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Markov Switching in GARCH Processes and Mean-Reverting Stock-Market Volatility

Bibliographic Data

ID19418583
AuthorsMichael J Dueker (Federal Reserve Bank of St. Louis, corresponding author)
Year1997
Volume15
Issue1
Pages26-34
Publication date1997-01-01
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenueJournal of Business and Economic Statistics (JOURNAL)
Journal identifiersISSN: 0735-0015 • E-ISSN: 1537-2707
PublisherInforma UK Limited (PUBLISHER • GB)
DOI10.1080/07350015.1997.10524683
OpenAlexW2068413413
LanguageEN
Citations received8
References cited15

This article introduces four models of conditional heteroscedasticity that contain Markov-switching parameters to examine their multiperiod stock-market volatility forecasts as predictions of options-implied volatilities. The volatility model that best predicts the behavior of the options-implied volatilities allows the Student-t degrees-of-freedom parameter to switch such that the conditional variance and kurtosis are subject to discrete shifts. The half-life of the most leptokurtic state is estimated to be a week, so expected market volatility reverts to near-normal levels fairly quickly following a spike

Autoregressive conditional heteroskedasticity · Conditional variance · Econometrics · Economics · Financial economics · Financial models with long-tailed distributions and volatility clustering · Forward volatility · Heteroscedasticity · Implied volatility · Kurtosis · Markov chain · Statistics · Stochastic volatility · Stock market · Complex Systems and Time Series Analysis · Financial Risk and Volatility Modeling · Market Dynamics and Volatility · Mathematics

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Unique citing works8
Citations per year0,31
Citation span2000 - 2025 (26)
Citation velocityrecent
Highly citedNo
Citation typesNeutral: 7

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