Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/233643 
Year of Publication: 
2021
Citation: 
[Journal:] Journal of Forecasting [ISSN:] 1099-131X [Volume:] 40 [Issue:] 5 [Publisher:] Wiley [Place:] Hoboken, NJ [Year:] 2021 [Pages:] 883-910
Publisher: 
Wiley, Hoboken, NJ
Abstract: 
We propose a copula-based periodic mixed frequency generalized autoregressive (GAS) framework in order to model and forecast the intraday exposure conditional value at risk (ECoVaR) for an intraday asset return and the corresponding market return. In particular, we analyze GAS models that account for long-memory-type of dependencies, periodicities, asymmetric nonlinear dependence structures, fat-tailed conditional return distributions, and intraday jump processes for asset returns. We apply our framework in order to analyze the ECoVaR forecasting performance for a large data set of intraday asset returns of the S&P500 index.
Subjects: 
CoVaR
dynamic copulas
intraday
systemic risk
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc-nd Logo
Document Type: 
Article
Document Version: 
Published Version

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