Value at risk forecasting for volatility index

ABSTRACT

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.

KEYWORDS:

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

JEL CLASSIFICATION:

• C22

Acknowledgement

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

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

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