AMS Seminar w/ Mete Soner (Princeton University) on Zoom

Title: Monte-Carlo methods for high-dimensional problems in quantitative finance
Abstract: Stochastic optimal control has been an effective tool for many problems in quantitative finance and financial economics. Although it provides much needed quantitative modeling for such problems, until recently it has been intractable in high-dimensional settings. However, several recent studies report impressive numerical results: Cheredito et al. studied the optimal stopping problem (a problem closely connected to pricing American-type options in quantitative finance finale) providing tight error bounds and an efficient algorithm in problems in up to 100 dimensions.  Buehler et al., on the other hand, consider the problem of hedging and again report results for high-dimensional problems that were intractable. These papers use a Monte Carlo type algorithm combined with deep neural networks proposed by E. Han and Jentzen.  In this talk I will outline this approach and discuss its properties.  Numerical results, while validating the power of the method in high dimensions, also show the dependence on the dimension and the size of the training data.  This is joint work with Max Reppen of Boston University.

Here is the link and the meeting info:
https://wse.zoom.us/j/98200438645?pwd=d3M3WEljc0sxd3BRQldUU3dudzhvdz09
Meeting ID: 982 0043 8645
Passcode: 374212
Enjoy.