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Outliers in financial data can lead to model parameter estimation biases, invalid inferences and poor volatility forecasts. Therefore, their detection and correction should be taken seriously when modeling financial data. The present paper focuses on these issues and proposes a general detection and correction method based on wavelets that can be applied to a large class of volatility models. The effectiveness of the new proposal is tested by an intensive Monte Carlo study for six well-known volatility models and compared to alternative proposals in the literature, before it is applied to three daily stock market indices. The Monte Carlo experiments show that the new method is both very effective in detecting isolated outliers and outlier patches and much more reliable than other alternatives, since it detects a significantly smaller number of false outliers. Correcting the data of outliers reduces the skewness and the excess kurtosis of the return series distributions and allows for more accurate return prediction intervals compared to those obtained when the existence of outliers is ignored.