Tracking of Historical Volatility

dc.creatorGoldentayer, L.
dc.creatorKlebaner, F.
dc.creatorLiptser, R.
dc.date2004-04-15
dc.date.accessioned2026-07-07T08:06:15Z
dc.date.available2026-07-07T08:06:15Z
dc.descriptionWe propose an adaptive algorithm for tracking of historical volatility. The algorithm is built under the assumption that the historical volatility function belongs to the Stone-Ibragimov-Khasminskii class of $k$ times differentiable functions with bounded highest derivative and its subclass of functions satisfying a differential inequalities. We construct an estimator of the Kalman filter type and show optimality of the estimator's convergence rate to zero as sample size $n\to\infty$. This estimator is in the framework of GARCH design, but a tuning procedure of its parameters is faster than with traditional GARCH techniques.
dc.description20 pages, 4 figures
dc.identifierhttps://arxiv.org/abs/math/0404277
dc.identifierhttp://arxiv.org/abs/math/0404277
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/130542
dc.subjectProbability
dc.subjectOptimization and Control
dc.subjectStatistics Theory
dc.subject60G35, 60G51, 62G05, 62M20, 91B70
dc.titleTracking of Historical Volatility
dc.typetext

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