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FARVaR: Functional Autoregressive Value-at-Risk

Cai, C.X.; Kim, M.; Shin, Y.; Zhang, Q.

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Authors

C.X. Cai

M. Kim

Y. Shin

Q. Zhang



Abstract

Motivated by the stylized fact that intraday returns can provide additional information on the tail behavior of daily returns, we propose a functional autoregressive value-at-risk (VaR) approach which can directly incorporate such informational advantage into the daily VaR forecast. Our approach leads to greater flexibility in modeling the dynamic evolution of the density function of intraday returns and the ability to capture substantial swings in the tails following major events. We comprehensively evaluate our proposed model using intraday transaction data and demonstrate that it can improve coverage ability, reduce economic cost, and enhance statistical reliability in market risk management.

Citation

Cai, C., Kim, M., Shin, Y., & Zhang, Q. (2019). FARVaR: Functional Autoregressive Value-at-Risk. Journal of Financial Econometrics, 17(2), 284-337. https://doi.org/10.1093/jjfinec/nby031

Journal Article Type Article
Acceptance Date Oct 19, 2018
Online Publication Date Nov 30, 2018
Publication Date 2019
Deposit Date Nov 1, 2018
Publicly Available Date Nov 30, 2020
Journal Journal of Financial Econometrics
Print ISSN 1479-8409
Electronic ISSN 1479-8417
Publisher Oxford University Press
Peer Reviewed Peer Reviewed
Volume 17
Issue 2
Pages 284-337
DOI https://doi.org/10.1093/jjfinec/nby031
Public URL https://durham-repository.worktribe.com/output/1343946

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Copyright Statement
This is a pre-copyedited, author-produced version of an article accepted for publication in Journal of Financial Econometrics following peer review. The version of record Cai, C. X., Kim, M., Shin, Y. & Zhang, Q. (2019). FARVaR: Functional Autoregressive Value-at-Risk. Journal of Financial Econometrics 17(2): 284-337 is available online at: https://doi.org/10.1093/jjfinec/nby031




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