Tuesday, September 20th @ 11:00-12:30 PM (ONLINE)
We argue that decentralized finance (DeFi) can be used to reorganize forex trading and market-making. Specifically, we show that an automated market-making (AMM) cross-settlement mechanism for digital assets on interoperable blockchains, handling central bank digital currencies (CBDCs) and stable coins, is a promising venue. We develop an innovative approach for generating fair exchange rates for on-chain assets consistent with traditional off-chain markets. Finally, we illustrate the efficacy of our approach on realized FX rates for G-10 currencies.
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 Approximate Message Passing (AMP) algorithm. In theoretical "spiked" models, EB-PCA achieves Bayes-optimal estimation accuracy in the same settings as an oracle Bayes AMP procedure that knows the true priors. Empirically, EB-PCA significantly improves over PCA when there is strong prior structure, both in simulation and on quantitative benchmarks constructed from the 1000 Genomes Project and the International HapMap Project. An illustration is presented for analysis of gene expression data obtained by single-cell RNA-seq.
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 collection of averages. In the context of a 1-factor model, JSE substantially improves optimization-based metrics for the minimum variance portfolio. With certain extra information, JSE is a consistent estimator of the leading eigenvector. This is based on joint work with Lisa Goldberg, Hubeyb Gurdogan, and Alex Shkolnik.
SEM217: Tizian Otto, University of Hamburg (visiting Stanford University): Estimating Stock Market Betas via Machine Learning
Tuesday, October 18th @ 11:00-12:30 PM
This paper evaluates the predictive performance of machine learning techniques in estimating time-varying market betas of U.S. stocks. Compared to established estimators, machine learning-based approaches outperform from both a statistical and an economic perspective. They provide the lowest forecast errors and lead to truly ex-post market-neutral portfolios. Among the different techniques, random forests perform the best overall. Moreover, the inherent model complexity is strongly time-varying. Historical betas, as well as turnover and size signals, are the most important predictors. Compared to linear regressions, interactions and nonlinear effects substantially enhance predictive performance.
SEM217: Lisa Goldberg, CDAR & BlackRock
Tuesday, November 1st @ 11:00-12:30 PM, RM 648 Evans Hall
SEM 217: Debashis Paul, UC Davis
Tuesday, November 8th @ 11:00-12:30 PM (ONLINE)
SEM217: Petter Kolm, NYU
Tuesday, November 15th @ 11:00-12:30 PM (ONLINE)
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, 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.