FARVaR: Functional Autoregressive Value-at-Risk: FARVaR

Yongcheol Shin; Minjoo Kim; Charlie Cai; Qi Chang

doi:10.1093/jjfinec/nby031

FARVaR: Functional Autoregressive Value-at-Risk: FARVaR

Yongcheol Shin, Minjoo Kim, Charlie Cai, Qi Chang

Economics

Research output: Contribution to journal › Article › peer-review

Abstract

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 language	English
Pages (from-to)	284-337
Number of pages	54
Journal	Journal of Financial Econometrics
Volume	17
Early online date	30 Nov 2018
DOIs	https://doi.org/10.1093/jjfinec/nby031
Publication status	Published - 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.

Keywords

Density Forecasts
Market Risk Management
Functional Autoregressive Model

Access to Document

10.1093/jjfinec/nby031

Cai Kim Shin Zhang JFEC 2018
© 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.
Accepted author manuscript, 2.06 MB

Cite this

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title = "FARVaR: Functional Autoregressive Value-at-Risk: FARVaR",

abstract = "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.",

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T2 - FARVaR

AU - Shin, Yongcheol

AU - Kim, Minjoo

AU - Cai, Charlie

AU - Chang, Qi

N1 - © 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.

PY - 2019

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N2 - 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.

AB - 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.

KW - Density Forecasts

KW - Market Risk Management

KW - Functional Autoregressive Model

U2 - 10.1093/jjfinec/nby031

DO - 10.1093/jjfinec/nby031

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EP - 337

JO - Journal of Financial Econometrics

JF - Journal of Financial Econometrics

SN - 1479-8409

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