Estimating Jump Activity Using Multipower VariationCitation formats

Standard

Estimating Jump Activity Using Multipower Variation. / Kolokolov, Aleksey.

In: Journal of Business and Economic Statistics, 25.06.2020.

Research output: Contribution to journal › Article › peer-review

Harvard

Kolokolov, A 2020, 'Estimating Jump Activity Using Multipower Variation', Journal of Business and Economic Statistics. https://doi.org/10.1080/07350015.2020.1784745

APA

Kolokolov, A. (2020). Estimating Jump Activity Using Multipower Variation. Journal of Business and Economic Statistics. https://doi.org/10.1080/07350015.2020.1784745

Vancouver

Kolokolov A. Estimating Jump Activity Using Multipower Variation. Journal of Business and Economic Statistics. 2020 Jun 25. https://doi.org/10.1080/07350015.2020.1784745

Author

Kolokolov, Aleksey. / Estimating Jump Activity Using Multipower Variation. In: Journal of Business and Economic Statistics. 2020.

Bibtex

@article{bb638ab14442442fbd2cd03d74dd7979,
title = "Estimating Jump Activity Using Multipower Variation",
abstract = "Realized multipower variation, originally introduced to eliminate jumps, can be extremely useful for infer- ence in pure-jump models. This article shows how to build a simple and precise estimator of the jump activity index of a semimartingale observed at a high frequency by comparing different multipowers. The novel methodology allows to infer whether a discretely observed process contains a continuous martingale component. The empirical part of the article undertakes a nonparametric analysis of the jump activity of bitcoin and shows that bitcoin is a pure jump process with high jump activity, which is critically different from conventional currencies that include a Brownian motion component.",
keywords = "bitcoin, Jump activity, Multipower variation, High-frequency data, Jumps",
author = "Aleksey Kolokolov",
year = "2020",
month = jun,
day = "25",
doi = "10.1080/07350015.2020.1784745",
language = "English",
journal = "Journal of Business and Economic Statistics",
issn = "0735-0015",
publisher = "American Statistical Association",

}

RIS

TY - JOUR

T1 - Estimating Jump Activity Using Multipower Variation

AU - Kolokolov, Aleksey

PY - 2020/6/25

Y1 - 2020/6/25

N2 - Realized multipower variation, originally introduced to eliminate jumps, can be extremely useful for infer- ence in pure-jump models. This article shows how to build a simple and precise estimator of the jump activity index of a semimartingale observed at a high frequency by comparing different multipowers. The novel methodology allows to infer whether a discretely observed process contains a continuous martingale component. The empirical part of the article undertakes a nonparametric analysis of the jump activity of bitcoin and shows that bitcoin is a pure jump process with high jump activity, which is critically different from conventional currencies that include a Brownian motion component.

AB - Realized multipower variation, originally introduced to eliminate jumps, can be extremely useful for infer- ence in pure-jump models. This article shows how to build a simple and precise estimator of the jump activity index of a semimartingale observed at a high frequency by comparing different multipowers. The novel methodology allows to infer whether a discretely observed process contains a continuous martingale component. The empirical part of the article undertakes a nonparametric analysis of the jump activity of bitcoin and shows that bitcoin is a pure jump process with high jump activity, which is critically different from conventional currencies that include a Brownian motion component.

KW - bitcoin

KW - Jump activity

KW - Multipower variation

KW - High-frequency data

KW - Jumps

U2 - 10.1080/07350015.2020.1784745

DO - 10.1080/07350015.2020.1784745

M3 - Article

JO - Journal of Business and Economic Statistics

JF - Journal of Business and Economic Statistics

SN - 0735-0015

ER -