There are many approaches for estimating an integrated variance and covariance in the presence of market microstructure noise. It is important to know a dependence of noise to construct the integrated variance and covariance estimators. We study a time dependence of bivariate noise processes in...

The cumulative covariance estimator in Hayashi and Yoshida (2005) which suits for non-synchronous observations possibly has a bias in the presence of the observational noise. We propose the test statistic to detect whether the observational noise causes a measurable bias in the estimator of...

The cumulative covariance estimator in Hayashi and Yoshida (2005b) which suits for non-synchronous observations possibly has a bias in the presence of the observational noise. We propose the test statistic to detect whether the observational noise causes a measurable bias in the estimator of...

Structural change is gauged with the change of parameters in the model. In the case of multiple time series model, the causality between the time series also changes when there is a structural change. However the magnitude of change in causality is not clear in the case of structural change. We...

The aim of this study is to develop a bias-correction method for realized variance (RV) estimation, where the equilibrium price process is contaminated with market microstructure noise, such as bid-ask bounces and price changes discreteness. Though RV constitutes the simplest estimator of daily...

This paper presents an examination of the relation between pre-trade transparency and market quality in the Tokyo Stock Exchange (TSE). Mixed evidence related to this relation has been reported worldwide. We analyzed this relation using a discrete change of disclosure policy in the 2000s. A...

The objective of this paper is to examine effects of realized covariance matrix estimators based on intraday returns on large-scale minimum-variance equity portfolio optimization. We empirically assess out-of-sample performance of portfolios with different covariance matrix estimators: the...