The value of robust statistical methods in portfolio construction arises because asset returns and other financial quantities often contain outliers. Outliers are data values that are well-separated from the bulk of the data values and are not predicted by univariate or multivariate normal distributions. Under normal distribution models, such an outlier sometimes occurs with exceedingly small probability. For example, if we fit a normal distribution to S & P 500 daily returns for various periods of time prior to the stock market crash of 1987, we find that the probability of occurrence of an event of that magnitude is so small that one would have to wait much longer than the history of civilization for another such occurrence. Large outliers of this type are not limited to situations with extreme market movements—one can find many such examples in individual asset returns.
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Keywords
- Efficient Frontier
- Exponentially Weight Move Average
- Influence Function
- Sharpe Ratio
- Robust Statistical Method
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(2005). Robust Statistical Methods for Portfolio Construction. In: Introduction to Modern Portfolio optimization with NUOPT and S-PLUS. Springer, New York, NY. https://doi.org/10.1007/978-0-387-27586-4_6
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DOI: https://doi.org/10.1007/978-0-387-27586-4_6
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