FARVaR: Functional Autoregressive Value-at-Risk: FARVaR

Yongcheol Shin, Minjoo Kim, Charlie Cai, Qi Chang

Research output: Contribution to journalArticlepeer-review


Motivated by the stylized fact that intraday returns can provide additional information on the tail behaviour of daily returns, we propose a functional autoregressive value-at-risk approach which can directly incorporate such informational advantage into the daily value-at-risk forecast. Our approach leads to greater flexibility in modelling 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.
Original languageEnglish
Pages (from-to)284-337
Number of pages54
JournalJournal of Financial Econometrics
Early online date30 Nov 2018
Publication statusPublished - 2019

Bibliographical note

© The Authors, 2018. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details.


  • Density Forecasts
  • Market Risk Management
  • Functional Autoregressive Model

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