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Value at risk forecasting for volatility index
- Value at risk forecasting for volatility index
- Park S.-K.; Choi J.-E.; Shin D.W.
- Ewha Authors
- SCOPUS Author ID
- Issue Date
- Journal Title
- Applied Economics Letters
- vol. 24, no. 21, pp. 1613 - 1620
- Conditional heteroscedasticity; HAR model; long-memory; skew-t distribution; VaR; volatility index
- SSCI; SCOPUS
- 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. © 2017 Informa UK Limited, trading as Taylor & Francis Group.
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