Department of Economics and Business Economics

Assessing relative volatility/intermittency/energy dissipation

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

Standard

Assessing relative volatility/intermittency/energy dissipation. / Barndorff-Nielsen, Ole E.; Pakkanen, Mikko S.; Schmiegel, Jürgen.

In: Electronic Journal of Statistics, Vol. 8, No. 2, 2014, p. 1996-2021.

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

Harvard

Barndorff-Nielsen, OE, Pakkanen, MS & Schmiegel, J 2014, 'Assessing relative volatility/intermittency/energy dissipation', Electronic Journal of Statistics, vol. 8, no. 2, pp. 1996-2021. https://doi.org/10.1214/14-EJS942

APA

Barndorff-Nielsen, O. E., Pakkanen, M. S., & Schmiegel, J. (2014). Assessing relative volatility/intermittency/energy dissipation. Electronic Journal of Statistics, 8(2), 1996-2021. https://doi.org/10.1214/14-EJS942

CBE

MLA

Vancouver

Author

Barndorff-Nielsen, Ole E. ; Pakkanen, Mikko S. ; Schmiegel, Jürgen. / Assessing relative volatility/intermittency/energy dissipation. In: Electronic Journal of Statistics. 2014 ; Vol. 8, No. 2. pp. 1996-2021.

Bibtex

@article{8aa908a65616460cac06752d4e56e8cd,
title = "Assessing relative volatility/intermittency/energy dissipation",
abstract = "We introduce the notion of relative volatility/intermittency and demonstrate how relative volatility statistics can be used to estimate consistently the temporal variation of volatility/intermittency when the data of interest are generated by a non-semimartingale, or a Brownian semistationary process in particular. This estimation method is motivated by the assessment of relative energy dissipation in empirical data of turbulence, but it is also applicable in other areas. We develop a probabilistic asymptotic theory for realised relative power variations of Brownian semistationary processes, and introduce inference methods based on the theory. We also discuss how to extend the asymptotic theory to other classes of processes exhibiting stochastic volatility/intermittency. As an empirical application, we study relative energy dissipation in data of atmospheric turbulence. ",
keywords = "Brownian semistationary process, Energy dissipation, Intermittency, Power variation, Turbulence, Volatility",
author = "Barndorff-Nielsen, {Ole E.} and Pakkanen, {Mikko S.} and J{\"u}rgen Schmiegel",
note = "Campus adgang til artiklen / Campus access to the article",
year = "2014",
doi = "10.1214/14-EJS942",
language = "English",
volume = "8",
pages = "1996--2021",
journal = "Electronic Journal of Statistics",
issn = "1935-7524",
publisher = "nstitute of Mathematical Statistics",
number = "2",

}

RIS

TY - JOUR

T1 - Assessing relative volatility/intermittency/energy dissipation

AU - Barndorff-Nielsen, Ole E.

AU - Pakkanen, Mikko S.

AU - Schmiegel, Jürgen

N1 - Campus adgang til artiklen / Campus access to the article

PY - 2014

Y1 - 2014

N2 - We introduce the notion of relative volatility/intermittency and demonstrate how relative volatility statistics can be used to estimate consistently the temporal variation of volatility/intermittency when the data of interest are generated by a non-semimartingale, or a Brownian semistationary process in particular. This estimation method is motivated by the assessment of relative energy dissipation in empirical data of turbulence, but it is also applicable in other areas. We develop a probabilistic asymptotic theory for realised relative power variations of Brownian semistationary processes, and introduce inference methods based on the theory. We also discuss how to extend the asymptotic theory to other classes of processes exhibiting stochastic volatility/intermittency. As an empirical application, we study relative energy dissipation in data of atmospheric turbulence.

AB - We introduce the notion of relative volatility/intermittency and demonstrate how relative volatility statistics can be used to estimate consistently the temporal variation of volatility/intermittency when the data of interest are generated by a non-semimartingale, or a Brownian semistationary process in particular. This estimation method is motivated by the assessment of relative energy dissipation in empirical data of turbulence, but it is also applicable in other areas. We develop a probabilistic asymptotic theory for realised relative power variations of Brownian semistationary processes, and introduce inference methods based on the theory. We also discuss how to extend the asymptotic theory to other classes of processes exhibiting stochastic volatility/intermittency. As an empirical application, we study relative energy dissipation in data of atmospheric turbulence.

KW - Brownian semistationary process

KW - Energy dissipation

KW - Intermittency

KW - Power variation

KW - Turbulence

KW - Volatility

U2 - 10.1214/14-EJS942

DO - 10.1214/14-EJS942

M3 - Journal article

VL - 8

SP - 1996

EP - 2021

JO - Electronic Journal of Statistics

JF - Electronic Journal of Statistics

SN - 1935-7524

IS - 2

ER -