View : 190 Download: 0
Value at risk forecasting for volatility index
- Value at risk forecasting for volatility index
- Park, Seul-Ki; Choi, Ji-Eun; Shin, Dong Wan
- Ewha Authors
- SCOPUS Author ID
- Issue Date
- Journal Title
- APPLIED ECONOMICS LETTERS
- APPLIED ECONOMICS LETTERS vol. 24, no. 21, pp. 1613 - 1620
- Conditional heteroscedasticity; HAR model; long-memory; skew-t distribution; VaR; volatility index
- ROUTLEDGE JOURNALS, TAYLOR &
- SSCI; SCOPUS
- Document Type
- 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.
- Appears in Collections:
- 자연과학대학 > 통계학전공 > Journal papers
- Files in This Item:
There are no files associated with this item.
- RIS (EndNote)
- XLS (Excel)
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.