Fall 2022

SEM217: Stjepan Begušić, University of Zagreb: Issues in large covariance matrix estimation for portfolio risk prediction

Tuesday, November 29th @ 11:00-12:30 PM (ONLINE) Zoom Meeting ID: 995 9778 2168

Most covariance matrix estimation studies are focused on portfolio optimization applications, ultimately mitigating the "error maximization" property of optimizers. However, correcting one kind of error might lead to introducing errors in other applications, such as portfolio risk measurement. This talk is focused on covariance estimation issues which arise in the application of risk prediction for different portfolios. Results for a...

SEM 217: Debashis Paul, UC Davis

Tuesday, November 8th @ 11:00-12:30 PM (ONLINE)

We consider the problem of testing linear hypotheses associated with a multivariate linear regression model. Classical tests for this type of hypotheses based on the likelihood ratio statistics suffer from substantial loss of power when the dimensionality of the observations is comparable to the sample size. To mitigate this problem, we propose two different classes of regularized test procedures that rely on a nonlinear shrinkage of the eigenvalues, and possibly eigenprojections, of the estimated noise covariance matrix. The first...

SEM217: Petter Kolm, NYU: Deep Order Flow Imbalance: Extracting Alpha at Multiple Horizons from the Limit Order Book

Tuesday, November 15th @ 11:00-12:30 PM (ZOOM)

We describe how deep learning methods can be applied to forecast stock returns from high frequency order book states. We review the literature in this area and describe a study where we evaluate return forecasts for several deep learning models for a large subset of symbols traded on the Nasdaq exchange. We investigate whether transformations of the order book states are necessary...

SEM 217: Jeff Bohn, One Concern: Valuing and insuring natural assets: Challenges and opportunities

Tuesday, October 11th @ 11:00-12:30 PM, RM 648 Evans Hall and ONLINE

As global population increases amidst rapid, continued urban development, lifeline networks (LN) (e.g., power, water, transportation, etc.) are becoming an important component of re/insurer’s catastrophic risk assessments. As climate change has increased the frequency or severity (and sometimes both) of natural disasters, a broader cross-section of financial firms is exploring how to incorporate resilience...

SEM 217: Alec Kercheval, Florida State University: The James-Stein estimator for eigenvectors

Tuesday, October 4th @ 11:00-12:30 PM (ONLINE)

Portfolio risk forecasts require an estimate of the covariance matrix of asset returns, often for a large number of assets. When only a small number of observations are available, we are in the high-dimension-low-sample-size (HL) regime in which estimation error dominates. Factor models are used to decrease the dimension, but the factors still need to be estimated. We describe a shrinkage estimator for the first principal component, called James-Stein for Eigenvectors (JSE), that is parallel to the famous James-Stein estimator for a...

SEM217: Xinyi Zhong, Yale: Empirical Bayes PCA in high dimensions

Tuesday, September 27th @ 11:00-12:30 PM (ONLINE)

When the dimension of data is comparable to or larger than the number of data samples, Principal Components Analysis (PCA) may exhibit problematic high-dimensional noise. In this work, we propose an Empirical Bayes PCA method that reduces this noise by estimating a joint prior distribution for the principal components. EB-PCA is based on the classical Kiefer-Wolfowitz nonparametric MLE for empirical Bayes estimation, distributional results derived from random matrix theory for the sample PCs, and iterative refinement using an...

SEM217: Andrew Ang, BlackRock: Systematic Sustainable Alpha

Tuesday, September 13th @ 11:00-12:30 PM (ONLINE)

Andrew Ang, PhD, Managing Director, is Head of Factors, Sustainable and Solutions (FS-Squared). He also serves as Senior Advisor to BlackRock Retirement Solutions. As part of BlackRock Systematic, FS-squared is responsible for proprietary factor investing, delivering cutting-edge sustainable alpha, ESG outcomes and product innovation.


SEM217: Jose Menchero, Bloomberg: Advances in Estimating Factor Correlations

Tuesday, September 6th @ 11:00-12:30 PM RM 648 Evans Hall

Covariance matrices are used in finance for two basic purposes: predicting portfolio volatility and constructing optimal portfolios. Covariance matrices that work well for one use case may work poorly for the other use case, especially when the dimensionality is high. In this seminar, we present a technique for estimating large covariance matrices that produces reliable results for both volatility forecasting as well as portfolio optimization.



Tuesday, October 25th @ 11:00-12:30 PM (ONLINE)