Learning volatility of discrete time series using prediction with expert advice

Vladimir V. V'yugin

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

In this paper the method of prediction with expert advice is applied for learning volatility of discrete time series. We construct arbitrage strategies (or experts) which suffer gain when "micro" and "macro" volatilities of a time series differ. For merging different expert strategies in a strategy of the learner, we use some modification of Kalai and Vempala algorithm of following the perturbed leader where weights depend on current gains of the experts. We consider the case when experts one-step gains can be unbounded. New notion of a volume of a game vt is introduced. We show that our algorithm has optimal performance in the case when the one-step increments Δvt = vt - vt-1 of the volume satisfy Δvt = o(vt) as t → ∞.

Original languageEnglish
Title of host publicationStochastic Algorithms
Subtitle of host publicationFoundations and Applications - 5th International Symposium, SAGA 2009, Proceedings
Pages16-30
Number of pages15
DOIs
Publication statusPublished - 2009
Externally publishedYes
Event5th Symposium on Stochastic Algorithms, Foundations and Applications, SAGA 2009 - Sapporo, Japan
Duration: 26 Oct 200928 Oct 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5792 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference5th Symposium on Stochastic Algorithms, Foundations and Applications, SAGA 2009
Country/TerritoryJapan
CitySapporo
Period26/10/0928/10/09

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