By William Cai

Digital Assets share some similarities to traditional assets such as stocks, bonds, and commodities. However, careful consideration is needed to apply traditional methods in their investment and risk management. For an investor in digital assets, diversification is an important topic. How can one use the concept of diversification to help achieve returns while managing risk? Diversification is a critical tool in the investment of traditional assets, but it could lead to undesired results when blindly applied to an emergent asset class.

In our view, one of the most direct plug-and-play applications of diversification is mean-variance optimization (“MV optimization”). Specific applications of MV optimization can vary, but the key driver is to balance return to risk by looking at historical prices, volatility and correlation of the assets. Generally, the less correlated the assets, the higher the benefit of diversification to achieve higher risk adjusted returns. Below is a snapshot showing the correlation matrix of several cryptocurrencies for the year 2018.

1 Year Correlation (12/29/2017 – 12/31/2018)

 

BTC

ETH

XRP

XMR

LTC

BCH

BTC

100.0%

74.5%

69.6%

79.9%

80.2%

72.1%

ETH

74.5%

100.0%

72.4%

78.1%

81.6%

69.2%

XRP

69.6%

72.4%

100.0%

66.4%

72.2%

60.9%

XMR

79.9%

78.1%

66.4%

100.0%

80.8%

74.2%

LTC

80.2%

81.6%

72.2%

80.8%

100.0%

71.0%

BCH

72.1%

69.2%

60.9%

74.2%

71.0%

100.0%

Data source: Kaiko

From these numbers, the intra-digital asset correlation looks to be quite high. The diversification benefit of such a portfolio appears to be muted. Of course, using different time horizons and time frames, one might see lower (or higher) correlation numbers. But more importantly, the MV optimization might not be the sole framework for diversification, as its calculation is based on historical prices and many digital assets have a limited historical time series (when compared to stocks or bonds). With stocks, for example, it would be not wise for an OTC/Pink sheet stock investor to try to use MV optimization only to manage and rebalance a portfolio of such stocks.

We are not advocating one diversification framework over another. And it is possible that currently, the benefits of diversification are muted (and untested), regardless of the method. But we would like to stress that traditional methods should not be blindly and carelessly applied. Lastly, we continue to observe and study the digital asset space closely, both in terms of the trading market and the underlying technology, in order to adapt and evolve as the space continues to grow.

ABOUT THE AUTHOR

William Cai is a Partner of Wilshire Phoenix and brings with him a vast experience of quantitative finance and risk management. Before joining Wilshire Phoenix, Will was a trader at JPMorgan for over ten years, managing billion-dollar risk portfolios across different asset classes including credit, equities, and most recently in commodity futures. In addition to his trading responsibilities, Will managed teams, oversaw projects, and had extensive experience with regulatory and legal matters in structured products.  Will graduated from Harvard University with a Bachelor of Arts in Physics and holds a Master of Science in Mathematics in Finance from New York University.

THE INFORMATION CONTAINED HEREIN REPRESENTS THE AUTHOR’S SUBJECTIVE BELIEF AND IS NOT AND SHOULD NOT BE CONSTRUED AS INVESTMENT ADVICE. THE INFORMATION AND OPINIONS PROVIDED HEREIN SHOULD NOT BE TAKEN AS SPECIFIC ADVICE ON THE MERITS OF ANY INVESTMENT DECISION. INVESTORS SHOULD MAKE THEIR OWN DECISIONS REGARDING THE INVESTMENTS MENTIONED HEREIN AND THEIR PROSPECTS BASED ON SUCH INVESTORS’ OWN REVIEW OF PUBLICLY AVAILABLE INFORMATION AND SHOULD NOT RELY ON THE INFORMATION CONTAINED HEREIN. NEITHER THE AUTHOR NOR ANY OF HIS AFFILIATES ACCEPTS ANY LIABILITY WHATSOEVER FOR ANY DIRECT OR CONSEQUENTIAL LOSS HOWSOEVER ARISING, DIRECTLY OR INDIRECTLY, FROM ANY USE OF THE INFORMATION CONTAINED HEREIN.

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