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.
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 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 Journal of Risk.
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
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.