Low volatility strategies have become more popular among investors in recent years. A topic that may interest the Quantopian community is robust methods for covariance estimation, and would be useful in building a robust minimum variance algorithm for trading. Take a look at Jim Gatheral's paper on using random matrix theory to filter sample covariance matrices. He shows that a minimum variance strategy in equities has stronger performance when using the filtered covariance matrix than the sample covariance or factor-based covariance matrices like Barra's. The motivation for using RMT is to separate a true covariance signal from noise by ascertaining how likely are observed volatilities and correlations to occur in randomly generated data, often with eigenvalue decompositions.
I agree that this is a very important topic and many people will just estimate a covariance matrix and not realize how brittle that is. Some experiments here would certainly be interesting. scikit-learn has support for various robust covariance estimation methods and good explanations of the pros and cons: http://scikit-learn.org/stable/modules/covariance.html Would be interesting to experiment with that.
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I think Meucci covers this in his book, and I'd be surprised if it wasn't in Grinold's too (but I haven't read it ).
The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by Quantopian.
In addition, the material offers no opinion with respect to the suitability of any security or specific investment. No information contained herein should be regarded as a suggestion to engage in or refrain from any investment-related course of action as none of Quantopian nor any of its affiliates is undertaking to provide investment advice, act as an adviser to any plan or entity subject to the Employee Retirement Income Security Act of 1974, as amended, individual retirement account or individual retirement annuity, or give advice in a fiduciary capacity with respect to the materials presented herein. If you are an individual retirement or other investor, contact your financial advisor or other fiduciary unrelated to Quantopian about whether any given investment idea, strategy, product or service described herein may be appropriate for your circumstances. All investments involve risk, including loss of principal. Quantopian makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.