FARVaR: Functional Autoregressive Value-at-Risk

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

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