Dynamic programming is the essential tool in dynamic economic analysis. Problems such as portfolio allocation for individuals and optimal economic growth are typical examples. Numerical methods typically approximate the value function. Recent work has focused on making numerical methods more...

An optimization method is developed for constructing investment portfolios which stochastically dominate a given benchmark for all decreasing absolute risk-averse investors, using Quadratic Programming. The method is applied to standard data sets of historical returns of equity price reversal...

We study the gap between the state pension provided by the Italian pension system pre-Dini reform and post-Dini reform. The goal is to fill the gap between the old and the new pension by joining a defined contribution pension scheme and adopting an optimal investment strategy that is...

In this contribution we propose a dynamic tracking error problem and we consider the problem of monitoring at discrete point the shortfall of the portfolio below a set of given reference levels of wealth. We formulate and solve the resulting dynamic optimization problem using stochastic...

Portfolio optimization literature has come quite far in the decades since the first publication, and many modern models are formulated using second-order cone constraints and take discrete decisions into consideration. In this study, we consider both single-period and multi-period portfolio...

This paper develops an approximate closed-form optimal portfolio allocation formula for a spot asset whose variance follows a GARCH(1,1) process. We consider an investor with constant relative risk aversion (CRRA) utility who wants to maximize the expected utility from terminal wealth under a...

The existing literature deals with the optimal investment strategy of defined benefit (DB) or defined contribution (DC) pension plans. This paper's objective is to compare the optimal policies of different types of pension plans. This is done by first defining an original framework, which is...

We develop a dynamic programming methodology that seeks to maximize investor outcomes over multiple, potentially competing goals (such as upgrading a home, paying college tuition, or maintaining an income stream in retirement), even when financial resources are limited. Unlike Monte Carlo...

We present a simulation-and-regression method for solving dynamic portfolio optimization problems in the presence of general transaction costs, liquidity costs and market impact. This method extends the classical least squares Monte Carlo algorithm to incorporate switching costs, corresponding...

We propose to impose a weighted L1 and squared L2 norm penalty on the portfolio weights to improve out-of-sample (OOS) performances of portfolio optimization when the number of assets becomes large. We show that under certain conditions, the realized risk of the optimal minimum variance...