Forecasting and Managing Correlation Risks

• 成果类型: Article; Early Access
• 署名作者: Bollerslev, Tim; Li, Sophia Zhengzi; Tang, Yushan
• 署名单位: Duke University; National Bureau of Economic Research; Rutgers University System; Rutgers University Newark; Rutgers University New Brunswick; Shanghai University of Finance & Economics
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2024.08294
• 发表日期: 2026
• 关键词: correlation forecasting; high-frequency data; Lasso; risk targeting and control; pairs trading; equity premium prediction

摘要

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.