Estimation, model selection and evaluation of regression functions in a Least-squares Monte-Carlo framework

Abstract: This master thesis will investigate one solution to the problem issues with nested stochastic simulation arising when the future value of a portfolio need to be calculated. The solution investigated is the Least-squares Monte-Carlo method, where regression is used to obtain a proxy function for the given portfolio value. We will further investigate how to generate an optimal regression function that minimizes the number of terms in the regression function and reduces the risk of overtting the regression.