Periodic autoregressive conditional heteroscedasticity

Published

Journal Article

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. © 1996 Taylor and Francis Group, LLC.

Full Text

Duke Authors

Cited Authors

  • Bollerslev, T; Ghysels, E

Published Date

  • January 1, 1996

Published In

Volume / Issue

  • 14 / 2

Start / End Page

  • 139 - 151

Electronic International Standard Serial Number (EISSN)

  • 1537-2707

International Standard Serial Number (ISSN)

  • 0735-0015

Digital Object Identifier (DOI)

  • 10.1080/07350015.1996.10524640

Citation Source

  • Scopus