Correcting spot power variation estimator via Edgeworth expansion

Lidan He (), Qiang Liu (), Zhi Liu () and Andrea Bucci ()
Additional contact information
Lidan He: Nanjing University of Information Science and Technology
Qiang Liu: Shanghai University of Finance and Economics
Zhi Liu: University of Macau
Andrea Bucci: University of Macerata

Metrika: International Journal for Theoretical and Applied Statistics, 2024, vol. 87, issue 8, No 1, 945 pages

Abstract: Abstract In this paper, we propose an estimator of power spot volatility of order p through Edgeworth expansion. We provide a precise description of how to compute the expansion and the first four cumulants are given in an explicit form. We also construct feasible confidence intervals (one-sided and two-sided) for the pth power spot volatility estimator by using Edgeworth expansion. A Monte Carlo simulation study shows that the confidence intervals and probability density curve based on Edgeworth expansion perform better than the conventional case based on Normal approximation.

Keywords: Spot volatility; High-frequency data; Edgeworth expansion; Confidence interval (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s00184-023-00935-z 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:metrik:v:87:y:2024:i:8:d:10.1007_s00184-023-00935-z

Ordering information: This journal article can be ordered from
http://www.springer.com/statistics/journal/184/PS2

DOI: 10.1007/s00184-023-00935-z

Access Statistics for this article

Metrika: International Journal for Theoretical and Applied Statistics is currently edited by U. Kamps and Norbert Henze

More articles in Metrika: International Journal for Theoretical and Applied Statistics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().