New Challenges for Big Data in Economics and Finance
Friday, November 11th
Chairman: Prosper Dovonon (Concordia Univ.)
– Stephane Gaiffas (Ecole Polytechnique): “Introduction to Machine Learning, Application to Hawkes Processes“.
– Yves Atchadé (University of Michigan): “Bayesian Inference of Exponential Random Graph Models for Large Social Networks”.
10:20–10:50 Coffee break
Chairman: Victor Aguirregabiria (Univ. of Toronto)
– Sanjog Misra (Chicago Booth): “Big Data and Marketing Analytics in Gaming: Combining Empirical Models and Field Experimentation“.
– Christian Gouriéroux (Univ. of Toronto & QMI): “Double IV Estimation of Factor Model with Application to Big Data“.
12:10–13:20 Lunch break
13:20–14:40 Statistical Inference
Chairman: René Garcia (Univ. Montréal)
– Nancy Reid (Univ. of Toronto): “Approximate Likelihoods”
– Ivana Komunjer (Univ. California San Diego): “Statistical Inference on Manifolds“.
14:40–16:00 Asset Management
Chairman: Angelo Melino (Univ. of Toronto)
– Ronnie Sadka (Boston College): “What do measures of real-time corporate sales tell us about earnings surprises and post-announcement returns?”.
– Serge Darolles (Univ. Paris – Dauphine & QMI): “Liquidity Risk and Investor Behavior: Issues, Data and Models“.
16:00–16:20 Coffee break
16:20–17:20 Panel session: big data opportunities and challenges
Chairman: Gaëlle Le Fol (Univ. Paris – Dauphine & QMI)
– Hicham Hajhamou, AQR
– Axel Pierron, Opimas
– Ronnie Sadka, Boston College
Saturday, November 12th
Chairman : Yuanyuan Wan (Univ. of Toronto)
– Ivan Fernandez-Val (Boston University): “Program Evaluation and Causal Inference with High-Dimensional Data“.
– Alberto Abadie (Harvard University): “The Risk of Machine Learning“
Chairman: Silvia Goncalves (Univ. of Western Ontario)
– Marc Hallin (Univ. Libre de Bruxelles): “Networks, dynamic factors, and the volatility analysis of high-dimensional
– Jianqing Fan (Princeton Univ.): “Validating Market Risk Factors and Forecasting Bond Risk Premia using Innovated Factor Models“.
Fileds Institute, University of Toronto, November 11-12, 2016
The recent increase of computational technology allows for collection of “Big data”. “Big data” is a term for data set that is so large or complex that traditional methods of analysis are inadequate. The goal of this conference is to bring expert from economics and finance to discuss the challenges that large data sets present, the questions such data might help us answer, and present the new avenues of research related to this issue.