Department of Economics and Business Economics

Bootstrapping pre-averaged realized volatility under market microstructure noise

Research output: Working paperResearch

Standard

Bootstrapping pre-averaged realized volatility under market microstructure noise. / Hounyo, Ulrich; Goncalves , Sílvia; Meddahi, Nour.

Aarhus : CREATES, Institut for Økonomi, Aarhus Universitet, 2013.

Research output: Working paperResearch

Harvard

Hounyo, U, Goncalves , S & Meddahi, N 2013 'Bootstrapping pre-averaged realized volatility under market microstructure noise' CREATES, Institut for Økonomi, Aarhus Universitet, Aarhus.

APA

Hounyo, U., Goncalves , S., & Meddahi, N. (2013). Bootstrapping pre-averaged realized volatility under market microstructure noise. Aarhus: CREATES, Institut for Økonomi, Aarhus Universitet. CREATES Research Papers, No. 2013-28

CBE

Hounyo U, Goncalves S, Meddahi N. 2013. Bootstrapping pre-averaged realized volatility under market microstructure noise. Aarhus: CREATES, Institut for Økonomi, Aarhus Universitet.

MLA

Hounyo, Ulrich, Sílvia Goncalves and Nour Meddahi Bootstrapping pre-averaged realized volatility under market microstructure noise. Aarhus: CREATES, Institut for Økonomi, Aarhus Universitet. (CREATES Research Papers; Journal number 2013-28). 2013., 37 p.

Vancouver

Hounyo U, Goncalves S, Meddahi N. Bootstrapping pre-averaged realized volatility under market microstructure noise. Aarhus: CREATES, Institut for Økonomi, Aarhus Universitet. 2013 Sep 9.

Author

Hounyo, Ulrich ; Goncalves , Sílvia ; Meddahi, Nour. / Bootstrapping pre-averaged realized volatility under market microstructure noise. Aarhus : CREATES, Institut for Økonomi, Aarhus Universitet, 2013. (CREATES Research Papers; No. 2013-28).

Bibtex

@techreport{ebf78010e446480491f848550b7709b7,
title = "Bootstrapping pre-averaged realized volatility under market microstructure noise",
abstract = "The main contribution of this paper is to propose a bootstrap method for inference on integrated volatility based on the pre-averaging approach of Jacod et al. (2009), where the pre-averaging is done over all possible overlapping blocks of consecutive observations. The overlapping nature of the pre-averaged returns implies that these are kn-dependent with kn growing slowly with the sample size n. This motivates the application of a blockwise bootstrap method. We show that the {"}blocks of blocks{"} bootstrap method suggested by Politis and Romano (1992) (and further studied by B{\"u}hlmann and K{\"u}nsch (1995)) is valid only when volatility is constant. The failure of the blocks of blocks bootstrap is due to the heterogeneity of the squared pre-averaged returns when volatility is stochastic. To preserve both the dependence and the heterogeneity of squared pre-averaged returns, we propose a novel procedure that combines the wild bootstrap with the blocks of blocks bootstrap. We provide a proof of the first order asymptotic validity of this method for percentile intervals. Our Monte Carlo simulations show that the wild blocks of blocks bootstrap improves the finite sample properties of the existing first order asymptotic theory. We use empirical work to illustrate its use in practice.",
keywords = "High frequency data, realized volatility, pre-averaging, market microstructure noise, wild bootstrap, block bootstrap",
author = "Ulrich Hounyo and S{\'i}lvia Goncalves and Nour Meddahi",
year = "2013",
month = "9",
day = "9",
language = "English",
series = "CREATES Research Papers",
publisher = "CREATES, Institut for {\O}konomi, Aarhus Universitet",
number = "2013-28",
type = "WorkingPaper",
institution = "CREATES, Institut for {\O}konomi, Aarhus Universitet",

}

RIS

TY - UNPB

T1 - Bootstrapping pre-averaged realized volatility under market microstructure noise

AU - Hounyo, Ulrich

AU - Goncalves , Sílvia

AU - Meddahi, Nour

PY - 2013/9/9

Y1 - 2013/9/9

N2 - The main contribution of this paper is to propose a bootstrap method for inference on integrated volatility based on the pre-averaging approach of Jacod et al. (2009), where the pre-averaging is done over all possible overlapping blocks of consecutive observations. The overlapping nature of the pre-averaged returns implies that these are kn-dependent with kn growing slowly with the sample size n. This motivates the application of a blockwise bootstrap method. We show that the "blocks of blocks" bootstrap method suggested by Politis and Romano (1992) (and further studied by Bühlmann and Künsch (1995)) is valid only when volatility is constant. The failure of the blocks of blocks bootstrap is due to the heterogeneity of the squared pre-averaged returns when volatility is stochastic. To preserve both the dependence and the heterogeneity of squared pre-averaged returns, we propose a novel procedure that combines the wild bootstrap with the blocks of blocks bootstrap. We provide a proof of the first order asymptotic validity of this method for percentile intervals. Our Monte Carlo simulations show that the wild blocks of blocks bootstrap improves the finite sample properties of the existing first order asymptotic theory. We use empirical work to illustrate its use in practice.

AB - The main contribution of this paper is to propose a bootstrap method for inference on integrated volatility based on the pre-averaging approach of Jacod et al. (2009), where the pre-averaging is done over all possible overlapping blocks of consecutive observations. The overlapping nature of the pre-averaged returns implies that these are kn-dependent with kn growing slowly with the sample size n. This motivates the application of a blockwise bootstrap method. We show that the "blocks of blocks" bootstrap method suggested by Politis and Romano (1992) (and further studied by Bühlmann and Künsch (1995)) is valid only when volatility is constant. The failure of the blocks of blocks bootstrap is due to the heterogeneity of the squared pre-averaged returns when volatility is stochastic. To preserve both the dependence and the heterogeneity of squared pre-averaged returns, we propose a novel procedure that combines the wild bootstrap with the blocks of blocks bootstrap. We provide a proof of the first order asymptotic validity of this method for percentile intervals. Our Monte Carlo simulations show that the wild blocks of blocks bootstrap improves the finite sample properties of the existing first order asymptotic theory. We use empirical work to illustrate its use in practice.

KW - High frequency data, realized volatility, pre-averaging, market microstructure noise, wild bootstrap, block bootstrap

M3 - Working paper

T3 - CREATES Research Papers

BT - Bootstrapping pre-averaged realized volatility under market microstructure noise

PB - CREATES, Institut for Økonomi, Aarhus Universitet

CY - Aarhus

ER -