Beta is used in many applications, ranging from asset pricing tests to cost of capital estimation, investment management and risk management. Beta needs to be estimated, and shrinkage to its cross-sectional average value of 1 is often applied to reduce estimation error. Since beta is the product of the return correlation of a security with the market and its return volatility relative to that of the market, we shrink correlation and volatility separately and evaluate the predictive power of this approach. We find economically and statistically significant gains from applying more shrinkage to correlations than to volatilities.