Скачать или смотреть Mike Ludkovski (UCSB): "Gaussian Process Models: From Option Greeks to Stochastic Impulse Control"

Abstract: In the first half of the talk I will survey Gaussian Process (GP) models which offer a flexible probabilistic framework for functional approximation and interpolation. GP training, kernel selection and observation noise modeling will be covered. The second half will consist of two applications: (i) statistical learning and uncertainty quantification of derivative contract sensitivities using GP gradients; (ii) GP surrogates for value- and policy-approximation within the Regression Monte Carlo framework for stochastic control problems.

Speaker Bio: Mike Ludkovski is a Professor of Statistics and Applied Probability at University of California Santa Barbara where he co-directs the Center for Financial Mathematics and Actuarial Research. Among his research interests are Monte Carlo techniques for optimal stopping/stochastic control, modeling of renewable energy markets, Gaussian process models for quantitative finance, and mortality analysis. His research has been supported by NSF, DOE, ARPA-E and CAS. He holds a Ph.D. in Operations Research and Financial Engineering from Princeton University and has held visiting positions at London School of Economics and Paris Dauphine University.