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March 2018

Alex Shkolnik presents “The Dispersion Bias” at the Financial Risks International Forum in Paris

March 26 - March 27

The explosive growth in the area of data-driven risk factor identification elevates the importance of analyzing and, to the extent possible, mitigating estimation error.  Using random matrix theory, we show how a large, estimation-error-induced bias in the sample eigenvectors of factor-based covariance matrices affects portfolio construction and risk estimation.  We develop a bias correction approach for the first sample eigenvector which corrects the problems of portfolio construction and risk estimation.  Our approach is distinct from the regularization and eigenvalue shrinkage methods found in…

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Lisa Goldberg presents “The Dispersion Bias” at NYU

March 27 @ 5:30 pm - 7:00 pm

Abstract: Estimation error has plagued quantitative finance since Harry Markowitz launched modern portfolio theory in 1952.  Using random matrix theory, we characterize a source of bias in the sample eigenvectors of financial covariance matrices.  Unchecked, the bias distorts weights of minimum variance portfolios and leads to risk forecasts that are severely biased downward.  To address these issues, we develop an eigenvector bias correction.  Our approach is distinct from the regularization and eigenvalue shrinkage methods found in the literature.   We provide…

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