Seminar (Dept. of Finance)

Title: Forecasting and Managing Correlation Risks

Speaker: Sophia Zhengzi Li, Assistant professor of finance, Rutgers Business School

Time: 10:00-11:30, Mar.3, 2023(Friday)

Venue: Online Meeting

Language: Chinese&English

ABSTRACT:

We propose a novel and easy-to-implement framework for forecasting correlation risks based on a large set of salient realized correlation features and the sparsity-encouraging LASSO technique. Considering the universe of S&P 500 stocks, we find that the new approach manifests in statistically superior out-of-sample forecasts compared to commonly used procedures. We further demonstrate how the forecasts translate into significant economic gains in the form of higher pairs trading profits, better equity premium predictions, more accurate portfolio risk targeting, and superior overall risk control and minimization.

SHORT BIOGRAPHY:

Sophia Zhengzi Li is an assistant professor of finance at Rutgers Business School. She previously taught at Michigan State University from 2013 to 2017. Her main research areas are empirical asset pricing, financial econometrics, big data, and machine learning, and her research interests include volatility, intraday analysis, return prediction, tail risk, news, disagreement, and shareholder voting. She received the First Prize of 2012 Morgan Stanley Prize for Excellence in Financial Markets and the Best Paper Award at the Triple Crown Conference. Her work has been published in leading academic journals, including the Review of Financial Studies, Journal of Financial Economics, Journal of Finance and Quantitative Analysis, and Journal of Econometrics. Her work has been presented at NBER Summer Institute, NBER-NSF Time Series Conference, American Finance Association, European Finance Association, and SFS Cavalcade North America.