Professor Shuping Shi is a leading econometrician whose research lies at the intersection of financial econometrics and applied economics, with a particular focus on asset price bubbles, non-stationary dynamics, and high-frequency financial markets.

She was awarded the Australian Research Council Discovery Early Career Researcher Award in 2020 and received the prestigious Young Economist Award from the Economic Society of Australia in 2022.

Her work has been published in leading journals, including Review of Financial Studies, Journal of Econometrics, Management Science, International Economic Review, and Econometric Theory.

Professor Shi currently serves as an Associate Editor for Econometric Theory, Journal of Financial Econometrics, and Journal of Time Series Analysis.

She has also been recognized among the world’s top 2% most-cited economists in the 2024 annual report published by Stanford University.

Lasse Heje Pedersen is a Professor of Finance and Director of the Center for Big Data in Finance (BIGFI) at Copenhagen Business School and a Principal at AQR Capital Management.

Pedersen has served as Director of the American Finance Association, in the Liquidity Working Group meeting at the Federal Reserve Bank of New York to address liquidity issues, in the New York Fed's Monetary Policy Panel, on the Economic Advisory Boards of NASDAQ and FTSE, and on the editorial boards of the Journal of Finance, Journal of Economic Theory, The Review of Asset Pricing Studies, and Quarterly Journal of Economics.

Pedersen's papers are highly cited among academics, the media, central bank chairs, the Nobel prize committee, and are used in classes around the world.

Pedersen's academic awards include the Bernácer Prize to the best E.U. economist under 40 years of age, the Banque de France-TSE Prize, the Stephen A. Ross Prize, Fama-DFA Prizes, the Michael Brennan Award, the Brattle Prize, and the Graham and Dodd Award.

Pedersen received his B.S. and M.S. from University of Copenhagen and his Ph.D. from Stanford University Graduate School of Business.

Michael Wolf is a Professor of Econometrics and Applied Statistics at the University of Zurich and holds a Ph.D. in Statistics from Stanford University. Before joining the Department of Economics at the University of Zurich, he held previous positions at The University of California (Los Angeles), Universidad Carlos III (Madrid), and Universitat Pompeu Fabra (Barcelona).

Michael Wolf's research interests include resampling-based inference, multiple testing methods, the estimation of large-dimensional covariance matrices, financial econometrics, and machine learning.

His research has been published in leading journals, such as The Annals of Statistics, Biometrika, Econometrica, Journal of the American Statistical Association, and The Review of Financial Studies.