State Space Model of Realized Volatility under the Existence of Dependent Market Microstructure Noise

TL;DR

This paper extends a state space model for realized volatility to account for dependent market microstructure noise, improving estimation accuracy using simulation and real data analysis.

Contribution

It introduces a model that considers autocorrelated and correlated microstructure noise, extending previous models and providing empirical validation.

Findings

Dependent microstructure noise affects realized volatility estimates.

The extended model improves estimation accuracy in simulations.

Empirical results demonstrate the model's effectiveness with real data.

Abstract

Volatility means the degree of variation of a stock price which is important in finance. Realized Volatility (RV) is an estimator of the volatility calculated using high-frequency observed prices. RV has lately attracted considerable attention of econometrics and mathematical finance. However, it is known that high-frequency data includes observation errors called market microstructure noise (MN). Nagakura and Watanabe[2015] proposed a state space model that resolves RV into true volatility and influence of MN. In this paper, we assume a dependent MN that autocorrelates and correlates with return as reported by Hansen and Lunde[2006] and extends the results of Nagakura and Watanabe[2015] and compare models by simulation and actual data.