By: |
Goncalves, Silvia;
Hounyo, Ulrich;
Meddahi, Nour |
Abstract: |
The main contribution of this paper is to propose a bootstrap method for
inference on integrated volatility based on the pre-averaging approach, where
the pre-averaging is done over all possible overlapping blocks of consecutive
observations. The overlapping nature of the pre-averaged returns implies that
the leading martingale part in the pre-averaged returns 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 is not valid when volatility is time-varying. 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 and percentile-t 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: |
Block bootstrap, high frequency data, market microstructure noise, preaveraging, realized volatility, wild bootstrap. |
Date: |
2017–05 |
URL: |
http://d.repec.org/n?u=RePEc:ide:wpaper:31734&r=mst |