Comparação de carteiras otimizadas segundo o critério média-variância formadas através de estimativas robustas de risco e retorno

Este trabalho examinou as características de carteiras compostas por ações e otimizadas segundo o critério de média-variância e formadas através de estimativas robustas de risco e retorno. A motivação para isto é a distribuição típica de ativos financeiros (que apresenta outliers e mais curtose que a distribuição normal). Para comparação entre as carteiras, foram consideradas suas propriedades: estabilidade, variabilidade e os índices de Sharpe obtidos pelas mesmas. O resultado geral mostra que estas carteiras obtidas através de estimativas robustas de risco e retorno apresentam melhoras em sua estabilidade e variabilidade, no entanto, esta melhora é insuficiente para diferenciar os índices de Sharpe alcançados pelas mesmas das carteiras obtidas através de método de máxima verossimilhança para estimativas de risco e retorno.

Semiparametric estimation and inference using doubly robust moment conditions

We study semiparametric two-step estimators which have the same structure as parametric doubly robust estimators in their second step. The key difference is that we do not impose any parametric restriction on the nuisance functions that are estimated in a first stage, but retain a fully nonparametric model instead. We call these estimators semiparametric doubly robust estimators (SDREs), and show that they possess superior theoretical and practical properties compared to generic semiparametric two-step estimators. In particular, our estimators have substantially smaller first-order bias, allow for a wider range of nonparametric first-stage estimates, rate-optimal choices of smoothing parameters and data-driven estimates thereof, and their stochastic behavior can be well-approximated by classical first-order asymptotics. SDREs exist for a wide range of parameters of interest, particularly in semiparametric missing data and causal inference models. We illustrate our method with a simulation exercise.

Robust statistical modeling of portfolios

Atypical points in the data may result in meaningless e±cient frontiers. This follows since portfolios constructed using classical estimates may re°ect neither the usual nor the unusual days patterns. On the other hand, portfolios constructed using robust approaches are able to capture just the dynamics of the usual days, which constitute the majority of the business days. In this paper we propose an statistical model and a robust estimation procedure to obtain an e±cient frontier which would take into account the behavior of both the usual and most of the atypical days. We show, using real data and simulations, that portfolios constructed in this way require less frequent rebalancing, and may yield higher expected returns for any risk level.