Scientific Beta

This paper studies the in-sample extreme risk of smart beta portfolios using the GARCH-EVT model. To validate the in-sample approach, we back-tested the methodology on smart beta indices constructed from long-term US data spanning 40 years and found that the methodology is robust and reliable. The VaR- and CVaR-based tests for the case of 1% tail probability indicated that, with a couple of exceptions, the model is statistically acceptable for all portfolios for both the left and the right tail.

Cap-weighted indices, although widely used as a passive investment vehicle, have two important drawbacks with far-reaching consequences — they represent concentrated portfolios and they are also exposed to risk factors that are not well rewarded. Smart beta indices have been introduced in an effort to improve on the two disadvantages; industry index providers have designed a new framework called smart factor investing that aims at tilting the portfolio towards better rewarded factors through stock selection or using different weighting schemes. Although clearly an improvement over cap-weighting, the industry index solutions are often based on ad-hoc stock-selection and weight allocation criteria prone to data-mining risks.

Since it has been demonstrated that smart beta indices have an improved performance and also sometimes lower volatility than the cap-weighted benchmarks, it is of practical and also theoretical importance to check if they exhibit higher extreme risk. This is an important question because the improvement in the risk-adjusted performance of smart beta strategies is usually demonstrated in terms of the Sharpe ratio. If, however, there is a substantial change in the thickness of the left tail of the smart beta return distribution which may be underestimated by volatility, then Sharpe ratios may mislead investors into thinking that smart beta strategies are superior.  

This paper studies the in-sample extreme risk of smart beta portfolios using the GARCH-EVT model. The main finding is that the total CVaR across strategies is primarily driven by the average volatility or the average tracking error for the case of absolute and relative returns, respectively. As a consequence, adopting a different weighting scheme can lead to superior performance compared to that of the corresponding cap-weighted index without a deterioration of the tail thickness of the left tail of smart beta returns. In other words, the additional performance does not come at the cost of an increase in tail thickness. Therefore, from a long-term investor perspective, focusing on volatility or tracking error management on a strategy level appears to be of first-order importance for total CVaR for the respective stock universes.