Julien Riposo
Robin Marshall
Alex Nae
- Digital assets are rapidly developing into an important asset class for investors, in addition to their roles on the blockchain.
- But investors need to be aware of the dynamic and evolving correlations with traditional asset classes, particularly for portfolio diversification purposes.
- Our empirical work, with FTSE Russell multi-asset data, suggests DAs share more characteristics with risk-on assets, than safe havens like US Treasuries, or gold.
- A new asset class? DAs do not fit easily into traditional asset classifications, suggesting the emergence of a unique, new asset class.
Digital assets are rapidly developing into an important asset class for investors, in addition to their roles on the blockchain. In our longer research paper, we delve deeper into the intricate relationships between DAs, primarily Bitcoin and Ethereum, and established asset classes, and conduct a comprehensive analysis of their correlation dynamics.
By using FTSE Russell index data since 2013/2014, this paper investigates the evolving nature of these relationships, their stability, behavioural patterns under various economic conditions, and whether DAs represent a unique asset class or share characteristics with existing ones. The analysis reveals a complex and dynamic set of correlations, underscoring both the growing interconnectedness between DAs and traditional assets, and the significant volatility inherent in the DA market.
Dynamic correlations shift between DAs and traditional assets
The most significant finding is the dynamic and evolving nature of correlation coefficients between DA returns and traditional asset classes. While certain consistent relationships emerge, the strength and direction of these correlations have shifted over time, as Chart 1 shows. A striking result is the marked increase in correlation between DA returns and risk-on assets (like equities) following the Covid-19 pandemic, and the subsequent surge in inflation and interest rates.
This increased correlation between DAs and equities has persisted since inflation normalised in 2023-2024 and may have been sustained by the spot ETF approvals for DAs in the US in early 2024. Correlations between US Treasuries and equities also increased but not until the Fed began raising rates in March 2022, remaining negative until Q2, 2022.
Chart 1: Correlations of DAs with Russell 1000 and US Treasuries
This suggests that DAs share characteristics with other risk-on assets, particularly during periods of heightened uncertainty, demonstrating significant sensitivity to broader market sentiment and risk appetite. The observed shift in correlations underscores the need for continuous monitoring, and reassessment, of the risk profiles of DAs as they become integrated into traditional investment strategies.
DAs show more risk-on asset characteristics than risk-off….
Another consistently observed characteristic of the analysed DAs is their high volatility and high beta, as Chart 2 shows. This pronounced volatility, especially when contrasted with more established asset classes like gold, highlights the sensitivity of DAs to macroeconomic and market shocks. It also helped drive the arrival of a new class of cryptocurrencies in stable coins, with lower volatility. For Bitcoin and Ethereum, high beta coefficients signal their responsiveness to changes in overall market conditions and sentiment. However, we also note that the 36-month rolling beta of Bitcoin is below the extreme levels of 2014-2015.
Chart 2: 36 month rolling betas of Bitcoin with Russell 1000, and tech stocks with Russell 1000.
…and show a higher risk profile than stable stores-of-value, like gold
This finding carries substantial implications for portfolio diversification strategies, suggesting DAs may not yet provide the equivalent level of downside protection as other, more established, risk-off assets. Heightened volatility also demands careful consideration of risk tolerance and a robust portfolio diversification strategy.
But despite the higher risk profile associated with DAs, within a broader portfolio context, and the low correlation to date with more stable stores of value like gold, it is very early days in the development of the asset class, which may yet acquire more stable store of value characteristics as it matures.
But some caution is required, given the limitations of short time series
We acknowledge the statistical limitations of using relatively short time series data for DAs, a constraint that affects the reliability of correlation estimates. The short history of many DAs limits the capacity to capture the full spectrum of market behaviours and potentially leads to an incomplete picture of the relationship between DAs and established assets.
Correlations may also change as blockchain adoption rates increase, and as investors become more comfortable with investment use cases and notions of fair, or equilibrium, value. This may contribute to lower volatility in DAs over time, as the asset class matures.
These factors underscore the need for future research, using longer time series data, to establish a more complete picture of the complex interplay between DAs and traditional markets. Furthermore, the influence of short-term market fluctuations on the correlation estimates should be carefully considered. Longer time horizons may offer a more stable basis for analysing correlations and reducing the distortion caused by short term market noise.
A novel asset class that challenges traditional classifications
Based on the correlation analysis, it appears DAs don't conform easily to traditional classification of either commodities or currencies. (The strongest correlations we found were with equities, even if they were variable). While exhibiting characteristics of both, as hybrid assets, DAs possess unique attributes distinguishing them from established assets. This supports the conclusion that DAs might represent a novel asset class with distinct market behaviours and dynamics. We also find little evidence of a strong correlation in performance returns between DAs and gold.
But further research is needed to fully characterize this emerging class and develop a robust taxonomy that accurately reflects its unique properties. The development of a clear and universal DA parlance is also important for facilitating investor understanding and optimising portfolio management strategies.
Risk tolerance key, given DAs higher vol. versus traditional assets
The paper highlights the inherent risk-reward trade-off associated with investing in DAs. Evidence of higher returns must be balanced against higher volatility versus traditional assets. Skewness in DA returns is another complexity. These factors highlight the need for investors to assess their risk tolerance and their portfolio diversification strategies before allocating capital to DAs. The unique characteristics of DAs, including their high volatility and sensitivity to market sentiment, necessitate a well-defined risk management approach and careful integration into a diversified portfolio.
In summary, early evidence suggests DAs are high beta, risk-on assets
Using FTSE Russell multi-asset data, the paper provides insights into the correlation dynamics between DAs and traditional assets, and the emerging characteristics of the asset class. Our correlation results suggest DAs have high beta and more risk-on characteristics than risk-off and cannot be defined simply as an asset class. The paper also underscores the need for further research to fully capture DAs’ complexity.
Longer datasets should improve the robustness of correlation estimates, and may also help investors establish fair, or equilibrium value metrics for DAs. In addition, by extending the analysis to other DAs, further evidence should emerge on the relationship between investment use cases and DAs role on the blockchain. Refining the classification of DAs may assist more effective portfolio management.
We hope addressing these research needs will lead to a more comprehensive understanding of DAs and their integration into the global financial system, and the use of DAs in investment strategies.
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