A fractal approach to long-only portfolio optimization is proposed. The quantitative system is based on naive risk parity approach. The core of the optimization scheme is a fractal distribution of returns , applied to estimation of the volatility law. Out-of-sample performance data has been represented in ten period of observation with half year and one year horizons. Implementation of fractal estimator of volatility improves all performance metrics of portfolio in comparison to the standard estimator of volatility. The efficiency of fractal estimator plays a significant protective role for the periods of market abnormal volatility and drawdowns, which allows beating the market in the long term perspective. The provided results may be useful for a wide range of quantitative investors, including hedge funds, robo-advisors and retail investors .
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 .
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A fractal approach to long-only portfolio optimization is proposed. The quantitative system is based on
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
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approach. The core of the optimization scheme is
strategy, which avoids estimation of expected returns and correlations. The authors develop the idea further and propose
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, applied to estimation of the volatility law. Out-of-sample performance data has been represented in ten period of observation with half year and one year horizons. Implementation of fractal estimator of volatility improves all
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
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portfolio in comparison to the standard estimator of volatility. The efficiency of fractal estimator
an investment portfolio over the ten year period. The fractal model of volatility
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for
during
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volatility and drawdowns, which allows beating the market in the long term perspective. The provided results
drawdowns. Consequently, it
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quantitative investors, including hedge funds, robo-advisors and retail investors
asset managers which incorporate innovative risk models into globally allocated portfolios