  For the past two decades investors have observed long memory and highly
correlated behavior of asset classes that does not fit into the framework of
Modern Portfolio Theory. Custom correlation and standard deviation estimators
consider normal distribution of returns and market efficiency hypothesis. It
forced investors to search more universal instruments of tail risk protection.
One of the possible solutions is a naive risk parity strategy, which avoids
estimation of expected returns and correlations. The authors develop the idea
further and propose a fractal distribution of returns as a core. This class of
distributions is more general as it does not imply strict limitations on risk
evolution. The proposed model allows for modifying a rule for volatility
estimation, thus, enhancing its explanatory power. It turns out that the latter
improves the performance metrics of an investment portfolio over the ten year
period. The fractal model of volatility plays a significant protective role
during the periods of market abnormal drawdowns. Consequently, it may be useful
for a wide range of asset managers which incorporate innovative risk models
into globally allocated portfolios.
