Wavelet-Based Detection of Outliers in Financial Time Series Articles uri icon

publication date

  • November 2010

start page

  • 2580

end page

  • 2593

issue

  • 11

volume

  • 54

international standard serial number (ISSN)

  • 0167-9473

electronic international standard serial number (EISSN)

  • 1872-7352

abstract

  • 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.