Periodic autoregressive conditional heteroscedasticity
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.
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Related Subject Headings
- Econometrics
- 49 Mathematical sciences
- 38 Economics
- 35 Commerce, management, tourism and services
- 15 Commerce, Management, Tourism and Services
- 14 Economics
- 01 Mathematical Sciences
Citation
Published In
DOI
EISSN
ISSN
Publication Date
Volume
Issue
Start / End Page
Related Subject Headings
- Econometrics
- 49 Mathematical sciences
- 38 Economics
- 35 Commerce, management, tourism and services
- 15 Commerce, Management, Tourism and Services
- 14 Economics
- 01 Mathematical Sciences