Home / Events

 

Life-Cycle Investment and Consumption Retirement Strategy via Markov Chain Monte Carlo with R

Qing Maggie Liu, Department of Mathematics, Washington University in St. Louis

March 29, 2013 - 3:00 pm to 4:00 pm
Location: Cupples I, Room 199 | Host: Prof. Renato Feres

A Senior Honors Thesis Presentation.
Abstract: This thesis develops a simulation-based approach to a maximum expected utility (MEU) portfolio allocation problems between investment and consumption. Traditional way to solve a maximum utility is gradient-based approach, which performance is highly dependent on the analytical properties of the utility function, such as convexity, boundedness, and smoothness. However, in portfolio problems, the expected utility is generally not analytical available. MEU requires computation of expected utility and its optimization over the decision variable. And in this type of problems, the complexity of the boundary conditions is another problem. A simulation-based methos avoids the calculation of derivatives and also allows for functional optimization. The algorithm combines MCMC with the insights of simulated annealing and evolutionary Monte Carlo. It can exploit conjugate utility functions and latent variables in the relevant predictive density for efficient simulation. We begin this methodology with a portfolio problem with estimation risk and CRRA utility.

 

Contact Us | Home Page
Department of Mathematics | Washington University in St. Louis | One Brookings Drive, St. Louis, MO 63130-4899 | contact@math.wustl.edu