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dc.contributor.authorVarneskov, Rasmus T.en_US
dc.contributor.authorPerron, Pierreen_US
dc.date2017-05-05
dc.date.accessioned2018-02-06T01:51:50Z
dc.date.available2018-02-06T01:51:50Z
dc.date.issued2017-07-12
dc.identifier.citationRasmus T Varneskov, Pierre Perron. 2017. "Combining Long Memory and Level Shifts in Modeling and Forecasting the Volatility of Asset Returns." Quantitative Finance (forthcoming),
dc.identifier.issn1469-7688
dc.identifier.urihttps://hdl.handle.net/2144/26709
dc.description.abstractWe propose a parametric state space model of asset return volatility with an accompanying estimation and forecasting framework that allows for ARFIMA dynamics, random level shifts and measurement errors. The Kalman filter is used to construct the state-augmented likelihood function and subsequently to generate forecasts, which are mean and path-corrected. We apply our model to eight daily volatility series constructed from both high-frequency and daily returns. Full sample parameter estimates reveal that random level shifts are present in all series. Genuine long memory is present in most high-frequency measures of volatility, whereas there is little remaining dynamics in the volatility measures constructed using daily returns. From extensive forecast evaluations, we find that our ARFIMA model with random level shifts consistently belongs to the 10% Model Confidence Set across a variety of forecast horizons, asset classes and volatility measures. The gains in forecast accuracy can be very pronounced, especially at longer horizons.en_US
dc.publisherTaylor & Francis (Routledge)en_US
dc.relation.ispartofQuantitative Finance
dc.subjectMathematical sciencesen_US
dc.subjectEconomicsen_US
dc.subjectFinanceen_US
dc.subjectLong memory processesen_US
dc.subjectKalman filteren_US
dc.subjectForecastingen_US
dc.subjectStochastic volatilityen_US
dc.subjectState space modellingen_US
dc.subjectStructural changeen_US
dc.titleCombining long memory and level shifts in modeling and forecasting the volatility of asset returnsen_US
dc.typeArticleen_US
dc.identifier.doi10.1080/14697688.2017.1329591
pubs.elements-sourcemanual-entryen_US
pubs.notesEmbargo: Not knownen_US
pubs.organisational-groupBoston Universityen_US
pubs.organisational-groupBoston University, College of Arts & Sciencesen_US
pubs.organisational-groupBoston University, College of Arts & Sciences, Department of Economicsen_US
pubs.publication-statusAccepteden_US
dc.date.online2017-07-12


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