| By: |
Ulrich Hounyo (Oxford-Man Institute of Quantitative Finance and CREATES);
Sílvia Goncalves (Département de sciences économiques, CIREQ and CIRANO, Université de Montréal);
Nour Meddahi (Toulouse School of Economics) |
| 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 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. |
| Keywords: |
High frequency data, realized volatility, pre-averaging, market microstructure noise, wild bootstrap, block bootstrap |
| JEL: |
C15 C22 C58 |
| Date: |
2013–08–29 |
| URL: |
http://d.repec.org/n?u=RePEc:aah:create:2013-28&r=mst |