Department of Economics and Business Economics

Bootstrap inference for pre-averaged realized volatility based on nonoverlapping returns

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

Standard

Bootstrap inference for pre-averaged realized volatility based on nonoverlapping returns. / Gonçalves, Sílvia; Hounyo, Ulrich; Meddahi, Nour.

In: Journal of Financial Econometrics, Vol. 12, No. 4, nbu011, 01.01.2014, p. 679-707.

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

Harvard

Gonçalves, S, Hounyo, U & Meddahi, N 2014, 'Bootstrap inference for pre-averaged realized volatility based on nonoverlapping returns', Journal of Financial Econometrics, vol. 12, no. 4, nbu011, pp. 679-707. https://doi.org/10.1093/jjfinec/nbu011

APA

Gonçalves, S., Hounyo, U., & Meddahi, N. (2014). Bootstrap inference for pre-averaged realized volatility based on nonoverlapping returns. Journal of Financial Econometrics, 12(4), 679-707. [nbu011]. https://doi.org/10.1093/jjfinec/nbu011

CBE

MLA

Gonçalves, Sílvia, Ulrich Hounyo and Nour Meddahi. "Bootstrap inference for pre-averaged realized volatility based on nonoverlapping returns". Journal of Financial Econometrics. 2014, 12(4). 679-707. https://doi.org/10.1093/jjfinec/nbu011

Vancouver

Gonçalves S, Hounyo U, Meddahi N. Bootstrap inference for pre-averaged realized volatility based on nonoverlapping returns. Journal of Financial Econometrics. 2014 Jan 1;12(4):679-707. nbu011. https://doi.org/10.1093/jjfinec/nbu011

Author

Gonçalves, Sílvia ; Hounyo, Ulrich ; Meddahi, Nour. / Bootstrap inference for pre-averaged realized volatility based on nonoverlapping returns. In: Journal of Financial Econometrics. 2014 ; Vol. 12, No. 4. pp. 679-707.

Bibtex

@article{7c1efd56116c420b91229471cdc75bca,
title = "Bootstrap inference for pre-averaged realized volatility based on nonoverlapping returns",
abstract = "The main contribution of this article is to propose bootstrap methods for realized volatility-like estimators defined on pre-averaged returns. In particular, we focus on the pre-averaged realized volatility estimator proposed by Podolskij and Vetter (2009). This statistic can be written (up to a bias correction term) as the (scaled) sum of squared preaveraged returns, where the pre-averaging is done over all possible nonoverlapping blocks of consecutive observations. Pre-averaging reduces the influence of the noise and allows for realized volatility estimation on the pre-averaged returns. The nonoverlapping nature of the pre-averaged returns implies that these are asymptotically uncorrelated, but possibly heteroskedastic. This motivates the application of the wild bootstrap in this context. We provide a proof of the firstorder asymptotic validity of this method for percentile and percentile-t intervals. Our Monte Carlo simulations show that the wild bootstrap can improve the finite sample properties of the existing first-order asymptotic theory provided we choose the external random variable appropriately. We use empirical work to illustrate its use in practice.",
keywords = "High-frequency data, Market microstructure noise, Pre-averaging, Realized volatility, Wild bootstrap",
author = "S{\'i}lvia Gon{\c c}alves and Ulrich Hounyo and Nour Meddahi",
year = "2014",
month = jan,
day = "1",
doi = "10.1093/jjfinec/nbu011",
language = "English",
volume = "12",
pages = "679--707",
journal = "Journal of Financial Econometrics",
issn = "1479-8409",
publisher = "Oxford University Press",
number = "4",

}

RIS

TY - JOUR

T1 - Bootstrap inference for pre-averaged realized volatility based on nonoverlapping returns

AU - Gonçalves, Sílvia

AU - Hounyo, Ulrich

AU - Meddahi, Nour

PY - 2014/1/1

Y1 - 2014/1/1

N2 - The main contribution of this article is to propose bootstrap methods for realized volatility-like estimators defined on pre-averaged returns. In particular, we focus on the pre-averaged realized volatility estimator proposed by Podolskij and Vetter (2009). This statistic can be written (up to a bias correction term) as the (scaled) sum of squared preaveraged returns, where the pre-averaging is done over all possible nonoverlapping blocks of consecutive observations. Pre-averaging reduces the influence of the noise and allows for realized volatility estimation on the pre-averaged returns. The nonoverlapping nature of the pre-averaged returns implies that these are asymptotically uncorrelated, but possibly heteroskedastic. This motivates the application of the wild bootstrap in this context. We provide a proof of the firstorder asymptotic validity of this method for percentile and percentile-t intervals. Our Monte Carlo simulations show that the wild bootstrap can improve the finite sample properties of the existing first-order asymptotic theory provided we choose the external random variable appropriately. We use empirical work to illustrate its use in practice.

AB - The main contribution of this article is to propose bootstrap methods for realized volatility-like estimators defined on pre-averaged returns. In particular, we focus on the pre-averaged realized volatility estimator proposed by Podolskij and Vetter (2009). This statistic can be written (up to a bias correction term) as the (scaled) sum of squared preaveraged returns, where the pre-averaging is done over all possible nonoverlapping blocks of consecutive observations. Pre-averaging reduces the influence of the noise and allows for realized volatility estimation on the pre-averaged returns. The nonoverlapping nature of the pre-averaged returns implies that these are asymptotically uncorrelated, but possibly heteroskedastic. This motivates the application of the wild bootstrap in this context. We provide a proof of the firstorder asymptotic validity of this method for percentile and percentile-t intervals. Our Monte Carlo simulations show that the wild bootstrap can improve the finite sample properties of the existing first-order asymptotic theory provided we choose the external random variable appropriately. We use empirical work to illustrate its use in practice.

KW - High-frequency data

KW - Market microstructure noise

KW - Pre-averaging

KW - Realized volatility

KW - Wild bootstrap

U2 - 10.1093/jjfinec/nbu011

DO - 10.1093/jjfinec/nbu011

M3 - Journal article

AN - SCOPUS:84911493911

VL - 12

SP - 679

EP - 707

JO - Journal of Financial Econometrics

JF - Journal of Financial Econometrics

SN - 1479-8409

IS - 4

M1 - nbu011

ER -