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- W2342204074 abstract "ABSTRACTIn this study, the impact of noise and jump on the forecasting ability of volatility models with high-frequency data is investigated. A signed jump variation is added as an additional explanatory variable in the volatility equation according to the sign of return. These forecasting performances of models with jumps are compared with those without jumps. Being applied to the Chinese stock market, we find that the jump variation has a significant in-sample predictive power to volatility and the predictive power of the negative one is greater than the positive one. Furthermore, out-of-sample evidence based on the fresh model confidence set (MCS) test indicates that the incorporation of singed jumps in volatility models can significantly improve their forecasting ability. In particular, among the realized variance (RV)-based volatility models and generalized autoregressive conditional heteroscedasticity (GARCH) class models, the heterogeneous autoregressive model of realized volatility (HAR-RV) model ..." @default.
- W2342204074 created "2016-06-24" @default.
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- W2342204074 date "2016-01-25" @default.
- W2342204074 modified "2023-10-18" @default.
- W2342204074 title "Forecasting the realized volatility in the Chinese stock market: further evidence" @default.
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- W2342204074 doi "https://doi.org/10.1080/00036846.2015.1136394" @default.
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