<北京大学数量经济与数理金融教育部重点实验室>学术报告——Deep Learning for Mortgage-Backed Securities Markets
Abstract: Predictions of prepayment speeds are mission-critical for investors, dealers, originators and other participants in the $10T Agency MBS market. We develop deep learning systems for prepayment speed prediction that set new accuracy standards, delivering performance-boosting edges to market participants. Our systems harness data of unprecedented size and granularity, covering monthly records for tens of millions of borrowers across the US over two decades. By uncovering hidden nonlinear patterns in borrower behavior at the individual loan level, they improve prediction accuracy for MBS pool CPRs by a full order of magnitude relative to the market’s current “gold standard.” Our predictions are robust in all market environments including the pandemic. Rigorous significance tests offer deep insights into the variables influencing predictions.
About the Speaker:
Kay Giesecke is Professor of Management Science & Engineering at Stanford University. He is the Director of the Advanced Financial Technologies Laboratory and the Director of the Mathematical and Computational Finance Program. Kay is a member of the Institute for Computational and Mathematical Engineering. He serves on the Governing Board and Scientific Advisory Board of the Consortium for Data Analytics in Risk. He is a member of the Council of the Bachelier Finance Society.
Kay is the founder, Executive Chairman and Chief Scientist of Infima Technologies, a capital markets technology company building transformative prediction systems for fixed-income market participants.
Kay is a financial technologist and engineer. He develops stochastic financial models, designs statistical methods for analyzing financial data, examines simulation and other numerical algorithms for solving the associated computational problems, and performs empirical analyses. Much of Kay's work is driven by important applications in areas such as credit risk management, investment management, and, most recently, housing finance. His research has been funded by the National Science Foundation, JP Morgan, State Street, Morgan Stanley, Swiss Re, American Express, Moody's, and several other organizations.
Kay has published numerous articles in operations research, probability, and finance journals. He has coauthored five United States patents. He is an Editor of Management Science in the Finance Area and an Associate Editor for Mathematical Finance, Operations Research, SIAM Journal on Financial Mathematics, Finance and Stochastics, Mathematics and Financial Economics, Journal of Credit Risk, and other journals.
Kay has won the JP Morgan AI Faculty Research Award (2019), the SIAM Financial Mathematics and Engineering Conference Paper Prize (2014), the Fama/DFA Prize for the Best Asset Pricing Paper in the Journal of Financial Economics (2011),and the Gauss Prize of the Society for Actuarial and Financial Mathematics of Germany (2003). Kay is the recipient of the Management Science & Engineering Graduate Teaching Award (2007), a DFG Postdoctoral Fellowship (2002-03), and a Deutsche Bundesbank Fellowship (2002).
Kay advises several financial technology startups and has been a consultant to banks, investment and risk management firms, governmental agencies, and supranational organizations.
Zoom Meeting: https://us02web.zoom.us/j/6817169181?pwd=Z1QrQm8xejJzME92NU54ZWRURjFLdz09
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