English, Article, Journal or magazine article edition: A State Space Approach to Estimating the Integrated Variance and Microstructure Noise Component Daisuke Nagakura; Toshiaki Watanabe

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Bookmark: http://trove.nla.gov.au/version/35352
Physical Description
  • preprint
Language
  • English

Edition details

Title
  • A State Space Approach to Estimating the Integrated Variance and Microstructure Noise Component
Author
  • Daisuke Nagakura
  • Toshiaki Watanabe
Physical Description
  • preprint
Notes
  • 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 state space method for estimating the integrated variance (IV) and MN component simultaneously. 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.
  • Realized Variance, Integrated Variance, Microstructure Noise, State Space, Identification, Exchange Rate
  • RePEc:hst:ghsdps:gd09-055
  • 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.
  • RePEc:ime:imedps:09-e-11
Language
  • English
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