The Intraday Analysis of Volatility, Volume and Spreads: A Review with Applications to Futures’ Markets
Dean Fantazzini
Abstract
Dean Fantazzini
Abstract
The growing interest in financial markets’ microstructure and the fact that financial professionals have access to huge intraday databases have made high-frequency data modeling a hot issue in recent empirical finance literature. We analyze the 12 main issues that are at stake when analyzing intraday financial time series, with particular emphasis on the joint dynamics of volatility, volume and spreads. We review the main econometric models used for volatility analysis in an intraday environment that works with non-equally spaced data and considers the whole information set provided by the market. Given the growing importance of tick-by-tick data analysis, we present an empirical application of ACD and ordered probit models to the Standard & Poor 500 and Nasdaq100 index futures’ data, and we point out the advantages and disadvantages of both approaches. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
A significance statement is not available in the OpenAlex record.
A contribution statement is not available in the OpenAlex record.
Method details are not available in the OpenAlex metadata.
Findings are not separately available in the OpenAlex metadata.
Limitations are not available in the OpenAlex metadata.
Application details are not available in the OpenAlex metadata.
The growing interest in financial markets’ microstructure and the fact that financial professionals have access to huge intraday databases have made high-frequency data modeling a hot issue in recent empirical finance literature. We analyze the 12 main issues that are at stake when analyzing intraday financial time series, with particular emphasis on the joint dynamics of volatility, volume and spreads. We review the main econometric models used for volatility analysis in an intraday environment that works with non-equally spaced data and considers the whole information set provided by the market. Given the growing importance of tick-by-tick data analysis, we present an empirical application of ACD and ordered probit models to the Standard & Poor 500 and Nasdaq100 index futures’ data, and we point out the advantages and disadvantages of both approaches. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Key concepts: Volatility (finance), Futures contract, Financial economics, Futures market, Financial market, Economics, Econometrics, Realized variance