A State Space Approach to Estimating the Integrated Variance and Microstructure Noise Component

Daisuke Nagakura and Toshiaki Watanabe
Additional contact information
Toshiaki Watanabe: Professor, Institute of Economic Research, Hitotsubashi University, and Institute for Monetary and Economic Studies, Bank of Japan (E-mail: watanabe@hit-u.ac.jp, toshiaki.watanabe@boj.or.jp)

No 09-E-11, IMES Discussion Paper Series from Institute for Monetary and Economic Studies, Bank of Japan

Abstract: We call the realized variance (RV) calculated with observed prices contaminated by microstructure noises (MNs) the noise-contaminated RV (NCRV) and refer to the component in the NCRV associated with the MNs as the MN component. This paper develops a method for estimating the integrated variance (IV) and MN component simultaneously, extending the state space method proposed by Barndorff-Nielsen and Shephard (2002). Our extension is based on the result obtained in Meddahi (2003), namely, when the true log-price process follows a general class of continuous-time stochastic volatility (SV) models, the IV follows an ARMA process. We represent the NCRV by a state space form and show that the state space form parameters are not identifiable; however, they can be expressed as functions of fewer identifiable parameters. We illustrate how to estimate these parameters. The proposed method is applied to yen/dollar exchange rate data. We find that the magnitude of the MN component is, on average, about 21%-48 % of the NCRV, depending on the sampling frequency.

Keywords: Realized Variance; Integrated Variance; Microstructure Noise (search for similar items in EconPapers)
JEL-codes: C0 G0 (search for similar items in EconPapers)
Date: 2009-03
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-mst
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Related works:
Working Paper: A State Space Approach to Estimating the Integrated Variance and Microstructure Noise Component (2009)
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