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. This 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 our proposal is tested by an intensive Monte Carlo study
for six well known volatility models and compared to alternative proposals in the literature,
before applying it to three daily stock market indexes. The Monte Carlo experiments show that
our method is both very effective in detecting isolated outliers and outlier patches and much
more reliable than other wavelet-based procedures since it detects a significant smaller number
of false outliers
