Estimation of Integrated Volatility Functionals with Kernel Spot Volatility Estimators

TL;DR

This paper develops advanced kernel-based estimators for integrated volatility functionals in multidimensional Itô semimartingales, demonstrating improved bias reduction and robustness over traditional methods.

Contribution

It introduces a general kernel spot volatility estimator with bias correction, achieving optimal convergence rates and robustness in estimating integrated volatility functionals.

Findings

Bias can be significantly reduced using general kernels.

The estimators maintain robustness across different sampling frequencies.

Bias correction improves the accuracy of volatility functional estimates.

Abstract

For a multidimensional It\^o semimartingale, we consider the problem of estimating integrated volatility functionals. Jacod and Rosenbaum (2013) studied a plug-in type of estimator based on a Riemann sum approximation of the integrated functional and a spot volatility estimator with a forward uniform kernel. Motivated by recent results that show that spot volatility estimators with general two-side kernels of unbounded support are more accurate, in this paper, an estimator using a general kernel spot volatility estimator as the plug-in is considered. A biased central limit theorem for estimating the integrated functional is established with an optimal convergence rate. Unbiased central limit theorems for estimators with proper de-biasing terms are also obtained both at the optimal convergence regime for the bandwidth and when applying undersmoothing. Our results show that one can significantly reduce the estimator's bias by adopting a general kernel instead of the standard uniform kernel. Our proposed bias-corrected estimators are found to maintain remarkable robustness against bandwidth selection in a variety of sampling frequencies and functions.