Value at risk forecasting for volatility index

Seul Ki Park, Ji Eun Choi, Dong Wan Shin

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


Forecasts of values at risk (VaRs) are made for volatility indices such as the VIX for the US S&P 500 index, the VKOSPI for the KOSPI (Korea Stock Price Index) and the OVX (oil volatility index) for crude oil funds, which is the first in the literature. In the forecasts, dominant features of the volatility indices are addressed: long memory, conditional heteroscedasticity, asymmetry and fat-tails. An out-of-sample comparison of the VaR forecasts is made in terms of violation probabilities, showing better performance of the proposed method than several competing methods which consider the features differently from ours. The proposed method is composed of heterogeneous autoregressive model for the mean, GARCH model for the volatility and skew-t distribution for the error.

Original languageEnglish
Pages (from-to)1613-1620
Number of pages8
JournalApplied Economics Letters
Issue number21
StatePublished - 15 Dec 2017

Bibliographical note

Funding Information:
This study was supported by a grant from the National Research Foundation of Korea (2016R1A2B4008780).

Funding Information:
This work was supported by a grant from the National Research Foundation of Korea [2016R1A2B4008780].

Publisher Copyright:
© 2017 Informa UK Limited, trading as Taylor & Francis Group.


  • Conditional heteroscedasticity
  • HAR model
  • long-memory
  • skew-t distribution
  • VaR
  • volatility index


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