The charts above show the historical prices of the leading cryptocurrencies, organized into distinct groups, each targeting specific functionalities:
While this categorization provides a snapshot of the diverse technologies and applications within the cryptocurrency market, it's important to recognize the fluidity within these categories. For instance, platforms originally designed for specific purposes, such as Solana's high-performance blockchain for dApps, have expanded their utility to support DeFi applications and services, demonstrating a fluidity that transcends traditional categorization. Similarly, Chainlink, while a Web3 token providing oracle services, has become integral to the functioning of DeFi protocols. This overlap highlights the dynamic nature of cryptocurrencies, necessitating a flexible and nuanced approach to understanding this ever evolving market.
The chart above shows a heat map depicting the correlation coefficients among various cryptocurrency tokens. Each token is represented on both the x and y axes, with intersections revealing the strength and direction of their correlation. A correlation coefficient of +1 indicates a perfect positive correlation, implying that two tokens moved in the same direction during the specified time window. Conversely, a coefficient of -1 signifies that the tokens moved in opposite directions. Colors range from deep blue, indicating a negative correlation, to dark red, signifying a strong positive correlation.
The correlation coefficient is important to consider for diversification because it helps investors assess the potential benefits of including different assets in their portfolios. Diversification is the practice of spreading investments across different asset classes to reduce risk. In his book Principles, Ray Dalio called diversification the “Holy Grail of Investing”. He realized that with fifteen to twenty uncorrelated return streams, he could dramatically reduce the risks without reducing the expected returns.
The minimum spanning tree (MST) simplifies the data from the correlation matrix above by retaining only the strongest correlations. If two tokens are connected, it means that they are positively correlated and that they tend to move in tandem. By analyzing the structure of the MST, one can identify clusters of tokens that move together. This visual tool is especially beneficial when considering international portfolio diversification. In fact, Marti, Gautier, et al. (2017) found that the optimal Markowitz portfolio is found at the outskirts of the tree and that the tree shrinks during a crisis.
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