Wavelet-based detection of outliers in volatility models
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Cited by:- Li, Yushu, 2013.
"Wavelet based outlier correction for power controlled turning point detection in surveillance systems,"
Economic Modelling, Elsevier, vol. 30(C), pages 317-321.
- Yushu Li, 2011. "Wavelet Based Outlier Correction for Power Controlled Turning Point Detection in Surveillance Systems," CREATES Research Papers 2011-29, Department of Economics and Business Economics, Aarhus University.
- Li, Yushu, 2012. "Wavelet Based Outlier Correction for Power Controlled Turning Point Detection in Surveillance Systems," Working Papers 2012:12, Lund University, Department of Economics.
- Li, Yushu & Reese, Simon, 2012.
"Wavelet Improvement in Turning Point Detection using a Hidden Markov Model,"
Working Papers
2012:14, Lund University, Department of Economics, revised 05 Apr 2014.
- Li, Yushu & Reese, Simon, 2014. "Wavelet improvement in turning point detection using a Hidden Markov Model," Discussion Papers 2014/10, Norwegian School of Economics, Department of Business and Management Science.
- Yushu Li & Simon Reese, 2014. "Wavelet improvement in turning point detection using a hidden Markov model: from the aspects of cyclical identification and outlier correction," Computational Statistics, Springer, vol. 29(6), pages 1481-1496, December.
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More about this item
Keywords
Outliers;JEL classification:
- C5 - Mathematical and Quantitative Methods - - Econometric Modeling
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
NEP fields
This paper has been announced in the following NEP Reports:- NEP-ECM-2009-02-22 (Econometrics)
- NEP-ETS-2009-02-22 (Econometric Time Series)
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