利用日内数据选择最小方差投资组合:BM&FBovespa数据不同实现测度的实证比较

F. Ziegelmann, B. Borges, J. Caldeira
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引用次数: 7

摘要

本文针对最小方差投资组合问题,探讨了由每日数据和已实现测度得到的不同协方差矩阵的条件估计和无条件估计。每天的数据以同步的方式和不同步的方式进行采样。为了便于比较,我们还使用了每日数据估计器。这项工作的主要贡献是集中在巴西情景的经验性质。我们评估了在圣保罗证券交易所(BM&FBovespa)交易的一组30只股票的所得投资组合的一些样本外绩效指标。结果表明,基于高频数据的标量vt-VECH模型的收益条件协方差矩阵的估计可以获得可观的收益,降低了投资组合的风险,提高了经风险调整后的平均收益,降低了换手率
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Selection of Minimum Variance Portfolio Using Intraday Data: An Empirical Comparison Among Different Realized Measures for BM&FBovespa Data
This paper explores different covariance matrix estimators, either the conditional or the unconditional versions, obtained via intradaily data and named realized measures, to the minimum variance portfolio selection problem. Intradaily data are sampled in a synchronized manner as well as in a unsynchronized version. For sake of comparison, we alsouse daily data estimators. The major contribution of this work has an empirical nature focused on the Brazilian scenario. We evaluate some out-of-sample performance indexes of the obtained portfolios for a set of 30 stocks traded on the Sao Paulo stock exchange (BM&FBovespa). The results show that the estimator of the conditional covariance matrix of returns coming from a scalar vt-VECH model based on higher frequency data leads to substantial earnings, reducing the portfolio risk, increasing the average adjustedby risk return and decreasing the turnover
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