Efficient asymptotic variance reduction when estimating volatility in high frequency data

TL;DR

This paper introduces a method for reducing the asymptotic variance in volatility estimation from high-frequency data by block-wise aggregation of realized kernels and QMLE, improving efficiency in practical scenarios.

Contribution

It proposes a novel block aggregation approach to enhance asymptotic efficiency of volatility estimators in high-frequency data analysis.

Findings

Variance ratio approaches 1 as blocks increase.

Method performs well with stochastic sampling and jumps.

Empirical results show practical efficiency gains.

Abstract

This paper shows how to carry out efficient asymptotic variance reduction when estimating volatility in the presence of stochastic volatility and microstructure noise with the realized kernels (RK) from [Barndorff-Nielsen et al., 2008] and the quasi-maximum likelihood estimator (QMLE) studied in [Xiu, 2010]. To obtain such a reduction, we chop the data into B blocks, compute the RK (or QMLE) on each block, and aggregate the block estimates. The ratio of asymptotic variance over the bound of asymptotic efficiency converges as B increases to the ratio in the parametric version of the problem, i.e. 1.0025 in the case of the fastest RK Tukey-Hanning 16 and 1 for the QMLE. The impact of stochastic sampling times and jump in the price process is examined carefully. The finite sample performance of both estimators is investigated in simulations, while empirical work illustrates the gain in practice.