Efficient estimation of the Hurst parameter in high frequency financial data with seasonalities using wavelets

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

S&P 500 Index data taken at one-minute intervals over the course of 11.5 years (January 1989- May 2000) is analyzed, and in particular the Hurst parameter over segments of stationarity (the time period over which the Hurst parameter is almost constant) is estimated. (The segments of stationarity are a byproduct of our analysis, no prior assumption about it is made.) An asymptotically efficient estimator using the log-scale spectrum is employed. This estimator is robust to additive non-stationarities, and it is shown to be robust to multiplicative non-stationarities, i.e. seasonalities, as well. Analyzing cumulative sums of returns, rather than the returns themselves, is essential in removing the effect of seasonalities. It is shown that it is necessary to use wavelets with at least two vanishing moments for the analysis in order to achieve this robustness. This analysis shows that the market has become more efficient since 1997.

Original languageEnglish (US)
Title of host publication2003 IEEE International Conference on Computational Intelligence for Financial Engineering, CIFEr 2003 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages309-316
Number of pages8
ISBN (Electronic)0780376544
DOIs
StatePublished - Jan 1 2003
Event2003 IEEE International Conference on Computational Intelligence for Financial Engineering, CIFEr 2003 - Hong Kong, China
Duration: Mar 20 2003Mar 23 2003

Publication series

NameIEEE/IAFE Conference on Computational Intelligence for Financial Engineering, Proceedings (CIFEr)
Volume2003-January

Other

Other2003 IEEE International Conference on Computational Intelligence for Financial Engineering, CIFEr 2003
CountryChina
CityHong Kong
Period3/20/033/23/03

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Artificial Intelligence
  • Software
  • Applied Mathematics
  • Finance

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