For estimating the realized volatility and covariance by using high frequency data, Kunitomo and Sato (2008a,b) have proposed the Separating Information Maximum Likelihood (SIML) method when there are micro-market noises. The SIML estimator has reasonable asymptotic properties; it is consistent and it has the asymptotic normality (or the stable convergence in the general case) when the sample size is large under general conditions including non-Gaussian processes and volatility models. We also show that the SIML estimator has the asymptotic robustness in the sense that it is consistent and it has the asymptotic normality when there are autocorrelations in the market noise terms and there are endogenous correlations between the signal and noise terms.