Volatility forecast of stock indices by model averaging using high-frequency data

@inproceedings{Wang2015VolatilityFO,
  title={Volatility forecast of stock indices by model averaging using high-frequency data},
  author={Chengyang Wang and Yoshihiko Nishiyama},
  year={2015}
}
  • Chengyang Wang, Yoshihiko Nishiyama
  • Published 2015
  • Economics
  • GARCH-class models provide good performance in volatility forecasts. In this paper, we use realized GARCH (RGARCH), HEAVY (high-frequency-based volatility), and MEM (multiplicative error model) models to forecast one-day volatility of Chinese and Japanese stock indices. Forecast series from each are computed and the results compared to see which performs the best. To explore the possibility of better predictions, we combine the models by a model-averaging technique. In the empirical analysis… CONTINUE READING

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