Kay Giesecke

Kay Giesecke is Associate Professor of Management Science & Engineering at Stanford University and the Paul Pigott Faculty Scholar in the School of Engineering. He is the Director of the Center for Financial and Risk Analytics and the Quantitative Finance Certificate Program. He is the Co-Chair 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.

Kay is a financial 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, American Express, and several other organizations.

Kay has published numerous articles in operations research, probability, and finance journals. He has coauthored five United States patents. He serves on the editorial boards of Mathematical Finance,Operations Research, SIAM Journal on Financial Mathematics, Mathematics and Financial Economics, Journal of Risk and other journals.

Kay’s papers have won 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.

Related Research

Deep Learning/Machine Learning

Working Papers

2016: Kay Giesecke, Justin Sirignano, and Apaar Sadhwani, "Deep Learning for Mortgage Risk"