Time Series Analysis and Big Data in Finance

Dear Colleagues,

The objective of this Special Issue is to disseminate novel and original research that includes real-world applications of big data modeling and predictive analytics in the financial markets. In conjunction with the recent introduction of a vast number of “big” and “massive” datasets, a great number of new statistical/econometric tools and methods have been developed for analyzing such high-dimensional and high-frequency data. Examples include new tools and methods in machine learning, data mining, dimension natural language processing, and artificial intelligence,reduction, natural language processing, and artificial intelligence, to name a few. Empirical and theoretical articles that focus on the use of such data in contexts analyzing, modeling and forecasting financial variables in the areas of financial economics and econometrics, such as asset pricing, risk management, risk measurement, portfolio management are solicited. For example, submissions that contain empirical and/or theoretical research on the use of high-frequency and/or high-dimensional data in the analysis of nonparametric volatility estimators, dimension reduction in econometric and financial models, forecasting, and new measures of financial uncertainty are encouraged.