Abstract
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 microstructure literature that focuses on 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 buy and sell limit and market orders on stock prices. More importantly, the permanent price impact at the daily level is a significant determinant in the volatility estimation for all sample stocks as shown by the panel VAR estimation, which allows us to examine simultaneously the dynamics of price impact on all sample stocks. Furthermore, when we augment traditional volatility models with the time series of daily price impact, the augmented models produce significantly more accurate forecasts at the one-day horizon. These forecasts from the augmented models offer economic gains to a mean-variance utility investor in a portfolio setting.
Highlights
Intraday price formation and variation is a central topic in the market microstructure literature dating back a few decades. French and Roll (1986) is an early effort which shows that stock volatility is significantly higher during trading hours than during non-trading hours and attributes this to microstructure phenomenon
This is motivated by the rich market microstructure literature that explores the mechanics of the price formation in both quote- and order-driven markets
As volatility is shown to be partly driven by market microstructure related information, we are interested in knowing whether the information content of price impact extracted from order book events is relevant to volatility estimation and forecasting
Summary
Intraday price formation and variation is a central topic in the market microstructure literature dating back a few decades. French and Roll (1986) is an early effort which shows that stock volatility is significantly higher during trading hours than during non-trading hours and attributes this to microstructure phenomenon. Adopting a panel VAR model which allows us to gauge the effect of permanent price impact series on all sample stocks, we show that changes in aggregate daily price impact cause significant changes in stock volatility This is the first piece of evidence on the link between stock volatility and the price impact of incoming limit or market orders and in line with the theoretical framework in Madhavan et al (1997) that microstructure noise is an integral part of the information source for volatility. For a mean-variance utility investor who allocates her wealth between a stock and the riskfree asset, the volatility predictions from augmented models lead to significantly higher annualized portfolio returns, Sharpe ratio, and certainty equivalent returns in a portfolio setting across a range of risk aversion levels These novel findings support our conjecture that price impact of incoming order book events contains valuable information for volatility and adding the information improves volatility forecasting precision in statistical and economic terms.
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