Forecasting and Managing Correlation Risks

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 LASSO technique. Considering the universe of S&P 500 stocks, we find that the new approach manifests in statistically superior out-of-sample forecasts compared to 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.

• 저널Management Science
• 토픽Financial Risk and Volatility Modeling · Finance
• DOI10.1287/mnsc.2024.08294
• 저자Tim Bollerslev, Sophia Zhengzi Li, Yushan Tang