Separating Information Maximum Likelihood Estimation of Realized Volatility and Covariance with Micro-Market Noise

Naoto Kunitomo and Seisho Sato
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Naoto Kunitomo: Faculty of Economics, University of Tokyo
Seisho Sato: Institute of Statistical Mathematics

No CIRJE-F-581, CIRJE F-Series from CIRJE, Faculty of Economics, University of Tokyo

Abstract: 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.

Pages: 28pages
Date: 2008-08
New Economics Papers: this item is included in nep-ecm and nep-mst
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Citations: View citations in EconPapers (17)

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