Quantile regression for long memory testing: A case of realized volatility
Paulo Rodrigues (),
Uwe Hassler and
Working Papers from Banco de Portugal, Economics and Research Department
In this paper we derive a quantile regression approach to formally test for long memory in time series. We propose both individual and joint quantile tests which are useful to determine the order of integration along the different percentiles of the conditional distribution and, therefore, allow to address more robustly the overall hypothesis of fractional integration. The null distributions of these tests obey standard laws (e.g., standard normal) and are free of nuisance parameters. The finite sample validity of the approach is established through Monte Carlo simulations, showing, for instance, large power gains over several alternative procedures under non-Gaussian errors. An empirical application of the testing procedure on different measures of daily realized volatility is presented. Our analysis reveals several interesting features, but the main finding is that the suitability of a long-memory model with a constant order of integration around 0.4 cannot be rejected along the different percentiles of the distribution, which provides strong support to the existence of long memory in realized volatility from a completely new perspective.
JEL-codes: C12 C22 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm and nep-ets
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3) Track citations by RSS feed
Downloads: (external link)
Journal Article: Quantile Regression for Long Memory Testing: A Case of Realized Volatility (2016)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:ptu:wpaper:w201207
Access Statistics for this paper
More papers in Working Papers from Banco de Portugal, Economics and Research Department Contact information at EDIRC.
Bibliographic data for series maintained by DEE-NTD ().