correlation forecasting, high-frequency data, LASSO, risk targeting and control, pairs trading, equity premium prediction
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