Heuristic portfolio construction techniques include the equally-weighted portfolio (1/N) and the inverse volatility portfolio (IVP), which is based on the low-volatility effect. They can be assembled much more efficiently than Markwoitz' mean-variance model that uses optimization of the asset returns covariance matrix. Markowitz' global minimum-variance portfolio (GMV) is known to promise high in-sample performance, but can disappoint out-of-sample.

Maillard (2010) derives IVP from the risk parity portfolio I think, but to make it clear, the two (IVP and risk parity) are not the same. The paper stops at the derivation, and only evaluates the inverse-beta portfolio instead, which is also based on the low-volatility effect.

de Prado (2018) went on to apply unsupervised learning algorithm called hierarchical clustering to form portfolios that outperform both GMV and the IVP by finding a compromise between the two since they are extreme opposites of one another in terms of the type of risk they minimize.

The (heuristic) 1/N portfolio is a benchmark that is tough to beat out-of-sample (de Miguel 2009), but how about the IVP? does the IVP consistently outperform Markowitz' GMV portfolio in-sample and out-of-sample? and does the IVP outperform the 1/N? Or are these three roughly the same in their chances of coming out on top, based on repeated simulations of new returns/volatility data?

  • $\begingroup$ my guess is the IVP would perform well in specific decades of stock market data, and for other decades perform poorly. making it inconsistent unlike efficient portfolios that seem to anchor their performance regardless of market, making IVP one of those strategies that depends on the current regime it's estimated in $\endgroup$
    – develarist
    Jul 17, 2020 at 12:56


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