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We find that combining two important predictors, stock market implied volatility and oil volatility, can improve the predictability of stock return volatility. We also document that the stock market implied volatility provides far more significant predictability than the oil volatility and other nonoil macroeconomic and financial variables. The empirical results show the “kitchen sink” combination approach that using two predictors jointly performs better than not only the univariate regression models which use oil volatility or stock market implied volatility separately but also convex combination of the individual forecasts. This improvement of predictability is also remarkable when we consider the business cycle. Furthermore, the robust test based on different lag lengths and different macroinformation shows that our forecasting strategy is efficient.