Chapter 5
High-Frequency Data Analysis and Market Microstructure
High-frequency data are observations taken at fine time intervals. In finance, they often mean observations taken daily or at a finer time scale. These data have become available primarily due to advances in data acquisition and processing techniques, and they have attracted much attention because they are important in empirical study of market microstructure and realized volatility. The ultimate high-frequency data in finance are the transaction-by-transaction or trade-by-trade data in security markets. Here time is often measured in seconds. The Trades and Quotes (TAQ) database of the New York Stock Exchange (NYSE) contains all equity transactions reported on the Consolidated Tape from 1992 to the present, which includes transactions on the NYSE, AMEX, NASDAQ, and the regional exchanges. The Berkeley Options Data Base provides similar data for options transactions from August 1976 to December 1996. More high-frequency options data are also available; see the website of Chicago Board Options Exchange. Transactions data for many other securities and markets, both domestic and foreign, are continuously collected and processed. Wood (2000) provides some historical perspective of high-frequency financial study.
High-frequency financial data are important in studying a variety of issues related to the trading process and market microstructure. They can be used to compare the efficiency of different trading systems in price discovery (e.g., the open out-cry system of the NYSE and the computer trading system of NASDAQ). They can also be used to study the dynamics of bid-and-ask quotes of a particular stock (e.g., Hasbrouck, 1999; Zhang, Russell, and Tsay, 2008). In an order-driven stock market (e.g., the Taiwan Stock Exchange), high-frequency data can be used to study the order dynamics and, more interesting, to investigate the question of “who provides the market liquidity.” Cho, Russell, Tiao, and Tsay (2003) use intraday 5-minute returns of more than 340 stocks traded on the Taiwan Stock Exchange to study the impact of daily stock price limits and find significant evidence of magnet effects toward the price ceiling.
However, high-frequency data have some unique characteristics that do not appear in lower frequencies. Analysis of these data thus introduces new challenges to financial economists and statisticians. In this chapter, we study these special characteristics, consider methods for analyzing high-frequency data, and discuss implications of the results obtained. In particular, we discuss nonsynchronous trading, bid–ask spread, duration models, price movements, and bivariate models for price changes and time durations between transactions associated with price changes. The models discussed are also applicable to other scientific areas such as telecommunications and environmental studies.