In this paper we assess the out-of-sample performance of two alternative quantitative portfolio optimization techniques - mean-variance and minimum variance optimization – and compare their performance with respect to a naive 1/N (or equally-weighted) portfolio and also to the market portfolio given by the Ibovespa. We focus on short selling-constrained portfolios and consider alternative estimators for the covariance matrices: sample covariance matrix, RiskMetrics, and three covariance estimators proposed by Ledoit and Wolf (2003), Ledoit and Wolf (2004a) and Ledoit and Wolf (2004b). Taking into account alternative portfolio re-balancing frequencies, we compute out-of-sample performance statistics which indicate that the quantitative approaches delivered improved results in terms of lower portfolio volatility and better risk-adjusted returns. Moreover, the use of more sophisticated estimators for the covariance matrix generated optimal portfolios with lower turnover over time.

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In recent years, Brazil has achieved substantial progress in capital market development by building a diversified investor base and expanding the menu of available financial instruments. In this context, we evaluated the invested Brazilian market portfolio for a period spanning 2005-15. This is a portfolio of all assets proportionally weighted by their market capitalization, and it is divided in eight broad categories: government bonds, equities, bank funding bonds, corporate bonds, real-estate, agribusiness, private-equity, and credit bonds. While the paper focuses on stylized facts related to market size, composition weighting and changes over time, the estimated market portfolio contains important information for policy makers and market participants alike.