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Wavelet-based detection of outliers in financial time series

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  • Grané, Aurea
  • Veiga, Helena

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.

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Bibliographic Info

Article provided by Elsevier in its journal Computational Statistics & Data Analysis.

Volume (Year): 54 (2010)
Issue (Month): 11 (November)
Pages: 2580-2593

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Handle: RePEc:eee:csdana:v:54:y:2010:i:11:p:2580-2593

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Web page: http://www.elsevier.com/locate/csda

Related research

Keywords: Outliers Outlier patches Prediction intervals Volatility models Wavelets;

References

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  1. Seth A. Greenblatt, 1994. "Wavelets in Econometrics: An Application to Outlier Testing," Econometrics 9410001, EconWPA.
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Citations

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Cited by:
  1. Aurea Grané & Belén Martín-Barragán & Helena Veiga, 2014. "Outliers in multivariate Garch models," Statistics and Econometrics Working Papers ws140503, Universidad Carlos III, Departamento de Estadística y Econometría.
  2. Aurea Grané & Helena Veiga, 2010. "Outliers in Garch models and the estimation of risk measures," Statistics and Econometrics Working Papers ws100502, Universidad Carlos III, Departamento de Estadística y Econometría.
  3. Carnero, M. Angeles & Peña, Daniel & Ruiz, Esther, 2012. "Estimating GARCH volatility in the presence of outliers," Economics Letters, Elsevier, vol. 114(1), pages 86-90.
  4. Oleg Shirokikh & Grigory Pastukhov & Vladimir Boginski & Sergiy Butenko, 2013. "Computational study of the US stock market evolution: a rank correlation-based network model," Computational Management Science, Springer, vol. 10(2), pages 81-103, June.
  5. Gallegati, Marco & Ramsey, James B. & Semmler, Willi, 2014. "Interest rate spreads and output: A time scale decomposition analysis using wavelets," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 283-290.

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