Forecasting and Managing Correlation Risks

doi:10.1287/mnsc.2024.08294

Forecasting and Managing Correlation Risks

2026-01-01

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DOI：10.1287/mnsc.2024.08294

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We propose a novel and easy-to-implement framework for forecasting timevarying 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.

Keywords:

correlation forecasting

high-frequency data

LASSO

risk targeting and control

pairs trading

equity premium prediction

Forecasting and Managing Correlation Risks

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