Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/236820 
Authors: 
Year of Publication: 
2021
Citation: 
[Journal:] Statistics in Transition New Series [ISSN:] 2450-0291 [Volume:] 22 [Issue:] 1 [Publisher:] Exeley [Place:] New York [Year:] 2021 [Pages:] 145-162
Publisher: 
Exeley, New York
Abstract: 
Although regulatory standards, currently developed by the Basel Committee on Banking Supervision, anticipate a shift from VaR to ES, the evaluation of risk models currently remains based on the VaR measure. Motivated by the Basel regulations, we address the issue of VaR backtesting and contribute to the debate by exploring statistical properties of the exponential autoregressive conditional duration (EACD) VaR test. We show that, under the null, the tested parameter lies at the boundary of the parameter space, which can profoundly affect the accuracy of this test. To compensate for this deficiency, a mixture of chi-square distributions is applied. The resulting accuracy improvement allows for the omission of the Monte Carlo simulations used to implement the EACD VaR test in earlier studies, which dramatically improves the computational efficiency of the procedure. We demonstrate that the EACD approach to testing VaR has the potential to enhance statistical inference in most problematic cases - for small samples and for those close to the null.
Subjects: 
VaR backtesting
exponential autoregressive conditional duration
boundary of the parameter space
test size
test power
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc-nd Logo
Document Type: 
Article

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