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Beta of bitcoin
The lack of a significant relationship to the Foreign Currency factor in the Two Sigma Factor Lens is interesting and perhaps unexpected, given both the factor and Bitcoin in this instance12 are expressed relative to the USD. To summarize, Bitcoin is not easily explained by the Two Sigma Factor Lens, nor is it substantially correlated to other currencies or any of the major commodities.
This leaves us with the following question that we will spend the rest of this Street View analyzing: are there any common risk drivers among cryptocurrencies themselves, or are they each their own beast, carrying a unique, idiosyncratic return even relative to each other? Correlations Among Crypto Assets To examine common risk drivers across crypto, we first need to establish a universe of crypto assets.
We selected the 10 coins,13 including Bitcoin, Ethereum, and Dogecoin, that had the highest day trading volume as of April 19th, according to CoinMarketCap, and that had at least 3 years of price history. A few interesting observations from the correlations across crypto assets: first, there was not a single negative correlation in the entire matrix. All of the crypto assets exhibited positive correlations. This is particularly interesting because of the different use cases of these two assets.
ETH has that use case as well, but it expands on that by representing a platform on which to build applications using its cryptocurrency, ether. While the correlation has always been in positive territory, the correlation between the two was much lower a few years ago. It substantially picked up around the Q1 crypto crash when both coins suffered their worst quarterly losses up to that point as regulatory scrutiny on crypto was picking up and tech giants, like Facebook and Google, banned cryptocurrency advertising.
The correlation has declined a bit since then, but has been picking up again more recently. In summary, we see fairly high correlations among the ten coins in our universe. Even BTC and ETH, which are two seemingly very different assets, have exhibited a high correlation, especially in recent years. It appears that there are common risks within this crypto universe, which we will explore in more detail in the next section. The PCs are fully data-driven and say little about economic intuition, thus making the underlying risk less clearly identifiable.
However, this analysis will tell us the extent to which there are common risks how many there are, how influential they are, etc. To put these results in context, we can compare them to the PCA results of traditional macro assets. For example, a PCA on the U. As mentioned earlier, PCs are difficult to put economic intuition behind, but we can look at the portfolio weights for each PC as denoted by their eigenvectors to understand their constructions.
Exhibit 9 displays those portfolio weights eigenvectors for the first and second PCs. It appears to be capturing the unique risk of DOGE relative to all of the other coins. Conclusion Crypto has been gaining a lot of attention recently. There are many ways to obtain crypto exposure, including by investing directly in coins on centralized and decentralized exchanges, through derivative instruments like swaps and futures, and via stocks that are investing in blockchain technology.
The beta calculator offers additional flexibility, such as: - Reference index: apply the same reference index for all companies in your list regardless of their country. About Beta Standard beta is co-called levered, which means that it reflects the capital structure of the company including the financial risk linked to the debt level. Unlevered beta or ungeared beta compares the risk of an unlevered company i.
Unlevered beta is useful when comparing companies with different capital structures as it focuses on the equity risk. Unlevered beta is generally lower than the levered beta.
TThey were outbid by Ken Griffin, the founder of Citadel, a large multi-strategy hedge fund and market maker Citadel Securities.
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