Faculty & Research

Jeffrey R. Russell

Alper Family Professor of Econometrics and Statistics

Phone :
773 834-0720
Address :
5807 South Woodlawn Avenue
Chicago, IL 60637

Jeffrey Russell conducts research on financial econometrics, time series, applied econometrics, empirical market microstructure, and high-frequency financial data. Russell's recent research has focused on using intraday price data to measure and predict financial asset volatility. His work has appeared in the Review of Economic Studies, Journal of Financial Economics and Econometrica. His research is supported by a Morgan Stanley Equity Microstructure Grant and he is the recipient of an Alfred P. Sloan Doctoral Dissertation Fellowship.

In addition to teaching and research, Russell is an associate editor of the Journal of Business and Economic Statistics. He has served on the NASDAQ Board of Economic Advisors. Additionally, he works as a consultant for legal and financial companies. He also has worked as a consultant for Citadel, where he modeled and forecasted intraday financial returns.

Russell has made presentations all over the world, including the 2006 North American Econometric Society Meetings, the 2005 Financial Econometrics Conference in Montreal, the 2005 Morgan Stanley Equity Microstructure Conference in Miami, and the 2005 American Finance Association Annual Meetings in Philadelphia.

He was the 2005-2006 Morgan Stanley visiting researcher at NYU. He earned a bachelor's of arts degree and a bachelor's of science degree in 1991. In 1996 he received a PhD in economics from the University of California at San Diego, where he earned an Econometric Analysis Fellowship and an Academic Excellence Award. He joined the Chicago Booth faculty in 1996.

 

2013 - 2014 Course Schedule

Number Name Quarter
41000 Business Statistics 2013 (Fall)
41203 Financial Econometrics 2014 (Winter)
41811 Financial Econometrics 2014 (Summer)
41910 Time-series Analysis for Forecasting and Model Building 2014 (Winter)

Other Interests

Food and wine, flying sailplanes (not at the same time).

 

Research Activities

Econometrics; time series; empirical finance; analysis of high-frequency financial data.

With F. Bandi, "Microstructure Noise, Realized Volatility, and Optimal Sampling," Review of Economic Studies (forthcoming).

With F. Bandi and J. Zhu, "Using High-Frequency Data in Dynamic Portfolio Choice" Econometric Reviews (forthcoming).

With F. Bandi, "Separating Microstructure Noise from Volatility," Journal of Financial Economics (2006).

With R. Engle, "A Discrete-State, Continuous-Time Model for Financial Transactions Prices and Times: The ACM-ACD Model," Journal of Business Economics and Statistics (2006).

With R. Engle, "Autoregressive Conditional Duration: A New Model for Irregularly Spaced Transaction Data," Econometrica (1998).

For a listing of research publications please visit ’s university library listing page.

New: Measuring and Modeling Execution Cost and Risk
Date Posted: Dec  29, 2008
We introduce a new analysis of transaction costs that explicitly recognizes the importance of the timing of execution in assessing transaction costs. Time induces a risk/cost tradeoff. The price of immediacy results in higher costs for quickly executed orders while more gradual trading results in higher risk since the value of the asset can vary more over longer periods of time. We use a novel data set that allows a sequence of transactions to be associated with individual orders and measure and

New: Measuring and Modeling Execution Cost and Risk
Date Posted: Dec  29, 2008
We introduce a new analysis of transaction costs that explicitly recognizes the importance of the timing of execution in assessing transaction costs. Time induces a risk/cost tradeoff. The price of immediacy results in higher costs for quickly executed orders while more gradual trading results in higher risk since the value of the asset can vary more over longer periods of time. We use a novel data set that allows a sequence of transactions to be associated with individual orders and measure and

New: Microstructure Noise, Realized Variance, and Optimal Sampling
Date Posted: May  29, 2008
A recent and extensive literature has pioneered the summing of squared observed intra-daily returns, realized variance, to estimate the daily integrated variance of financial asset prices, a traditional object of economic interest. We show that, in the presence of market micro-structure noise, realized variance does not identify the daily integrated variance of the frictionless equilibrium price. However, we demonstrate that the noise-induced bias at very high sampling frequencies can be appropr

Forecasting the Frequency of Changes in Quoted Foreign Exchange Prices with the Autoregressive Condi...
Date Posted: Apr  29, 2008
This paper applies the Autoregressive Conditional Duration model to Foreign Exchange quotes arriving on Reuter's screens. The Autoregressive Conditional Duration model, proposed in Engle and Russell (1995), is a new statistical model for the analysis of data that do not arrive in equal time intervals. When Dollar/Deutschmark data are examined, it is clear that many of the price quotes carry little information about the price process, as they are simply repeats of the previous quote. By selective

Autoregressive Conditional Duration: A New Model for Irregularly Spaced Transaction Data
Date Posted: Apr  29, 2008
This paper proposes a new statistical model for the analysis of data that do not arrive in equal time intervals, such as financial transactions data, telephone calls, or sales data on commodities that are tracked electronically. In contrast to fixed interval analysis, the model treats the time between events as a stochastic time varying process. We propose a new model for point processes with intertemporal correlation. Because the model focuses on the time interval between events it is called th

Econometric Analysis of Discrete-Valued Irregularly-Spaced Financial Transactions Data Using a New A...
Date Posted: Apr  29, 2008
This paper proposes a new approach to modeling financial transactions data. A model for discrete valued time series is introduced in the context of generalized linear models. Since the model specifies probabilities of return outcomes conditional on both the previous state and the historic distribution, we call the it the Autoregressive Conditional Multinomial (ACM) model. Recognizing that prices are observed only at transactions, the process is interpreted as a marked point process. The ACD m

Separating Microstructure Noise from Volatility
Date Posted: Jan  03, 2005
There are two volatility components embedded in the returns constructed using recorded stock prices: the genuine time-varying volatility of the unobservable returns that would prevail (in equilibrium) in a frictionless, full-information, economy and the variance of the equally unobservable microstructure noise. Using straightforward sample averages of high-frequency return data recorded at different frequencies, we provide a simple technique to identify both volatility features. We apply our met