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dc.contributor.author Bollerslev, Tim en_US
dc.contributor.author Ghysels, Eric en_US
dc.date.accessioned 2010-03-09T15:28:16Z
dc.date.available 2010-03-09T15:28:16Z
dc.date.issued 1996 en_US
dc.identifier.uri http://hdl.handle.net/10161/1891
dc.description.abstract Most high-frequency asset returns exhibit seasonal volatility patterns. This article proposes a new class of models featuring periodicity in conditional heteroscedasticity explicitly designed to capture the repetitive seasonal time variation in the second-order moments. This new class of periodic autoregressive conditional heteroscedasticity, or P-ARCH, models is directly related to the class of periodic autoregressive moving average (ARMA) models for the mean. The implicit relation between periodic generalized ARCH (P-GARCH) structures and time-invariant seasonal weak GARCH processes documents how neglected autoregressive conditional heteroscedastic periodicity may give rise to a loss in forecast efficiency. The importance and magnitude of this informational loss are quantified for a variety of loss functions through the use of Monte Carlo simulation methods. Two empirical examples with daily bilateral Deutschemark/British pound and intraday Deutschemark/U.S. dollar spot exchange rates highlight the practical relevance of the new P-GARCH class of models. Extensions to discrete-time periodic representations of stochastic volatility models subject to time deformation are briefly discussed. en_US
dc.format.extent 421219 bytes
dc.format.mimetype application/pdf
dc.language.iso en_US
dc.publisher Journal of Business and Economic Statistics en_US
dc.subject ARCH en_US
dc.subject Exchange rates
dc.subject Periodic structures
dc.subject P-GARCH
dc.subject Seasonality
dc.subject Volatility clustering
dc.title Periodic Autoregressive Conditional Heteroskedasticity en_US
dc.type Journal Article en_US
dc.department Economics

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