6:00 PM Seminar Begins
7:30 PM Reception
Hybrid Event:
Fordham University
McNally Amphitheater
140 West 62nd Street
New York, NY 10023
Free Registration!
For Virtual Attendees: Please select virtual instead of member type upon registration.
Abstract:
We develop a framework to quantify the vulnerability of mutual funds to fire-sale spillover losses. We account for the first-mover incentive that results from the mismatch between the liquidity offered to redeeming investors and the liquidity of assets held by the funds. In our framework, the negative feedback loop between investors’ redemptions and price impact from asset sales leads to an aggregate change in funds’ NAV, which is determined as a fixed point of a nonlinear mapping. We show that a higher concentration of first movers increases the aggregate vulnerability of the system, as measured by the ratio between endogenous losses due to fund redemptions and exogenous losses due to initial price shocks only. When calibrated to U.S. mutual funds, our model shows that, in stressed market scenarios, spillover losses are significantly amplified through a nonlinear response to initial shocks that results from the first-mover incentive. Higher spillover losses provide a stronger incentive to redeem early, further increasing fire-sale losses and the transmission of shocks through overlapping portfolio holdings.
Bio:
Agostino Capponi is a Professor in the Department of Industrial Engineering and Operations Research at Columbia University, where he is also a member of the Data Science Institute and the founding director of the Columbia Center for Digital Finance and Technology. His current research interests are in financial technology, machine learning in finance, market microstructure, systemic and liquidity risk, climate finance, energy markets, and economic networks. Agostino's research has been funded by major agencies, including NSF, DARPA, DOE, IBM, GRI, INET, Ripple, Stellar, and the Ethereum foundation. His research has been recognized with the 2018 NSF CAREER award, a JP Morgan AI Research Faculty award, and the UBRI Innovator award. His research has also been covered by various media outlets, including Bloomberg, the Financial Times, Vox, and Politico. Agostino is a fellow of the crypto and blockchain economics research forum, and an academic fellow of Alibaba's Luohan academy. He serves as an editor of Management Science in the Finance Department, co-editor of Mathematics and Financial Economics, and financial engineering area editor of Operations Research. He has held editorial positions at several major journals in his field, such as the SIAM Journal on Financial Mathematics, Mathematical Finance, Finance and Stochastics, Operations Research Letters, Stochastic Systems, and Stochastic Models. Agostino is a past Chair of the SIAG/FME Activity Group and of the INFORMS Finance Section, and is currently a member of the Council of the Bachelier Finance Society. Agostino is co-editor of the book Machine Learning and Data Sciences for Financial Markets: A Guide to Contemporary Practices, published in 2023 by the Cambridge University press.
