Multiscale Inference for High-Frequency Data

TL;DR

This paper introduces a multiscale estimator for integrated volatility that effectively corrects bias caused by market microstructure noise, improving accuracy in high-frequency financial data analysis.

Contribution

The paper presents a novel multiscale bias correction method for integrated volatility estimation that accounts for correlated observation errors and enhances existing techniques.

Findings

Improved accuracy over existing methods in simulations

Effective bias correction using multiscale ratio estimation

Method applicable to models like Heston with high precision

Abstract

This paper proposes a novel multiscale estimator for the integrated volatility of an Ito process, in the presence of market microstructure noise (observation error). The multiscale structure of the observed process is represented frequency-by-frequency and the concept of the multiscale ratio is introduced to quantify the bias in the realized integrated volatility due to the observation error. The multiscale ratio is estimated from a single sample path, and a frequency-by-frequency bias correction procedure is proposed, which simultaneously reduces variance. We extend the method to include correlated observation errors and provide the implied time domain form of the estimation procedure. The new method is implemented to estimate the integrated volatility for the Heston and other models, and the improved performance of our method over existing methods is illustrated by simulation studies.