Quantile estimation in ultra-high frequency financial data: a comparison between parametric and semiparametric approach

Paola Zuccolotto ()
Additional contact information
Paola Zuccolotto: Universitá degli Studi di Brescia

Statistical Methods & Applications, 2003, vol. 12, issue 2, No 9, 243-257

Abstract: Abstract. In the context of ACD models for ultra-high frequency data different specifications are available to estimate the conditional mean of intertrade durations, while quantiles estimation has been completely neglected by literature, even if to trading extent it can be more informative. The main problem arising with quantiles estimation is the correct specification of durations’ probability law: the usual assumption of Exponentially distributed residuals, is very robust for the estimation of parameters of the conditional mean, but dramatically fails the distributional fit. In this paper a semiparametric approach is formalized, and compared with the parametric one, deriving from Exponential assumption. Empirical evidence for a stock of Italian financial market strongly supports the former approach.

Keywords: Ultra-high frequency data; ACD models; quantiles estimation; distribution free sample random variables (search for similar items in EconPapers)
Date: 2003
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10260-003-0058-y Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:stmapp:v:12:y:2003:i:2:d:10.1007_s10260-003-0058-y

Ordering information: This journal article can be ordered from
http://www.springer. ... cs/journal/10260/PS2

DOI: 10.1007/s10260-003-0058-y

Access Statistics for this article

Statistical Methods & Applications is currently edited by Tommaso Proietti

More articles in Statistical Methods & Applications from Springer, Società Italiana di Statistica
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().