GSOC 2013: IID Assumptions in Performance Measurement

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GSOC2013Google Summer of Code for 2013 has been announced and organizations such as R are beginning to assemble ideas for student projects this summer. If you’re an interested student, there’s a list of project proposals on the R wiki. If you’re considering being a mentor, post a project idea on the site soon – project outlines end up being 1-2 pages of text, plus references – and they should be up on the wiki by mid-to-late March. Google will use the listed projects outlines as part of their criteria for accepting the R project for another year of GSoC and in their preliminary budgeting of slots.

I’ve posted one project idea so far, one that would extend PerformanceAnalytics’ standard tools for analysis to better deal with various violations of a standard assumption that returns are IID (that is, each observation is drawn from an identical distribution and is independent of other observations).

Observable autocorrelation is one of those violations. There have been a number of different approaches for addressing autocorrelation in financial data that have been discussed in the literature. Various authors, such as Lo (2002) and Burghardt, et. al. (2012), have noted that the effects of autocorrelation can be huge, but are largely ignored in practice. Burghardt observes that the effects are particularly interesting when measuring drawdowns, a widely used performance measure that describes the performance path of an investment. Recently, Bailey and Lopez del Prado (2013) have developed a closed-form solution for the estimating drawdown potential, without having to assume IID cashflows.

There’s more detail at the project site, including a long list of references. I’d be glad to hear from you if you have any ideas, thoughts, or even code in this vein (or others). Here are a few of the references to get you thinking:

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