Separating Information Maximum Likelihood Estimation of Realized Volatility and Covariance with Micro-Market Noise
For estimating the realized volatility and covariance by using high frequency data, we introduce the Separating Information Maximum Likelihood (SIML) method when there are possibly micro-market noises. The resulting estimator is simple and it has the representation as a specific quadratic form of returns. 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. Based on simulations, we find that the SIML estimator has reasonable finite sample properties and thus it would be useful for practice. It is also possible to use the limiting distribution of the SIML estimator for constructing testing procedures and confidence intervals.
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- Neil Shephard & Ole E. Barndorff-Nielsen, 2006.
"Designing realised kernels to measure the ex-post variation of equity prices in the presence of noise,"
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"How Often to Sample a Continuous-Time Process in the Presence of Market Microstructure Noise,"
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- Yacine Aït-Sahalia, 2005. "How Often to Sample a Continuous-Time Process in the Presence of Market Microstructure Noise," Review of Financial Studies, Society for Financial Studies, vol. 18(2), pages 351-416.
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