![]() Exhibit 9 displays those portfolio weights (eigenvectors) for the first and second PCs. 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. Let’s dive into those first two crypto PCs in more detail. 22 So, while there were two major risk drivers in this seemingly diverse crypto market, when compared with more traditional assets, the first few PCs weren’t as explanatory. ![]() For context, broad-based equity indices like the S&P 500 exhibited 90% of the variance. This is a relatively high amount of residual risk. 91% of Bitcoin’s risk since January 2015 was unexplained. The exhibit below shows how the Two Sigma Factor Lens, which does not include a crypto factor, attempts to explain Bitcoin. For a brief overview of some of the ways that investors can transact in crypto and obtain other types of crypto exposures, please see Appendix 1 in the pdf version of this article. 10 Bitcoin is the largest crypto asset by market cap and is arguably the most canonical. In order to understand the extent to which crypto is correlated with factors in traditional financial markets, let’s start by analyzing Bitcoin’s relationships with the factors in the Two Sigma Factor Lens. To our knowledge, most financial risk models do not incorporate idiosyncratic crypto risk as a “factor.” If crypto has mostly unique risks and returns that are specific to the crypto market, then any portfolio with allocations to crypto will have residual, or unexplained risk, according to these factor risk models. Many established risk models, like our own Two Sigma Factor Lens, are constructed to explain the majority of risks and returns in traditional financial portfolios, which often include heavy allocations to well-established asset classes like stocks, bonds, commodities, and fiat currencies, as well as to well-known investment strategies such as trend following in macro asset classes and value investing in stocks. Using Financial Risk Factors to Explain Risk in Crypto We then seek to understand the extent to which there are influential, common risk drivers across crypto assets using a statistical technique called Principal Components Analysis. We explore how traditional financial risk factor models can potentially explain the risk of the largest crypto asset, Bitcoin. 9Īs institutional investors evaluate crypto assets, how can they think about properly assessing their risks, especially in the context of a broader, multi-asset class portfolio? In this Street View, we will seek to answer this question. In fact, PWC estimates that crypto hedge fund assets increased 100% from $1 to $2 billion in 2019, and it seems like it’s only increasing from there. Increasingly, institutional investors are entering the crypto space, with managers like Skybridge, 6 Blackrock, 7 and Tudor 8 announcing the addition of crypto to their investment universes or even the launch of crypto-dedicated funds. The incredible price appreciation (leading to bandwagon and “fear of missing out” effects), massive government stimulus, and people around the world spending more time at home and in front of screens have certainly contributed to Bitcoin’s meteoric rise, especially among retail investors. 3 In fact, Bitcoin’s five-day average daily trading volume as of April 18th was approximately $77B, 4 roughly 70% of the NASDAQ’s five-day average volume. In January 2021, Cointelegraph reported that volume in the Bitcoin market doubled, smashing previous all-time records. 2 It has not been a smooth ride-that return has come with an annualized volatility of 81%, but the risk-adjusted returns were still very attractive (Bitcoin’s annualized Sharpe ratio was ~1.3 over this period).īitcoin trading volumes have increased meaningfully during the pandemic. Bitcoin has surged nearly 40,000% 1 (that is not a typo) since April of 2013, for an annualized return of approximately 110%. The chart below speaks volumes to the spectacular rise in cryptocurrency investing.
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