Noise reduced realized volatility: a kalman filter approach

John P. Owens and Douglas Steigerwald

A chapter in Econometric Analysis of Financial and Economic Time Series, 2006, pp 211-227 from Emerald Group Publishing Limited

Abstract: Microstructure noise contaminates high-frequency estimates of asset price volatility. Recent work has determined a preferred sampling frequency under the assumption that the properties of noise are constant. Given the sampling frequency, the high-frequency observations are given equal weight. While convenient, constant weights are not necessarily efficient. We use the Kalman filter to derive more efficient weights, for any given sampling frequency. We demonstrate the efficacy of the procedure through an extensive simulation exercise, showing that our filter compares favorably to more traditional methods.

Date: 2006
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.emerald.com/insight/content/doi/10.101 ... d&utm_campaign=repec (text/html)
https://www.emerald.com/insight/content/doi/10.101 ... d&utm_campaign=repec (application/pdf)
Access to full text is restricted to subscribers

Related works:
Working Paper: Noise Reduced Realized Volatility: A Kalman Filter Approach (2009)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eme:aecozz:s0731-9053(05)20008-7

DOI: 10.1016/S0731-9053(05)20008-7

Access Statistics for this chapter

More chapters in Advances in Econometrics from Emerald Group Publishing Limited
Bibliographic data for series maintained by Emerald Support ().