QRFE talk on Forecasting and Managing Correlation Risks

Abstract

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