Ruey Tsay studies business and economic forecasting, 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 2013 by Wiley; and coauthored A Course in Time Series Analysis, with D. Pena and G. Tiao, published by Wiley in 2001. 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 is a coeditor of the Journal of Forecasting, and associate editor of Asia-Pacific Financial Markets, Studies in Nonlinear Dynamics and Econometrics, and Metron.
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.
2013 - 2014 Course Schedule
||Analysis of Financial Time Series
||Applied Multivariate 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.
Analysis of Financial Time Series, 3rd Edition (Wiley, 2010) and An Introduction to Analysis of Financial Data with R, (Wiley, 2012).
With D. Pena and G. Tiao, A Course in Time Series Analysis (Wiley, 2001).
With H. Tsai, “Constrained factor models,” Journal of the American Statistical Association (2010).
With David Matteson, “Dynamic orthogonal components for multivariate time series,” Journal of the American Statistical Association (2011).
With P. Galeano, “Shifts in individual parameters of a GARCH model,” Journal of Financial Econometrics (2010).
With J. Yeh, “Random aggregation with applications in high-frequency finance,” Journal of Forecasting, (2010).
With H. Lopes, “Particle filters and Bayesian inference in financial econometrics,” Journal of Forecasting, (2010).
For a listing of research publications please visit
’s university library listing
New: Market-Based Credit Ratings
We present a methodology for rating the creditworthiness of public companies in the U.S. from the prices of traded assets. Our approach uses asset pricing data to impute a term structure of risk neutral survival functions or default probabilities. Firms are then clustered into ratings categories based on their survival functions using a functional clustering algorithm. This allows all public firms whose assets are traded to be directly rated by market participants. For firms whose assets are not
New: Random Aggregation with Applications in High-Frequency Finance
In this paper we consider properties of random aggregation in time series analysis. For application, we focus on the problem of estimating high-frequency beta of an asset return when the returns are subject to the effects of market microstructure. Specifically, we study the correlation between intraday log returns of two assets. Our investigation starts with the effect of non-synchronous trading on intraday log returns when the underlying return series follows a stationary time series model. Thi
Bayesian Methods for Change-Point Detection in Long-Range Dependent Processes
We describe a Bayesian method for detecting structural changes in a long-range dependent process. In particular, we focus on changes in the long-range dependence parameter, d, and changes in the process level, p. Markov chain Monte Carlo (MCMC) methods are used to estimate the posterior probability and size of a change at time t, along with other model parameters. A time-dependent Kalman filter approach is used to evaluate the likelihood of the fractionally integrated ARMA model characterizing t