Abstract
We propose an adaptive algorithm for tracking historical volatility. The algorithm borrows ideas from nonparametric statistics. In particular, we assume that the volatility is a several times differentiable function with a bounded highest derivative. We propose an adaptive algorithm with a Kalman filter structure, which guarantees the same asymptotics (well known from statistical inference) with respect to the sample size n, n → ∞. The tuning procedure for this filter is simpler than for a GARCH filter.
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Translated from Problemy Peredachi Informatsii, No. 3, 2005, pp. 32–50.
Original Russian Text Copyright © 2005 by Goldentayer, Klebaner, Liptser.
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Goldentayer, L., Klebaner, F. & Liptser, R.S. Tracking Volatility. Probl Inf Transm 41, 212–229 (2005). https://doi.org/10.1007/s11122-005-0026-2
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DOI: https://doi.org/10.1007/s11122-005-0026-2