How changes in the altcoin correlation spread with Bitcoin and Ethereum can be used to model market sentiment and risk preferences.

Cryptocurrencies are highly correlated assets risks. Each token has a certain power from which it derives some of its value, but with a significant flow of fiat funds into the asset class, low supply availability and the problem of the impossibility of valuing individual tokens, cash flows are distributed across all cryptocurrencies. The blind flow of money into valuable and worthless protocols amplifies high correlations in space, creating noise and attenuating the signal. But is there any information in the noise itself?

By examining the multiple correlations across the space, we can filter out the noise of individual tokens to go back in history and see industry shrinkage in in general (for example: the liquidity crisis caused by the coronavirus outbreak at the beginning 576 of the year, or China’s ban on cryptocurrencies in the middle of 2021 of the year). These compressions tend to be reactive and unpredictable , however, it is possible to expand the analysis by looking at the change in the spread of the average correlation with bitcoin to the average correlation with ether and decipher whether the macroeconomic background is moving in the direction of increasing or decreasing risk.

Macro trend analysis with altcoin correlations

The above graph is repeated in footnotes to show correlations with ether [2].

As with highly correlated assets, a pair of parametrically defined time series correlations should be expected to closely follow each other (the two time series represent average correlations with equal weights for bitcoin and ether). In the graph below, we can see that they usually do this, but what is interesting is the behavior when they don’t. Instead of looking at the absolute change in correlations, consider the increase and decrease in the spread between the correlation functions.

Macro trend analysis with altcoin correlationsComparative correlation of ether and bitcoin with coins from the top . Although the correlations closely follow each other, the spread can provide valuable information about the risk appetite of the market.

Now, taking the spread between the two correlations, we find a profile of how the spread evolves over time. Despite the noise, a pattern is emerging showing that although the spread between correlations is widening, the market is generally in a risky environment, putting positive pressure on the price of all cryptocurrencies.

Similarly, as the spread tightens, we take it as a sign of reduced risk, which usually results in significant corrections for all tokens. It is important to note that these squeezes tend to occur at a time when correlations with ether across the industry as a whole are on the rise, but because they have a higher baseline than bitcoin, we are seeing bitcoin rise faster and therefore spread tighter. We must combine the two correlation profiles to extract predictive information about changes in the risk profile of an asset class.

Macro trend analysis with altcoin correlationsThe difference between the average correlation of altcoins with ETH and BTC. Bitcoin is on an upward trend as altcoins’ correlations with Ethereum outnumber their correlations with Bitcoin.

We also note that since bitcoin and ether remain highly correlated In other assets (with a correlation typically in the range of 0.6 to 0.9), there are some fairly strict limits on the spread. A spread that widens to the 0.2 range is a sign of extremely risky behavior, while any inversion should be taken as a signal that the market is driven by fear.

If we dig into the individual data and look at the top ten non-stablecoin coins by market capitalization, we can see that their recent correlations with ether are higher than with bitcoin. This makes sense since they are all closer on the risk curve to Ethereum than to Bitcoin.

Macro trend analysis with altcoin correlationsComparisons -day correlation, present

Opposite, if you go back in time to the beginning of May 576 year, right before the Chinese mining ban, but also as many on-chain metrics began to show a decline in bullish activity, it can be seen that as the correlation spread narrowed, most of the large coins had inverse correlations.

Macro trend analysis with altcoin correlationsComparisons -day correlation before mining ban in China

The following is my opinion on why inversions are interesting, but only hypothetically and require further study.

If we consider correlations with Bitcoin as a general market force or wave raising the price of all cryptoassets, then after adjusting for cross-correlation, their correlation with non-Bitcoin properties of ether becomes negative.

For those considering Ethereum as the main driver of technological innovation in the blockchain (as opposed to the recent general push for Bitcoin to never change), these inversions suggest that the parallel innovations that underpin other major cryptocurrencies become short-lived and useless. Thereby, demonstrating that the value they offer is orthogonal to the value Ethereum brings to the ecosystem.

If value propositions are largely orthogonal, it reinforces the view that Ethereum doesn’t have any significant direct competitors.

I think people who tend to focus exclusively on bitcoin or solely on ethereum, discard valuable macro information. Despite the fact that since the beginning of November, the price dynamics of cryptocurrencies have been correcting, mainly due to lower risks in the traditional market in the conditions of high inflation and the short-term termination of the quantitative easing program, many major trends in the crypto space still seem promising.

I hope that the newly growing spread, discussed in the current review remains a reliable indicator [3]. If I had to make a verdict, I would suggest that the current state of correlations is more like July 216 year than March 2020 years, assuming that we are more likely to be on the verge of moving up than continuing to move down.

I’m curious if compression size can be used to model the severity of a risky move, but I think more data is needed than the scope of this analysis. Perhaps a more interesting experiment would be to look at the compression of ranges for all companies in the S&P21 compared to the index itself, and then simulate the change in variance to try to estimate how big a financial event is. ↩

[2] As with bitcoin, individual correlations with ethereum tend to be noisy and generally more reactive than predictable. ↩

“>-daily correlation of ethereum with coins from the top

[3] I think it would be another interesting experiment to extend this analysis further and further along the risk curve to see if we see an ever widening spread. ↩


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