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Solving Market Index Biases Using Minimum Risk Indices

机译：使用最小风险指数解决市场指数偏差

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摘要

The market, in contrary to what is defended in traditional finance, is not an efficient zero-sum game where hypotheses of the CAPM are fullfilled. In that situation, the market portfolio is not located in the efficient frontier, and passive investments are not optimal but biased. In this paper, the sample, construction, efficiency and active biases are defined, and tracking error is also analysed. We propose Minimum Risk Indices (MRI) to solve market index biases, and to provide investors with investments closer to the efficient frontier. MRJ (using a Value-at-Risk Minimization approach) are calculated for three stock markets achieving interesting results. Our indices are less risky and more profitable than current Market Indices in the Argentinian and Spanish markets. Two innovations must be outlined: First, an error dimension has been included in the backtesting and, second, the Sharpe Ratio has been used to select the 'best' model from all models presented
机译：与传统金融所捍卫的市场相反，市场并非有效的零和博弈，无法完全满足CAPM的假设。在那种情况下，市场投资组合并不位于有效的边界，被动投资不是最优的而是有偏见的。本文定义了样本，构造，效率和有源偏置，并分析了跟踪误差。我们提出最低风险指数（MRI）以解决市场指数偏差，并为投资者提供更接近有效边界的投资。针对三个股票市场计算了MRJ（使用风险最小化方法），获得了有趣的结果。与当前阿根廷和西班牙市场的市场指数相比，我们的指数风险更低，更有利可图。必须概述两个创新：首先，回溯测试中包括了一个错误维度，其次，使用了Sharpe Ratio来从提出的所有模型中选择“最佳”模型。

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