 Stylized Facts of High-frequency Financial Time Series DataMoonis Shakeel and Bhavana Srivastava Global Business Review, 2021, vol. 22, issue 2, 550-564 Abstract: High-frequency financial time series data have an ability to define market microstructure and are helpful in making rational real-time decisions. These data sets carry unique characteristics and properties which are not available in low-frequency data; with that high-frequency data also create more challenges and opportunities for econometric modelling and financial data analysis. So it is essential to know the features and the facts related to the high-frequency time series data. In this article, we provide the characteristics and stylized facts exhibited by the high-frequency financial time series data of the S&P CNX Nifty futures index. Stylized facts are mostly related to the empirical observed behaviours, distributional properties, autocorrelation function and seasonality of the high-frequency data. Also, it illustrates the importance of stationarity in financial time series analysis. The knowledge of such facts and concepts is helpful to establish better empirical models and to produce reliable forecasts. Keywords: High-frequency financial time series data; stylized facts; stationarity; autocorrelation; GARCH (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:sae:globus:v:22:y:2021:i:2:p:550-564 Access Statistics for this article More articles in Global Business Review from International Management Institute |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.