Noise Reduced Realized Volatility: A Kalman Filter Approach

John Owens and Douglas Steigerwald

University of California at Santa Barbara, Economics Working Paper Series from Department of Economics, UC Santa Barbara

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.

Keywords: Realized Volatility; Microstructure Noise; Kalman Filter (search for similar items in EconPapers)
Date: 2009-06-01
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Citations: View citations in EconPapers (6)

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Chapter: Noise reduced realized volatility: a kalman filter approach (2006)
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