2019 Q3 Crypto-Correlations Review

In the financial industry, correlations have been widely used by analysts and fund managers to diversify and efficiently allocate their assets across sectors and industries. Despite the nascency of the cryptoasset industry, these metrics have gradually become more scrutinized and analyzed by all crypto-stakeholders, institutional and retail.

This report will analyze correlations across all largest cryptoassets over the first six months of 2019.

1. Market performance of Q3 2019

The third quarter of 2019 exhibited adverse price movements across all large marketcap cryptoassets, breaking the two consecutive quarterly price gains in Q1 and Q2. Bitcoin price dropped by nearly -30%, closing on September 30th at around $8,000.

Meanwhile, large marketcap altcoins displayed performances ranging from -38% to -60%, as illustrated in table 1 (see below).

Table 1 - Comparison of quarterly price changes

Owing to the price of Bitcoin appreciating against all other ninth-largest cryptoassets, Bitcoin market dominance increased (see chart 1), passing above the 70% mark for a few days.

Chart 1 - Bitcoin market dominance in 2019

As illustrated in chart 1, Bitcoin market dominance kept increasing over the first three quarters of 2019, moving from 50% in early January to new highs around 70% in September (crossing the 70% mark briefly on September 3rd).

This market dominance is put in perspective against Bitcoin trading dominance, which is the second focal point of this section’s analysis. For this report, it is defined as:

“Bitcoin trading dominance represents the respective volume contribution from Bitcoin trading, with BTC as a base currency, relative to the total spot volume on a platform (e.g., Binance).”

Chart 2 - Bitcoin trading dominance in 2019 on Binance.com

This ratio started at around 20% on January 1st 2019, and fell below 10% on March 26th 2019. Subsequently only 10% of the trading volume was against BTC (as a base asset). Since then, this ratio kept increasing, reaching new highs above 45-50%.

However, as chart 3 (see below) indicates, the Bitcoin trading dominance increased at a faster pace than the increase in Bitcoin marketcap dominance, which could indicate an overall disinterest from market participants to altcoins.

Chart 3 - Bitcoin trading dominance divided by Bitcoin marketcap dominance

Further research must be conducted to determine whether (1) altcoin volumes shriveled in absolute numbers or (2) whether the increase in Bitcoin trading dominance can be explained by other factors such as new traders focusing mostly on BTC trading.

2. Correlation analysis of large-cap assets

In this section, a correlation analysis is conducted over a set of large-cap cryptoassets.

2.1 A look at correlations over the third quarter of 2019

“Correlation statistically measures the strength of a linear relationship between two relative movements of two variables and ranges from -1 to +1.”

In general, assets with a correlation above 0.5 or below -0.5 are considered to have strong positive/negative associations. Similarly, a close-to-zero correlation indicates a lack of linear relationship between two variables, and for this analysis, the returns of two assets.

If the returns of two assets do exhibit a positive correlation, it implies that these two assets tend to move in the same direction, and therefore share similar risks. On the other hand, a negative correlation between the returns of two assets indicates that the two assets move in opposite directions, and it is thus possible to use one asset as a hedge against the other.

In this report, 30 of the largest cryptoassets (excluding stablecoins) were selected based on their average market capitalization over the third quarter of 2019.

Chart 4 - Correlations of USD daily returns of thirty large-cap cryptoassets between June 30th and September 30th 2019

Based on the above chart, correlations between all pairs are always positive and typically more significant amongst the largest cryptoassets. For instance, Ethereum (ETH) and Bitcoin (BTC) displayed a correlation coefficient of 0.81 over the third quarter of 2019.

In a similar fashion to our previous reports, several factors influence the strengths of these relationships, such as:

• Proof of Work assets exhibited a larger correlation with each other. For instance, Bitcoin (BTC), Litecoin (LTC), Ethereum (ETH), Bitcoin Cash (BCH), and Bitcoin SV (BSV) displayed an extremely high median (above 0.75). However, Ethereum is presumably transitioning to PoS -in the near future-, and it remains to be seen whether it would impact the strength of its correlation with Bitcoin.
• Privacy effect: Dash (DASH), ZCoin (ZEC), and Monero (XMR) displayed higher correlations than with other cryptoassets.
• Programmable blockchains (e.g., NEO, Ethereum, EOS) exhibited, on average, higher correlations with each other than with non-programmable assets.
• “Binance Effect”: assets listed on Binance displayed higher correlations than with the cryptoassets not listed on Binance. LEO, Crypto.com (CRO), and Huobi Token (HT) were less correlated on average with other cryptoassets.

Specifically, additional asset-specific factors may have existed with LEO and Crypto.com (CRO) being almost uncorrelated to all other cryptoassets.

Ultimately, a few correlations are worth mentioning explicitly:

• Even though Ethereum (ETH) displayed the most substantial correlations with most of the investigated cryptoassets , ETH exhibited a relatively low correlation with Ethereum Classic (ETC), with a correlation coefficient of 0.69.
• HT (Huobi Token) has its highest correlation coefficient (0.69) with BNB.
• XRP remains highly correlated with Stellar (0.80), in line with findings from our past report about cluster analysis. While Stellar was initially built on the Ripple protocol, its code was forked and quickly revised.
• Among all cryptoassets investigated, most of them displayed their highest correlation coefficients with Ethereum. Only Decred (DRC) exhibited its highest correlation coefficient with Bitcoin (0.82).

In the next subsection, these results are confronted with the second quarter of 2019.

2.2. Comparison with the previous quarter

Chart 5 - Correlations of USD daily returns of thirty large-cap cryptoassets between March 31st and June 30th 2019

In Q3 2019, Ethereum (ETH) and Bitcoin (BTC) displayed a strong correlation of 0.81, which is the same as in Q2 2019. The average correlation between these 29 cryptoassets¹ increased from 0.54 in Q2 2019 to 0.64 in Q3 2019.

Chart 6 - Changes in correlations between USD daily returns of 29 large-cap cryptoassets between Q3 and Q2 2019

Compared with the previous two quarters of 2019², BNB got much more correlated in Q3 2019 with other large-cap assets. Similarly, Cosmos (ATOM) and ChainLink (“LINK”) displayed a sizeable positive increase in their correlation with most of the large cryptoassets.

Conversely, Stellar (XLM) displayed the largest decrease in its median correlation with other cryptoassets, possibly owing to specific idiosyncratic factors (e.g., “Keybase Stellar Space Drop”³)

Even though the correlation of Dogecoin (DOGE) with others remain quite small despite its Binance listing in early July 2019⁴, it increased in comparison to the past quarter of 2019.

While this could be indicative of a potential “Binance Effect”, the delisting of Bitcoin SV on April 15th 2019⁵ has not significantly reduced its correlation with other assets.

3. Conclusion

Over the third quarter of 2019, the average correlation between Bitcoin and most other large cryptoassets remained in line with the previous quarter. However, the average correlation among large cryptoassets increased in Q3 2019 with a significant positive increase in the correlations of BNB, ChainLink, and Bitcoin SV with other cryptoassets. In line with our previous two reports, idiosyncratic factors such as Proof-of-Work consensus or a potential “Binance Effect” remain some of the critical factors impacting the strength of correlations among cryptoassets. Additionally, Ethereum became one of the benchmarks of the crypto-market in the third quarter of 2019, displaying the highest median correlation with all other cryptoassets. Yet, past empirical results are not representative of the future of this industry. Hence, it remains to be seen whether some of these findings will repeat in the fourth quarter of 2019.