Ruey Tsay studies business and economic forecasting, big data analysis, risk modeling and management, credit ratings, and process control. Tsay's research aims at finding the dynamic relationships between variables and how to extract information from messy data. He has authored Analysis of Financial Time Series, 3rd Edition, published in 2010 by Wiley; An Introduction to Analysis of Financial Data with R, published in 2012 by Wiley; Multivariate time series analysis with R and Financial Applications, published in 2014 by Wiley; and coauthored A Course in Time Series Analysis, with D. Pena and G. Tiao, published by Wiley in 2001; Nonlinear Time Series Analysis, with R. Chen, published by Wiley in 2018; and Statistical Learning for Bid Dependent Data, with D. Pena, published by Wiley in 2020. He also published more than 100 articles in leading econometrics and statistical journals. Tsay has worked as a consultant for numerous American, Chinese, and Taiwanese companies. This experience taught him what works in practice and what does not - knowledge that he shares with students in the classroom. He hopes they learn ideas and methods for extracting information from data, large or small.
Tsay is the winner of the 2005 IBM Faculty Research Award and the John Wiley and Sons Author of the Year for his book, Analysis of Financial Time Series, in probability and statistics. He has received nine National Science Foundation grants and holds a U.S. patent for a system and method for building a time series model. He has delivered invited lectures at IMF and central banks of several countries.
He is a fellow of the American Statistical Association, the Institute of Mathematical Statistics, the Royal Statistical Society, and Academia Sinica. He served as coeditor of the Journal of Business & Economic Statistics, Journal of Forecasting, and Statistica Sinica, and as associate editor of several journals.
Tsay earned a bachelor's degree from the National Tsing Hua University in Taiwan in 1974 and a PhD in statistics from the University of Wisconsin-Madison in 1982. He joined Chicago Booth in 1989.
Outside of the classroom, Tsay enjoys gardening.
2020 - 2021 Course Schedule
||Analysis of Financial Time Series
||Multivariate Time Series Analysis
Market-based credit rating, analysis of high-frequency data; financial econometrics; value at risk and extreme value theory; Markov chain Monte Carlo method; multivariate and nonlinear time series analysis; risk management.
Multivariate Time Series Analysis with R and Financial Applications, John Wiley & Sons, 2014. ISBN: 978-1-118-61790-8.
An Introduction to Analysis of Financial Data with R, John Wiley & Sons, 2013.
Analysis of Financial Time Series, 3rd Edition, John Wiley & Sons, 2010.
Nonlinear Time Series Analysis (with Rong Chen), John Wiley & Sons (2017, to appear)
"Independent component analysis via distance covariance" (with D. Matteson), Journal of the American Statistical Association, (2017), (to appear).
"Modeling structured correlation matrices" (with M. Pourahmadi), Biometrika, (2017), 104, 237-242.
"Principal volatility component analysis" (with discussions), (with Y.P. Hu),
Journal of Business & Economic Statistics, (2014).
"Market-based credit ratings" (with D. Creal and B. Gramacy), Journal of Business & Economic Statistics, (2014).
"High dimensional dynamic stochastic copula models" (with D. Creal),
Journal of Econometrics, (2015).
"Doubly constrained factor models with applications" (with H. Tsai, C. W. Lin, and C.W. Cheng), Statistica Sinica, (2016).
"Independent component analysis via distance covariance" (with D. Matteson),
Journal of the American Statistical Association, (2016).
"Some methods for analyzing big dependent data," Journal of Business &
Economic Statistics, (2016).
For a listing of research publications, please visit the university library listing