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Realized Volatility Forecast: Structural Breaks, Long Memory, Asymmetry, and Day‐of‐the‐Week Effect
Author(s) -
Yang Ke,
Chen Langnan
Publication year - 2014
Publication title -
international review of finance
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.489
H-Index - 18
eISSN - 1468-2443
pISSN - 1369-412X
DOI - 10.1111/irfi.12030
Subject(s) - econometrics , volatility (finance) , autoregressive model , forward volatility , autoregressive conditional heteroskedasticity , economics , stock exchange , realized variance , heteroscedasticity , asymmetry , stock (firearms) , long memory , autoregressive fractionally integrated moving average , implied volatility , finance , engineering , physics , mechanical engineering , quantum mechanics
We investigate the properties of the realized volatility in C hinese stock markets by employing the high‐frequency data of Shanghai Stock Exchange Composite Index and four individual stocks from S hanghai Stock Exchange and S henzhen Stock Exchange, and find that the volatility exhibits the properties of long‐term memory, structural breaks, asymmetry, and day‐of‐the‐week effect. In addition, the structural breaks only partially explain the long memory. To capture these properties simultaneously, we derive an adaptive asymmetry heterogeneous autoregressive model with day‐of‐the‐week effect and fractionally integrated generalized autoregressive conditional heteroskedasticity errors ( HAR ‐ D ‐ FIGARCH ) and use it to conduct a forecast of realized volatility. Compared with other heterogeneous autoregressive realized volatility models, the proposed model improves the in‐sample fit significantly. The proposed model is the best model for the day‐ahead realized volatility forecasts among the six models based on various loss functions by utilizing the superior predictive ability test.