About

Eric Zivot is the Robert Ricards Chaired Professor of Economics and serves as the Department Chair in the Department of Economics at the University of Washington. He received his Ph.D. in Economics from Yale University in 1992, focusing on econometrics under the supervision of Donald Andrews and Peter C.B. Phillips. His academic background also includes an A.B. in Economics (with honors) and A.B. in Statistics from the University of California, Berkeley, obtained in May 1986. Professor Zivot's research primarily concentrates on econometrics, financial econometrics, and time series analysis. He is involved in developing and applying advanced statistical and computational methods to financial data, including state-space models, price discovery, and deep learning approaches to return forecasting. He is also an adjunct Professor of Finance in the Business School and an Adjunct Professor of Statistics. Additionally, he is the co-founder of the Professional Masters Program in Computational Finance and Risk Management at the University of Washington.

Research topics

• Computer Science
• Financial economics
• Political Science
• Econometrics
• Data Mining
• Economics
• Business
• Data science
• Mathematics
• Finance

Selected publications

• Improving information leadership share for measuring price discovery

  Journal of Empirical Finance · 2025-08-26 · 1 citations

  articleSenior authorCorresponding
  PublisherDOI

• In Defense of Information Leadership Share: A Response to Shrestha and Lee (2023)

  SSRN Electronic Journal · 2024

  Senior authorCorresponding
  • Political Science
  • Business
  • Political Science
  PublisherDOI

• Leverage Corrections to Price Discovery Measures With an Application to Leveraged Exchange-Traded Funds

  SSRN Electronic Journal · 2024-01-01

  articleOpen accessSenior author
  PublisherDOI

• Price Discovery Share: An Order Invariant Measure of Price Discovery

  SSRN Electronic Journal · 2024 · 1 citations

  Senior authorCorresponding
  • Computer Science
  • Data Mining
  • Econometrics
  PublisherDOI

• Generalizing Price Discovery Measures with Leverage Corrections: Evidence from Leveraged Exchange-Traded Funds

  SSRN Electronic Journal · 2024-01-01

  preprintOpen accessSenior author
  PublisherDOI

• Improving Price Leadership Share for Measuring Price Discovery

  SSRN Electronic Journal · 2024 · 2 citations

  Senior authorCorresponding
  • Computer Science
  • Economics
  • Business
  PublisherDOI

• Price discovery share: An order invariant measure of price discovery

  Finance research letters · 2024-06-22 · 2 citations

  articleSenior author
  PublisherDOI

• facmodTS: Time Series Factor Models for Asset Returns

  2023-11-09

  datasetOpen access

  Supports teaching methods of estimating and testing time series factor models for use in robust portfolio construction and analysis. Unique in providing not only classical least squares, but also modern robust model fitting methods which are not much influenced by outliers. Includes returns and risk decompositions, with user choice of standard deviation, value-at-risk, and expected shortfall risk measures. "Robust Statistics Theory and Methods (with R)", R. A. Maronna, R. D. Martin, V. J. Yohai, M. Salibian-Barrera (2019) &lt;<a href="https://doi.org/10.1002%2F9781119214656" target="_top">doi:10.1002/9781119214656</a>&gt;.

  PublisherOA PDFDOI

• A NEW PROJECTION-TYPE SPLIT-SAMPLE SCORE TEST IN LINEAR INSTRUMENTAL VARIABLES REGRESSION

  UNC Libraries · 2021-07-01

  articleOpen access

  In this paper we introduce a new method of projection-type inference and describe it in the context of two stage least squares-based split-sample inference on subsets of structural coefficients in a linear instrumental variables regression model. The use of the new method not only guards against the uncontrolled over-rejection of the true value of the parameters of interest, but also reduces the conservativeness of the usual method of projection proposed by Dufour and his co-authors.

  PublisherDOI

• A new method of projection-based inference in GMM with weakly identified nuisance parameters

  UNC Libraries · 2021-08-14

  articleOpen access1st authorCorresponding

  Projection-based methods of inference on subsets of parameters are useful for obtaining tests that do not over-reject the true parameter values. However, they are also often criticized for being conservative. We show that the usual method of projection can be modified to obtain tests that are as powerful as the conventional tests for subsets of parameters. Like the usual projection-based methods, one can always put an upper bound to the rate at which the new method over-rejects the true value of the parameters of interest. The new method is described in the context of GMM with possibly weakly identified parameters.

  PublisherDOI

Frequent coauthors

• Jiahui Wang

  66 shared

• Saraswata Chaudhuri

  12 shared

• Kris Boudt

  Vrije Universiteit Brussel

  8 shared

• Drew Creal

  University of Notre Dame

  6 shared

• Siem Jan Koopman

  Tinbergen Institute

  6 shared

• Kyongwook Choi

  6 shared

• Bingcheng Yan

  Hunan University of Science and Technology

  6 shared

• Shulin Shen

  6 shared

Education

• Ph.D., Economics

  Yale University

  1992

Awards & honors

• Share Award for Outstanding Paper (2016)