Teaching

Advanced Topics in Data Science and Finance

Course Description

This course is intended for PhD students in finance and related fields. It is designed to teach students how to conduct empirical research in asset pricing. The goal is that students become familiar with the issues at stake in empirical asset pricing, the methodologies used, and be able to analyze and evaluate new research effectively.

Prerequisites

Prerequisites are: Econ 770, 771 and Busi 880. This means students must have basic knowledge of financial economics and econometrics at the level of first year PhD courses. Knowledge of the material in Econ 871 (Time Series) is beneficial.

Table of Contents

Part I: Generalized Method of Moments

Part II: Hansen-Jagannathan Bounds and Distances
Part III: Machine Learning with Regularized Regressions
Part IV: High-dimensional Linear and Regularized GMM

Part V: Simulation-based Estimation

Part V: Deep Learning

Part VII: An Adversarial Approach to Structural Estimation
Part VIII: Univariate ARCH Models

Part IX: Multivariate ARCH Models

Part X: State Space Models and Kalman Filter Markov Chain Monte Carlo Estimation and Filtering

Part XI: Principal Components Analysis and Estimation of High-dimensional Covariance Matrices

FORECASTING TECHNIQUES IN BUSINESS APPLICATIONS

The course introduces basic time series regression techniques for the purpose of forecasting. The material will focus on macro economic and business applications. Students will learn the nuts and bolts of forecasting techniques through practical applications.

• Currently not offered

• Visit Canvas course webpage for further details

CURRENT TOPICS IN FINANCE: FINTECH

Recent developments in block chain technology and cryptocurrencies are covered. 

 • Visit Canvas course webpage for further details

CURRENT TOPICS IN FINANCE: FINANCIAL INNOVATION AND THE CRISIS

The course covers advanced techniques in fixed income pricing combined with discussions regarding the recent financial crisis.

• Currently not offered

• Visit Canvas course webpage for further details