research report investigating the myth of zero …

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RESEARCH REPORT

Investigating the Myth of Zero Correlation Between Crypto Cur-

rencies and Market Indices

Commissioned by Iconic Funds

PREPARED BY

Robert Richter, CFA

Philipp Rosenbach

An Empirical Study

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DISCLAIMER

ICONIC FUNDS GMBH is the holding company of a series of subsidiaries that manage and issue crypto asset index in-vestment products. Collectively, ICONIC FUNDS GMBH and its subsidiaries are branded as “Iconic Funds.” Iconic Funds is a joint venture between Iconic Hold-ing GmbH and Cryptology Asset Group p.l.c., founded by Christian Angermayer and Mike Novogratz.

In no event will you hold ICONIC FUNDS GMBH, its subsidiaries or any affiliated party liable for any direct or indirect investment losses caused by any information in this report. This report is not investment advice or a recommendation or solicitation to buy any securities.

ICONIC FUNDS GMBH is not registered as an investment advisor in any jurisdiction. You agree to do your own research and due diligence before making any invest-ment decision with respect to securities or investment opportunities discussed herein.

Our articles and reports include for-ward-looking statements, estimates, pro-jections, and opinions which may prove to be substantially inaccurate and are inherently subject to significant risks and uncertainties beyond ICONIC FUNDS GMBH’s control. Our articles and reports express our opinions, which we have based upon generally available informa-tion, field research, inferences and deduc-tions through our due diligence and ana-lytical process.

ICONIC FUNDS GMBH believes all information contained herein is accurate and reliable and has been obtained from public sources we believe to be accurate and reliable. However, such information is presented “as is,” without warranty of any kind.

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Since the rise of Bitcoin, crypto currencies have been assumed to be uncorrelated with other asset classes. During an economic downturn triggered by COVID-19 in March, however, the price of crypto currencies plunged alongside most other assets in an event since-dubbed “Black Thursday.” Since, market participants have started acknowl-edging non-zero correlations between crypto cur-rencies and other assets during liquidity crises. This report challenges the theory of zero correlations and stipulates that crypto currencies are not only correlated with markets during liquidity shortages, but generally have a minor correlation with the ma-jority of market movements.

The hypothesis is that crypto currencies are, indeed, correlated with financial markets and possess be-tas within the range of 1. In order to evaluate this, several different pieces of empirical analysis are conducted. Firstly, correlations amongst the cryp-to currencies themselves are analysed to establish whether crypto currencies behave as one asset class or diverge amongst one another. Secondly, the correlations between these crypto currencies and market indices are evaluated. This analysis aims to provide empirical evidence as to whether crypto currencies are correlated with traditional markets.

The key component of this analysis is that a liquid market is considered as part of it. So far, analysts have been quick to look at the entire data history of crypto currencies and conclude that there is no statistically significant relationship between crypto and financial market performance. When adjust-ing for differences in liquidity, however, this story changes significantly. The report analyses this issue.

Furthermore, this report reviews how the correla-tions changed during the most recent March 2020 liquidity crisis, triggered by the outbreak of COV-ID-19. It will be shown that, along with other asset classes, the correlations of crypto currencies in-creased significantly.

Market betas are analysed in the conclusion sec-tion and, contrary to popular belief, show that crypto currencies move more closely in line with financial markets than previously thought.

In order to tackle this question, ten of the largest crypto currencies1 were analysed in detail.

Before presenting the results of the analysis, the following sections provide an overview of the data used for the analysis and the technical review methodology.

1 Based on market capitalisation as of 31st December 2019.

Introduction

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DataThe research presented in this report requires two types of data, namely crypto currency data and financial market data. This section provides an overview of how the data was sourced and prepared for the ensu-ing analysis.

Data sources

Traditional market data was sourced from Bloomb-erg and covered a time period from 1.1.2009 to 31.3.2020, on a daily basis. All market indices were sourced in US Dollars to ensure better compara-bility. Table 1 provides an overview of the differ-ent indices used and their reference ticker symbol.2 Furthermore, Table 1 provides details of the assets contained within each index and the rationale as to why they were included in this analysis.

Crypto currency data was sourced from https://coinmarketcap.com. The data was obtained since the inception of each individual currency until 31st March 2020. The currency prices and market cap-italisations were sourced on a daily basis and are denominated in US Dollars. Please note, that for the purposes of this analysis, the day’s closing price was used.

Since the universe of crypto currencies has in-creased to over 2,000 at the time of this writing, it was decided to focus on 10 of the largest crypto currencies, measured by market capitalisation. As a result, the following crypto currencies are within the scope of this analysis: Bitcoin (BTC), Ethereum, XRP, Tether, Litecoin, EOS, BinanceCoin, Tezos, Chain-link and UNUS SED LEO.

2 For each index the day’s closing price was used (PX LAST)

The characteristics and value drivers of these coins diverge significantly from one another, which im-pacts correlations and market betas. Table 2 out-lines the key characteristics of each crypto currency.

Data Preparation

As shown in Table 2, a significant number of cryp-to currencies have only been in existence for a few years, which means that the choice of data frequency had to be economical. Daily data would maximise the data points available, but is rather noisy for such an analysis. Monthly data is less noisy in comparison but reduces the number of available data points drasti-cally. In order to strike the right balance between data availability and noise reduction, the analysis was con-ducted based on weekly data.

The weekly returns of the market indices and crypto currencies were calculated from the previous week’s Friday to the following week’s Friday.

ICONIC FUNDS: Investigating the Myth of Zero Correlation Between Crypto Currencies and Market Indices

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Index Ticker Overview

MSCI World incl. Emerging Markets MXWD

This index was chosen to represent the performance of the full opportunity set of large- and mid-cap stocks across 23 developed and 26 emerging markets. It aims to reflect the overall economic condition of the existing equity markets. As of December 2019, it covers more than 3,000 constituents across 11 sectors and approximately 85% of the free float-adjusted market capitali-zation in each market.

MSCI World excl. Emerging Market MXWO

The MSCI World index represents the equity markets of 23 developed countries. It was included into this report to provide a relevant overview of the economic conditions in the developed and therefore more stable equity markets worldwide.The index is a market cap weighted stock market index of 1,644 stocks from companies throughout the world.

iShares Global Govt. Bond Index IGLO LN

This index was chosen to provide a relevant allocation of governmental bonds and therefore a fixed income asset class. The funds consists of over 99% governmental bonds and the remaining percentages as cash. The largest position are US-Bonds, with 39. 81% allocated assets, next are Japan with 18.45%, France with 7.94%, Italy with 7.18%, UK with 5.18% and Germa-ny with 5.05%. Other bonds include Belgium, Spain, Canada and Australia.

Commodities BCOM

This index was chosen in order to provide relevant information about the commodity market. The index is calculated on an excess return basis and reflects commodity futures price movements. The index rebalances annually, weighted 2/3 by trading volume and 1/3 by world production and weight-caps are applied at the commodity, sector and group level for diversification.

Real Estate MXWO0RE

The MSCI World Real Estate index was chosen to reflect the real estate market. It is a free float-adjusted market capitalization index that consists of large- and mid-cap equity across several developed countries. The compa-nies in the index are mainly Real Estate Investment Trust (RETI) companies, supplemented by RE operating companies. Geographically the funds invests in: US with 64% assets allocated, Japan with 10.27% , Hong Kong with 8.02%, Australia with 5.12%, Germany with 3.86% and other countries with 8.73%.

Private Equity PSPIV

The index includes securities, ADRs and GDRs of 40 to 75 private equity com-panies, including business development companies (BDCs), master limited partnerships (MLPs) and other vehicles whose principal business is to invest in, lend capital to or provide services to privately held companies (collectively, listed private equity companies) The fund and the index are rebalanced and reconstituted quarterly. Country-wise the funds allocate to: US 43.01%, UK with 13.81%, Switzerland with 7.68%, France 5.37%, Sweden 5.30%, Germa-ny with 3.82% and others with 12.44%.

Hedge Funds HFRI5FWC

The HFRI 500 Fund Weighted Composite Index is a global, equal-weight-ed index of the largest hedge funds that report to the HFR Database which are open to new investments and offer at least quarterly liquidity. The index constituents are classified into Equity Hedge, Event Driven, Macro or Relative Value strateries. The index is rebalanced on a quarterlv basis.

Infrastructure IGF US Equity

This index was chosen to provide relevant information and allocation towards the infrastructure sector. The fund has major exposure towards companies providing utilities (52.21%), transportation (32.85%) and energy (14.53%) companies. Geographically the fund is invested in: US with 44.68%, Canada with 9.40%, Spain and Australia with 8.40% each, Italy with 6.85%, China with 5.31%, France with 5.24% and others with 9.31%.

Timber & Forestry WOOD US Equity

The fund was chosen to primarily to mirror the endowment fund‘s allocation to the alternative asset class timber and forestry. The fund is mainly engaged in companies from following sectors: Paper & Forest Production (56.89%), Equi-ty Real Estate Investment Trusts (22.26%), Containers & Packaging (16.44%) and Household Durables (3.86%). Geographically the fund is exposed into: US with 33.70%, Japan with 15.63%, Sweden with 14.40%, Finland with 10.69%, Brazil with 8.44%,Canada with 6.47% and others with 10.10%.

Table 1: Bloomberg Tickers


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Methodology

This report uses two different sta-tistical methods to investigate how crypto currencies behave in relation to other asset classes. Firstly, corre-lation coefficients are calculated to assess how crypto currencies behave amongst each other. This part of the analysis will shed some light on the question whether crypto currencies can be considered a coherent bas-ket, and therefore, one single asset class, or if they are distinguishable

from one another. Secondly, beta analysis is conducted to assess how correlated crypto currencies are compared to traditional market indi-ces. Each of these methodologies is outlined below.

The correlations presented in this re-port are Pearson correlations. Pear-son correlation coefficients are cal-culated as per the equation below:

Pearson correlation coefficients measure the linear correlation be-tween two variables. It was chosen over the Spearman correlation since Spearman correlation coefficients are more suitable for ordinal varia-bles rather than continuous data such as market returns (Simon & Blume, 2010).

The market betas are calculated in line with standard portfolio manage-ment theory as per the equation below:

The beta of an asset describes how responsive the asset return is to changes in overall market conditions. For example, a beta of 2 implies that the return of the asset would be ex-pected to increase by 2% if the gen-eral market is up by 1% over the same period (Kaplan University, 2013).


Covariance (x,y)Pearson correlation(x,y) = σx σy

Covariance (x,Market)Beta (x,Market) = σ²Market

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Crypto Currency Overview

Bitcoin

Bitcoin was the very first of its kind. Launched on 31st October 2008, it was the first blockchain based crypto currency that solved the double spending problem. Bitcoin’s consensus mechanism is based on the proof of work and the supply of Bitcoins are limited. Currently, Bitcoin is trying to establish itself as “digital gold”, i. e. a safe haven during times of crisis. Bitcoin price data is available from 29th April 2013.

Ethereum

Ether is the crypto currency on the Ethereum platform. The Ethereum platform is blockchain based and not only allows trading the crypto currency but enables its users to write smart contracts and therefore provides significantly more functionality than Bitcoin. The Ethereum platform also enables its users to create tokens which can be used to tokenise any real world asset.

Ether Price data is available from 7th August 2015.

XRP

XRP is a crypto currency traded on the platform RippleNet. In contrast to Bitcoin and Ethereum, this platform is not blockchain based. Instead, it is a distributed ledger. It was created to provide a faster and more scalable alternative to the existing blockchain based solutions. XRP price data is available from 4th August 2013.

Tether

Tether is a crypto currency aiming to mirror the value of the USD, i.e. 1 Tether should be worth ap-prox. 1 USD. Tether is therefore considered a stablecoin. Note that by definition a low correlation with the market is expected. Even when the price of other crypto currencies moves, the value of Tether is expected to be stable. Tether price data is available from 25th February 2015.

Litecoin

Litecoin was created as a faster alternative to Bitcoin. It was initially based on the Bitcoin protocol

but uses a different hashing algorithm and consequently has a different transaction speed.

Litecoin price data is available from 29th April 2013.

EOS

EOS is the crypto currency associated with the platform EOSIO, which gives its users the ability to

write smart contracts and deploy industrial-scale DApps.

EOS price data is available from 1st July 2017.

BinanceCoin

The BinanceCoin was initially set-up as an Ethereum ERC-20 token, but has migrated onto the

Binance mainnet since then. It acts as a payment and utility token and can be used on the Binance

DEX, which is a decentralised exchange for crypto currencies.

BinanceCoin price data is available from 25th July 2017.

Tezos

Tezos is a multi-purpose platform that supports the use of smart contracts as well as DApps. Further-

more it attempts to solve the issue of on-chain governance.

Tezos price data is available from 2nd October 2017.

Chainlink

Chainlink is an oracle based network attempting to combine smart contracts with real world data.

In order to ensure the delivery of accurate data, providers of accurate data are provided with

tokens whereas delivery of poor data is punished via the deduction of tokens.

Chainlink price data is available from 20th September 2017.

UNUS SED LEO

This crypto currency has received relatively little attention since its inception in May 2019. Akin to

the BinanceCoin its purpose is to act as a means of transacting on crypto currency exchanges.

UNUS price data is available from 21st May 2019.

Table 2: Crypto Currency Overview


Source: https://coinmarketcap.com/

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Bitcoin Ether-eum XRP Tether Litecoin EOS Binance-

Coin Tezos Chain-link

Ethereum 33% ***

XRP 33% *** 32% ***

Tether 4% -3% 3%

Litecoin 63% *** 38% *** 62% *** 1%

EOS 61% *** 60% *** 50% *** 9% 59% ***

Binance-Coin

33% *** 29% *** 15% * 10% 18% ** 13%

Tezos 45% *** 50% *** 27% *** 0% 40% *** 36% *** 44% ***

Chainlink 48% *** 61% *** 42% *** 2% 41% *** 27% *** 57% *** 35% ***

UNUS SED LEO

17% 37% ** 40% *** 8% 41% *** 41% *** 23% 16% 18%

Results

Having disclosed the data and meth-odology, this section discusses the re-sults of the analysis. Firstly, the corre-lation between the crypto currencies is discussed, followed by a presenta-tion of the crypto currencies’ correla-tions with the market and their betas.

Furthermore, it will be shown how correlations change during liquidity crises. For this case study, the cor-relations are calculated only for the time period 1st January 2020 – 31st March 2020, which approximately reflects the time when markets were initially adjusting in lieu of the

COVID-19 outbreak.

Correlation between crypto currencies

The results of the correlation analysis between crypto currencies is present-ed in Table 3. The table shows the Pearson correlations in percentage points. Note that statistical signifi-cance is represented by asterisks, as per the legend.

Three general observations emerge from the results in Table 3. Unsur-prisingly, Tether appears to have

low correlations with all other crypto currencies. Based on the information presented in Table 2, this result is to be expected. Since Tether is consid-ered a stablecoin, which means that its value should not deviate signifi-cantly from 1 USD, it is expected that the price of Tether does not move as freely compared to other crypto currencies.

The second observation is that LEO appears to have lower correlations to other crypto currencies than the re-maining coins. This may be driven by the facts that LEO has different value

Table 3: Correlation Results between Cyrpto Currencies

* Significant at the 10% level

** Significant at the 5% level

*** Significant at the 1% level


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drivers than the other coins and that it is not trying to become a worldwide meth-od of payment. Additionally, the sale of LEO was initially done privately, which limited its public exposure and liquidity (Coin Kurier, 2019).

Apart from the exceptions Tether and LEO, the results show that the degree of correlation is medium to high amongst the other crypto currencies, and with very few exceptions, they are all highly statistically significant.

This shows that leading crypto currencies may be considered as a coherent bas-ket, unless their structure and value driv-ers differ significantly, as is the case with stable coins and others. It follows from this finding that one would expect similar responses from these coins to changes in the market. Since we know that the crypto currencies move in relatively the same direction, in most cases, it would be expected that they respond similarly to changes in the financial markets. This is discussed in the following section.


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Correlation with traditional market indices

As mentioned in the introduction, the general public assumption is that crypto currencies are uncorrelated with traditional market indices. This section will analyse this assumption in detail and determine whether it is valid. As a starting point, the Pearson correlations were calculated be-tween the returns of the crypto cur-rencies and the market indices over the entire period available. The re-sults of this analysis are presented in Table 4.

Those results do indeed show limited correlation between crypto curren-cies and financial markets. Bitcoin, Ethereum and Chainlink are the only currencies that exhibit some statisti-cally significant correlation with the major indices. Whilst this seemingly confirms the hypothesis that crypto currencies are uncorrelated with the market, these results are misleading.

The reason is liquidity.

When crypto currencies are first launched, their secondary market li-quidity is negligible. This even applies to the first few years of Bitcoin. During these infant stages of a crypto curren-cy, very few people trade it. By defi-nition, correlations with other market indices are expected to be close to zero because there aren’t enough market participants to influence prop-er price discovery. Rather than being influenced by systemic market events, prices are driven by random, and of-ten illogical, behaviour.

The influence of liquidity should be accounted for before drawing the conclusion that crypto currencies are uncorrelated with the market. The dataset was filtered for observa-tions where liquidity had already im-proved. Since there is no clinical term for what defines a “liquid crypto cur-rency market”, two scenarios were investigated. In the first scenario, it

was analysed when the daily trading volume of each crypto currency first hit 100,000,000 USD. All observa-tions prior to that date were excluded from the sample. In the second sce-nario, this threshold was increased to 500,000,000 USD. Whilst these numbers are negligible in the context of developed financial markets, it is a sizeable volume in the relatively new crypto currency market. The results of this analysis are presented in Table 5 and Table 6.

Firstly, Tether once again does not correlate well with other market in-dices. Based on the results from the previous section, this finding is in line with expectations. Since Tether is a stablecoin, which does not exhibit drastic price movements, it would not be expected to correlate with market indices.

When comparing the results of Table 4, Table 5 and Table 6, one general trend emerges. As shown, the corre-

MXWO MXWD IGLO LN FXNAX BCOM MXWO0RE PSPIV IGF US WOOD US

Bitcoin 10% * 9% * 4% 6% 7% 0% 9% * 8% 3%

Ethereum 14% ** 14% ** 10% 10% 14% ** 8% 14% ** 15% ** 10%

XRP 7% 7% 7% 8% 3% 6% 7% 7% 5%

Tether 0% -1% 3% 2% 4% 3% -2% 4% -1%

Litecoin 5% 5% 1% 2% 2% -1% 6% 3% 5%

EOS 12% 12% 6% 9% 15% * 7% 12% 11% 8%

BinanceCoin 3% 4% 2% 3% 7% 3% 3% 7% 3%

Tezos 13% 14% 0% 9% 10% 10% 15% * 13% 6%

Chainlink 21% ** 21% ** 3% 4% 20% ** 10% 21% ** 19% ** 20% **

UNUS SED LEO

0% 0% 2% 14% -10% 1% -2% -1% 4%

Table 4: Correlation Crypto Currencies with Market Indices (entire history)





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lations increase as liquidity increases with statistical significance. For exam-ple, the correlation measured over the entire sample between Bitcoin and the MSCI World (excl. emerg-ing markets) is 10%, which is signifi-cant at the 10% level. The correlation between the same two variables in-creases to 11% significant at the 5% level when zooming in on a time when Bitcoin started trading with a volume of 100 million USD. Looking only at a time when Bitcoin started trading with a volume of 500 million USD, the correlation increases even further to 16% significant at the 5% level. This trend is equally applicable to the other crypto currencies and shows that they move in line with the traditional market to a certain extent.

The crypto currencies are not corre-lated with all market indices, howev-er. The correlations with large equity indices, such as the MSCI World in-dices and the commodity index, are still low but statistically significant.

Meanwhile, the returns with the glob-al and US bond indices are not sig-nificant. This is to be expected, how-ever, since these traditional market indices barely correlate with bond indices, historically.

The correlations with the alterna-tive investment class indices are less clear-cut. Crypto currencies appear more correlated with private equity funds as well as infrastructure funds but do not correlate well with real es-tate and forestry.

Based on the results presented, it appears that crypto currencies are slightly correlated with the tradition-al financial market. Correlations are highest with equity indices, whereas bonds exhibit lower correlations to crypto currencies.

Correlation during the Q1 2020 liquidity crisis

During times of crisis, correlations

generally tend to increase across asset classes. This section analyses whether this phenomenon also ap-plied to crypto currencies during the onset of COVID-19 in Q1 2020. The results are presented in Table 7.As shown, the Pearson correlations increased across the board, sup-porting this hypothesis. Furthermore, statistical significance increased as well, evidencing that the higher cor-relations depicted are valid. Whilst the correlation coefficients for the bond indices are not significant, their point estimates increased drastically, which shows that the indices moved in the same direction.

Based on these findings, it is evident that the correlations between crypto currencies and other asset classes increased considerably during the most recent liquidity crisis.


Bitcoin 11% ** 11% * 7% 7% 9% 5% 13% ** 12% ** 4%

Ethereum 20% *** 21% *** 12% * 11% 14% * 14% ** 23% *** 22% *** 17% **

XRP 14% * 15% * 5% 5% 18% ** 10% 13% 13% * 11%

Tether 5% 4% 5% 5% -1% 5% 4% 8% 3%

Litecoin 16% * 16% * 8% 10% 8% 11% 14% * 16% * 12%

EOS 12% 12% 6% 9% 15% * 7% 12% 11% 8%

BinanceCoin 19% ** 20% ** 9% 11% 26% *** 12% 20% ** 20% ** 13%

Tezos 49% ** 51% ** 17% 26% 50% ** 41% * 49% ** 52% ** 51% **

Chainlink 26% * 27% * 12% 16% 27% * 23% 24% 31% ** 27% *

UNUS SED LEO

Hasn‘t reached trading volume of 100 million USD yet




Table 5: Correlation Crypto Currencies with Market Indices (100 million USD trading volume)


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Bitcoin 16% ** 16% ** 10% 14% * 16% ** 8% 15% ** 20% *** 7%

Ethereum 23% *** 23% *** 14% * 13% 22% *** 14% * 24% *** 24% *** 16% **

XRP 16% * 17% ** 6% 7% 19% ** 12% 14% * 14% * 12%

Tether 6% 6% 7% 8% 0% 5% 3% 9% 3%

Litecoin 16% * 16% * 8% 10% 8% 11% 13% 16% * 12%

EOS 17% * 17% * 6% 5% 22% ** 10% 19% ** 16% * 15%

BinanceCoin 20% ** 20% ** 9% 12% 23% ** 13% 21% ** 22% ** 12%

Tezos Hasn‘t reached trading volume of 500 million USD yet

Chainlink 33% ** 34% ** 16% 24% 28% * 31% * 33% ** 40% ** 32% **

UNUS SED LEO


Table 6: Correlation Crypto Currencies with Market Indices (500 million USD trading volume)






Bitcoin 50% * 51% * 27% 43% 51% * 32% 47% 58% ** 45%

Ethereum 62% ** 63% ** 24% 38% 65% ** 49% * 60% ** 66% ** 60% **

XRP 70% *** 71% *** 30% 44% 64% ** 56% ** 66% ** 71% *** 66% **

Tether 46% 44% 39% 36% 31% 48% * 40% 44% 47%

Litecoin 55% ** 56% ** 23% 34% 52% * 42% 54% * 60% ** 50% *

EOS 52% * 53% * 23% 34% 46% 39% 50% * 57% ** 47%

BinanceCoin 58% ** 59% ** 24% 38% 56% ** 42% 53% * 61% ** 53% *


Chainlink 26% * 27% * 12% 16% 27% * 23% 24% 31% ** 27% *

UNUS SED LEO





Table 7: Correlation Crypto Currencies with Market Indices during Q1 2020

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Bitcoin 0.78 0.79 1.39 2.58 1.10 0.34 0.58 0.86 0.28

Ethereum 1.43 1.45 2.49 3.06 2.00 0.76 1.17 1.30 0.80

XRP 1.55 1.63 1.69 2.52 2.65 1.01 1.04 1.15 0.93

Tether 0.01 0.01 0.05 0.07 0.00 0.01 0.01 0.02 0.01

Litecoin 1.17 1.19 1.60 2.95 0.89 0.68 0.76 1.02 0.68

EOS 1.13 1.21 1.28 1.34 2.30 0.60 0.99 0.96 0.79

BinanceCoin 1.03 1.06 1.40 2.50 1.84 0.56 0.84 1.00 0.47


Chainlink 1.40 1.47 1.83 3.69 1.98 1.04 1.04 1.32 1.10

UNUS SED LEO


Note: The betas that are greyed out are not statistically significant

Table 8 : Crypto Currency Betas with Market Indices (500 million USD trading volume)


Market Betas

Building on the analysis of correlations between crypto currencies and market indices raises the question what the market betas are for crypto currencies. Recall from the methodology section that the betas measure the ex-pected responsiveness of an asset relative to market movements. Since beta analysis is only meaningful for a liquid market, the analysis focusses on the sample where daily trading volumes have reached 500 million USD for the respective crypto currency. The results are pre-sented in Table 8.

As expected, the beta of Tether is close to zero, be-cause it is a stablecoin. The betas of the other crypto currencies are in the range of 0.8 – 2.7. The previous sections showed that the correlations with the MSCI Worlds, commodities, private equity and infrastructure indices were statistically significant. Therefore, the focus should be placed on the betas corresponding to those indices. The betas of Bitcoin appear to be slightly lower compared to the betas of Ethereum. For example, a 1% return of the MSCI World (excl. emerging markets) is likely to lead to a 0,79% return of Bitcoin, but a 1.43% return of Ethereum.

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Conclusion


The previous sections presented anal-ysis of the correlations of crypto cur-rencies amongst each other as well as correlations and betas of crypto currencies with traditional market indices.

It was found that the correlations within the crypto currency basket are high unless the coins are structurally different from the others, such as Tether and LEO.

More importantly, the analysis of correlations with regards to the tradi-tional market showed that the general public assumption of zero correlation between crypto currencies and the fi-nancial markets is not true. Whilst the overall correlations were found to be statistically insignificant, the under-lying reason was not that the assets are truly uncorrelated, but that market

activity and liquidity was so low in the early years of crypto currencies that there could not have been any meaningful correlation with the rest of the market due to a lack of price discovery.

When adjusting for crypto curren-cy market liquidity, it was found that crypto currencies are, indeed, slightly correlated with the traditional market. Furthermore, it was found that like most other asset classes these cor-relations increase during a liquidity crisis event. Market betas were found to be in the range of 0.8 – 2.7, de-pending on the crypto currency. In any event, this analysis disproves the assumption that crypto currencies are uncorrelated with financial markets and shows that they are more intri-cately linked than is generally believed.

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References

Coin Kurier, 2019. UNUS SED LEO: Warum dieser Token aus dem Nichts in die Top 15 stieg!. [Online] Available at: https://www.coinkuri-er.de/unus-sed-leo/[Accessed 10 06 2020].

CoinMarketCap, 2020. Top 100 Cryptocurrencies by Market Capital-ization. [Online] Available at: https://coinmarketcap.com/

Kaplan University, 2013. Schwes-er Notes 2014 CFA Level 1 Book 4: Corporate Finance, Portfolio Man-agement, and Equity Investments. United States of America: Kaplan, Inc..

Simon, C. & Blume, L., 2010. Mathematics for Economists, Interna-tional Student Edition. s.l.:Norton.

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