Volatility modeling and prediction: the role of price impact

Ying Jiang, Yi Cao, Xiaoquan Liu*, Jia Zhai

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)


In this paper, we are interested in exploring the role of price impact, derived from the order book, in modeling and predicting stock volatility. This is motivated by the market microstructure literature that examines the mechanics of price formation and its relevance to market quality. Using a comprehensive dataset of intraday bids, asks, and three levels of market depths for 148 stocks in the Shanghai Stock Exchange from 2005 to 2016, we find substantial intraday impact from incoming bid and ask limit and market orders on stock prices. More importantly, the permanent price impact at the daily level is a significant determinant of stock volatility dynamics as suggested by the panel VAR estimation. Furthermore, when we augment traditional volatility models with the time series of daily price impact, the augmented models produce significantly more accurate volatility predictions at the one-day ahead forecasting horizon. These volatility predictions also offer economic gains to a mean-variance utility investor in a portfolio setting.

Original languageEnglish
Pages (from-to)2015-2031
Number of pages17
JournalQuantitative Finance
Issue number12
Publication statusPublished - 2 Dec 2019
Externally publishedYes


  • Chinese stock market
  • Market microstructure
  • Panel vector autoregression
  • Volatility modeling


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