Risk-Based Portfolio Optimization in the Cryptocurrency World

53 Pages Posted: 25 Sep 2019 Last revised: 2 Nov 2019

See all articles by Tobias Burggraf

Tobias Burggraf

WHU - Otto Beisheim School of Management

Date Written: October 31, 2019


This study explores the performance of seven state-of-the-art risk-based portfolio optimization strategies from the perspective of a cryptocurrency investor. Analyzing the inverse volatility, minimum variance, l2-norm constrained minimum variance, l2-norm constrained maximum decorrelation, maximum diversification and risk parity portfolio, we find that most strategies systematically outperform individual cryptocurrencies and the equally-weighted benchmark portfolio. Further, a bull and bear market performance comparison as well as tail, extreme risk, and diversification analyses reveal that these strategies provide significant downside risk reduction. The results are robust to using different estimation windows, rebalancing periods and covariance estimation methodologies. Finally, our empirical results indicate that the maximum decorrelation portfolio is the worst strategy in terms of risk-adjusted return, while the long-only minimum variance portfolio is the best performing strategy.

Keywords: Portfolio Optimization, Cryptocurrencies, Investments

JEL Classification: G15, G41

Suggested Citation

Burggraf, Tobias, Risk-Based Portfolio Optimization in the Cryptocurrency World (October 31, 2019). Available at SSRN: https://ssrn.com/abstract=3454764 or http://dx.doi.org/10.2139/ssrn.3454764

Tobias Burggraf (Contact Author)

WHU - Otto Beisheim School of Management ( email )

Burgplatz 2
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