An unbiased autoregressive conditional intraday seasonal variance filtering process
AbstractWe develop a new autoregressive conditional seasonal variance (ARCSV) process that captures both the changes in and the persistency of the intraday seasonal (U-shape) pattern of volatility. Unlike other procedures for seasonality, this approach allows for the intraday volatility pattern to change over time, resulting in an increase in the filtering performance over the extant deterministic filtering models. We quantify the gains in the filtering performance by comparing our model with the flexible Fourier form (FFF) model of Andersen and Bollerslev [ J. Empir. Finance , 1997a, 4 , 115--158]. Moreover, the ARCSV model does not create any statistical distortion in the filtered series, as occurs with other de-seasoning processes. We prove that the ARCSV model satisfies the spectral criteria required to be judged as a good filtering process. Monte Carlo simulation results show that the performance of the ARCSV model is superior to the FFF model. In particular, the seasonal adjustment performance of the ARCSV model is robust under the condition that the innovation of the underlying seasonal variance process is large and the daily non-seasonal variance process is misspecified.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoArticle provided by Taylor & Francis Journals in its journal Quantitative Finance.
Volume (Year): 12 (2012)
Issue (Month): 2 (October)
Contact details of provider:
Web page: http://www.tandfonline.com/RQUF20
You can help add them by filling out this form.
reading list or among the top items on IDEAS.Access and download statisticsgeneral information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Michael McNulty).
If references are entirely missing, you can add them using this form.