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The paper presented in this video, from the field of financial mathematics, addresses the problem of building optimal loan portfolios and develops a novel computational method to do so even if with an infinite number of loans. The new tool was tested on a data-set of 120 million mortgage loans, and was able to solve this high-dimensional problem. As KAY GIESECKE explains, the applied method is an asymptotic approximation approach: To solve the problem at hand, the solution to a problem with fewer dimensions is computed, and as the portfolio grows larger again, the solution “grows” into the solution of the actual problem.


Kay Giesecke is an Associate Professor of Management Science and Engineering, as well as the Director of the Stanford Centre for Financial and Risk Analysis, at Stanford University.

Giesecke’s research seeks to explain and improve risk management at financial institutions, particularly systemic risks in financial markets. For instance, Giesecke seeks to find a method which addresses the issue of loan portfolios. He advises several private and public institutions and organizations on matters of financial risks and holds a U.S. patent on a method for qualifying credit risk with incomplete information.


Stanford University

"Stanford University, located between San Francisco and San Jose in the heart of California's Silicon Valley, is one of the world's leading teaching and research universities. Since its opening in 1891, Stanford has been dedicated to finding solutions to big challenges and to preparing students for leadership in a complex world." ( Source )
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Original publication

Large-Scale Loan Portfolio Selection

Giesecke Kay, Sirignano Justin and Tsoukalas Gerry
Stanford University
Published in 2016

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Default Clustering in Large Portfolios: Typical Events

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