Average drawdown risk reduction and risk tolerances

We investigate the effects of average drawdown risk reduction on US mutual funds. Due to numerous evidence of the asymmetric distribution of portfolio returns, the asymmetric risk measures have extensively been used in risk management during the recent decades with extensive usages on the n-degree lower partial moment (LPM) methodology. Unlike the previous literature, we use the n-degree average drawdown risk measure, which is a special case of n-degree LPM, to empirically investigate the impacts of n-degree average drawdown risk reduction on the risk tolerances generated by the US mutual funds. The evidence shows that skewness does not impose any significant problem on the n-degree A-DRM model. Moreover, the effect of changing the tolerances of average drawdown risk in the n-degree A-DRM models is a reduction in the fund returns. The n-degree CA-DRM optimization model reduces investors' risk more than other models. Thus, the A-DRM can be accommodated with risk-averse investors' approach. The efficient set of mean-variance choices from the investment opportunity set, as described by Markowitz, shows that the n-degree CA-DRM algorithms create this set with lower risk than other algorithms. It implies that the mean-variance opportunity set generated by the n-degree CA-DRM creates lower risk for a given return than covariance and CLPM.