文章基本信息

标题：Quantitative Portfolio Optimization Techniques Applied to the Brazilian Stock Market
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作者：André Alves Portela Santos ; Cristina Tessari
期刊名称：Brazilian Review of Finance
印刷版ISSN：1984-5146
出版年度：2012
卷号：10
期号：3
页码：369-393
语种：Portuguese
出版社：Link to the Brazilian Society of Finance
摘要：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.
其他摘要：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.
关键词：Optimization;estimation error;volatility;Otimização;erro de estimação;volatilidade
其他关键词：Economics; Business; Statistics; Mathematics; Econometrics;;Optimization; estimation error; volatility;G11; G32.