Investigating the Myth of Zero Correlation Between Crypto Cur-rencies and Market Indices - An Empirical Study
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RESEARCH REPORT
Investigating the Myth of Zero
Correlation Between Crypto Cur-
rencies and Market Indices
An Empirical Study
PREPARED BY
Robert Richter, CFA
Philipp Rosenbach
Commissioned by Iconic Funds
1
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.
2Introduction
Since the rise of Bitcoin, crypto currencies have The key component of this analysis is that a liquid
been assumed to be uncorrelated with other asset market is considered as part of it. So far, analysts
classes. During an economic downturn triggered have been quick to look at the entire data history
by COVID-19 in March, however, the price of of crypto currencies and conclude that there is no
crypto currencies plunged alongside most other statistically significant relationship between crypto
assets in an event since-dubbed “Black Thursday.” and financial market performance. When adjust-
Since, market participants have started acknowl- ing for differences in liquidity, however, this story
edging non-zero correlations between crypto cur- changes significantly. The report analyses this issue.
rencies and other assets during liquidity crises. This
report challenges the theory of zero correlations Furthermore, this report reviews how the correla-
and stipulates that crypto currencies are not only tions changed during the most recent March 2020
correlated with markets during liquidity shortages, liquidity crisis, triggered by the outbreak of COV-
but generally have a minor correlation with the ma- ID-19. It will be shown that, along with other asset
jority of market movements. classes, the correlations of crypto currencies in-
creased significantly.
The hypothesis is that crypto currencies are, indeed,
correlated with financial markets and possess be- Market betas are analysed in the conclusion sec-
tas within the range of 1. In order to evaluate this, tion and, contrary to popular belief, show that
several different pieces of empirical analysis are crypto currencies move more closely in line with
conducted. Firstly, correlations amongst the cryp- financial markets than previously thought.
to currencies themselves are analysed to establish
whether crypto currencies behave as one asset In order to tackle this question, ten of the largest
class or diverge amongst one another. Secondly, crypto currencies1 were analysed in detail.
the correlations between these crypto currencies
and market indices are evaluated. This analysis Before presenting the results of the analysis, the
aims to provide empirical evidence as to whether following sections provide an overview of the
crypto currencies are correlated with traditional data used for the analysis and the technical review
markets. methodology.
1 Based on market capitalisation as of 31st December 2019.
3ICONIC FUNDS: Investigating the Myth of Zero Correlation Between Crypto Currencies and Market Indices
Data
The 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- The characteristics and value drivers of these coins
erg and covered a time period from 1.1.2009 to diverge significantly from one another, which im-
31.3.2020, on a daily basis. All market indices were pacts correlations and market betas. Table 2 out-
sourced in US Dollars to ensure better compara- lines the key characteristics of each crypto currency.
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 Data Preparation
why they were included in this analysis. As shown in Table 2, a significant number of cryp-
to currencies have only been in existence for a few
Crypto currency data was sourced from https:// years, which means that the choice of data frequency
coinmarketcap.com. The data was obtained since had to be economical. Daily data would maximise the
the inception of each individual currency until 31st data points available, but is rather noisy for such an
March 2020. The currency prices and market cap- analysis. Monthly data is less noisy in comparison but
italisations were sourced on a daily basis and are reduces the number of available data points drasti-
denominated in US Dollars. Please note, that for the cally. In order to strike the right balance between data
purposes of this analysis, the day’s closing price was availability and noise reduction, the analysis was con-
used. ducted based on weekly data.
Since the universe of crypto currencies has in- The weekly returns of the market indices and crypto
creased to over 2,000 at the time of this writing, currencies were calculated from the previous week’s
it was decided to focus on 10 of the largest crypto Friday to the following week’s Friday.
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)
4Table 1: Bloomberg Tickers
Index Ticker Overview
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
MSCI World incl. markets. It aims to reflect the overall economic condition of the existing equity
MXWD
Emerging Markets 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.
The MSCI World index represents the equity markets of 23 developed
countries. It was included into this report to provide a relevant overview of
MSCI World excl.
MXWO the economic conditions in the developed and therefore more stable equity
Emerging Market
markets worldwide.The index is a market cap weighted stock market index of
1,644 stocks from companies throughout the world.
This index was chosen to provide a relevant allocation of governmental
bonds and therefore a fixed income asset class. The funds consists of over
iShares Global Govt. 99% governmental bonds and the remaining percentages as cash. The
IGLO LN
Bond Index 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.
This index was chosen in order to provide relevant information about the
commodity market. The index is calculated on an excess return basis and
Commodities BCOM 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.
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,
Real Estate MXWO0RE
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%.
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,
Private Equity PSPIV
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%.
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
Hedge Funds HFRI5FWC 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.
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%)
Infrastructure IGF US Equity
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%.
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-
Timber & Forestry WOOD US Equity 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%.
ICONIC FUNDS: Investigating the Myth of Zero Correlation Between Crypto Currencies and Market Indices 5ICONIC FUNDS: Investigating the Myth of Zero Correlation Between Crypto Currencies and Market Indices
Methodology
This report uses two different sta- from one another. Secondly, beta
tistical methods to investigate how analysis is conducted to assess how
crypto currencies behave in relation correlated crypto currencies are
to other asset classes. Firstly, corre- compared to traditional market indi-
lation coefficients are calculated to ces. Each of these methodologies is
assess how crypto currencies behave outlined below.
amongst each other. This part of the
analysis will shed some light on the The correlations presented in this re-
question whether crypto currencies port are Pearson correlations. Pear-
can be considered a coherent bas- son correlation coefficients are cal-
ket, and therefore, one single asset culated as per the equation below:
class, or if they are distinguishable
Covariance (x,y)
Pearson correlation(x,y) =
σx σy
Pearson correlation coefficients The beta of an asset describes how
measure the linear correlation be- responsive the asset return is to
tween two variables. It was chosen changes in overall market conditions.
over the Spearman correlation since For example, a beta of 2 implies that
Spearman correlation coefficients the return of the asset would be ex-
are more suitable for ordinal varia- pected to increase by 2% if the gen-
bles rather than continuous data such eral market is up by 1% over the same
as market returns (Simon & Blume, period (Kaplan University, 2013).
2010).
The market betas are calculated in
line with standard portfolio manage-
ment theory as per the equation
below:
Covariance (x,Market)
Beta (x,Market) =
σ²Market
6Table 2: Crypto Currency Overview
Crypto Currency Overview
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
Bitcoin
establish itself as “digital gold”, i. e. a safe haven during times of crisis.
Bitcoin price data is available from 29th April 2013.
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
Ethereum
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 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
XRP faster and more scalable alternative to the existing blockchain based solutions.
XRP price data is available from 4th August 2013.
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
Tether is expected to be stable.
Tether price data is available from 25th February 2015.
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
Litecoin price data is available from 29th April 2013.
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
EOS price data is available from 1st July 2017.
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
BinanceCoin DEX, which is a decentralised exchange for crypto currencies.
BinanceCoin price data is available from 25th July 2017.
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
Tezos price data is available from 2nd October 2017.
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
Chainlink tokens whereas delivery of poor data is punished via the deduction of tokens.
Chainlink price data is available from 20th September 2017.
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 SED LEO
UNUS price data is available from 21st May 2019.
Source: https://coinmarketcap.com/
ICONIC FUNDS: Investigating the Myth of Zero Correlation Between Crypto Currencies and Market Indices 7ICONIC FUNDS: Investigating the Myth of Zero Correlation Between Crypto Currencies and Market Indices
Results
Having disclosed the data and meth- COVID-19 outbreak. low correlations with all other crypto
odology, this section discusses the re- currencies. Based on the information
sults of the analysis. Firstly, the corre- Correlation between presented in Table 2, this result is to
lation between the crypto currencies crypto currencies be expected. Since Tether is consid-
is discussed, followed by a presenta- ered a stablecoin, which means that
tion of the crypto currencies’ correla- The results of the correlation analysis its value should not deviate signifi-
tions with the market and their betas. between crypto currencies is present- cantly from 1 USD, it is expected that
ed in Table 3. The table shows the the price of Tether does not move as
Furthermore, it will be shown how Pearson correlations in percentage freely compared to other crypto
correlations change during liquidity points. Note that statistical signifi- currencies.
crises. For this case study, the cor- cance is represented by asterisks, as
relations are calculated only for the per the legend. The second observation is that LEO
time period 1st January 2020 – 31st appears to have lower correlations
March 2020, which approximately Three general observations emerge to other crypto currencies than the re-
reflects the time when markets were from the results in Table 3. Unsur- maining coins. This may be driven by
initially adjusting in lieu of the prisingly, Tether appears to have the facts that LEO has different value
Table 3: Correlation Results between Cyrpto Currencies
Ether- Binance- Chain-
Bitcoin XRP Tether Litecoin EOS Tezos
eum Coin link
Ethereum 33% ***
XRP 33% *** 32% ***
Tether 4% -3% 3%
Litecoin 63% *** 38% *** 62% *** 1%
EOS 61% *** 60% *** 50% *** 9% 59% ***
Binance-
33% *** 29% *** 15% * 10% 18% ** 13%
Coin
Tezos 45% *** 50% *** 27% *** 0% 40% *** 36% *** 44% ***
Chainlink 48% *** 61% *** 42% *** 2% 41% *** 27% *** 57% *** 35% ***
UNUS SED
17% 37% ** 40% *** 8% 41% *** 41% *** 23% 16% 18%
LEO
* Significant at the 10% level
** Significant at the 5% level
*** Significant at the 1% level
8drivers 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.
ICONIC FUNDS: Investigating the Myth of Zero Correlation Between Crypto Currencies and Market Indices
9ICONIC FUNDS: Investigating the Myth of Zero Correlation Between Crypto Currencies and Market Indices
Correlation with The reason is liquidity. was analysed when the daily trading
traditional market indices volume of each crypto currency first
When crypto currencies are first hit 100,000,000 USD. All observa-
As mentioned in the introduction, the launched, their secondary market li- tions prior to that date were excluded
general public assumption is that quidity is negligible. This even applies from the sample. In the second sce-
crypto currencies are uncorrelated to the first few years of Bitcoin. During nario, this threshold was increased
with traditional market indices. This these infant stages of a crypto curren- to 500,000,000 USD. Whilst these
section will analyse this assumption cy, very few people trade it. By defi- numbers are negligible in the context
in detail and determine whether it is nition, correlations with other market of developed financial markets, it is a
valid. As a starting point, the Pearson indices are expected to be close to sizeable volume in the relatively new
correlations were calculated be- zero because there aren’t enough crypto currency market. The results of
tween the returns of the crypto cur- market participants to influence prop- this analysis are presented in Table 5
rencies and the market indices over er price discovery. Rather than being and Table 6.
the entire period available. The re- influenced by systemic market events,
sults of this analysis are presented in prices are driven by random, and of- Firstly, Tether once again does not
Table 4. ten illogical, behaviour. correlate well with other market in-
dices. Based on the results from the
Those results do indeed show limited The influence of liquidity should be previous section, this finding is in line
correlation between crypto curren- accounted for before drawing the with expectations. Since Tether is a
cies and financial markets. Bitcoin, conclusion that crypto currencies stablecoin, which does not exhibit
Ethereum and Chainlink are the only are uncorrelated with the market. drastic price movements, it would not
currencies that exhibit some statisti- The dataset was filtered for observa- be expected to correlate with market
cally significant correlation with the tions where liquidity had already im- indices.
major indices. Whilst this seemingly proved. Since there is no clinical term
confirms the hypothesis that crypto for what defines a “liquid crypto cur- When comparing the results of Table
currencies are uncorrelated with the rency market”, two scenarios were 4, Table 5 and Table 6, one general
market, these results are misleading. investigated. In the first scenario, it trend emerges. As shown, the corre-
Table 4: Correlation Crypto Currencies with Market Indices (entire history)
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
0% 0% 2% 14% -10% 1% -2% -1% 4%
LEO
* Significant at the 10% level
** Significant at the 5% level
*** Significant at the 1% level
10lations increase as liquidity increases Meanwhile, the returns with the glob- generally tend to increase across
with statistical significance. For exam- al and US bond indices are not sig- asset classes. This section analyses
ple, the correlation measured over nificant. This is to be expected, how- whether this phenomenon also ap-
the entire sample between Bitcoin ever, since these traditional market plied to crypto currencies during the
and the MSCI World (excl. emerg- indices barely correlate with bond onset of COVID-19 in Q1 2020. The
ing markets) is 10%, which is signifi- indices, historically. results are presented in Table 7.
cant at the 10% level. The correlation As shown, the Pearson correlations
between the same two variables in- The correlations with the alterna- increased across the board, sup-
creases to 11% significant at the 5% tive investment class indices are less porting this hypothesis. Furthermore,
level when zooming in on a time clear-cut. Crypto currencies appear statistical significance increased as
when Bitcoin started trading with a more correlated with private equity well, evidencing that the higher cor-
volume of 100 million USD. Looking funds as well as infrastructure funds relations depicted are valid. Whilst
only at a time when Bitcoin started but do not correlate well with real es- the correlation coefficients for the
trading with a volume of 500 million tate and forestry. bond indices are not significant, their
USD, the correlation increases even point estimates increased drastically,
further to 16% significant at the 5% Based on the results presented, it which shows that the indices moved
level. This trend is equally applicable appears that crypto currencies are in the same direction.
to the other crypto currencies and slightly correlated with the tradition-
shows that they move in line with the al financial market. Correlations are Based on these findings, it is evident
traditional market to a certain extent. highest with equity indices, whereas that the correlations between crypto
bonds exhibit lower correlations to currencies and other asset classes
The crypto currencies are not corre- crypto currencies. increased considerably during the
lated with all market indices, howev- most recent liquidity crisis.
er. The correlations with large equity Correlation during the
indices, such as the MSCI World in- Q1 2020 liquidity crisis
dices and the commodity index, are
still low but statistically significant. During times of crisis, correlations
Table 5: Correlation Crypto Currencies with Market Indices (100 million USD trading volume)
MXWO MXWD IGLO LN FXNAX BCOM MXWO0RE PSPIV IGF US WOOD US
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
Hasn‘t reached trading volume of 100 million USD yet
LEO
* Significant at the 10% level
** Significant at the 5% level
*** Significant at the 1% level
ICONIC FUNDS: Investigating the Myth of Zero Correlation Between Crypto Currencies and Market Indices 11ICONIC FUNDS: Investigating the Myth of Zero Correlation Between Crypto Currencies and Market Indices
Table 6: Correlation Crypto Currencies with Market Indices (500 million USD trading volume)
MXWO MXWD IGLO LN FXNAX BCOM MXWO0RE PSPIV IGF US WOOD US
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
Hasn‘t reached trading volume of 500 million USD yet
LEO
* Significant at the 10% level
** Significant at the 5% level
*** Significant at the 1% level
Table 7: Correlation Crypto Currencies with Market Indices during Q1 2020
MXWO MXWD IGLO LN FXNAX BCOM MXWO0RE PSPIV IGF US WOOD US
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% *
Tezos Hasn‘t reached trading volume of 500 million USD yet
Chainlink 26% * 27% * 12% 16% 27% * 23% 24% 31% ** 27% *
UNUS SED
Hasn‘t reached trading volume of 500 million USD yet
LEO
* Significant at the 10% level
** Significant at the 5% level
*** Significant at the 1% level
12Market Betas As expected, the beta of Tether is close to zero, be-
cause it is a stablecoin. The betas of the other crypto
Building on the analysis of correlations between crypto currencies are in the range of 0.8 – 2.7. The previous
currencies and market indices raises the question what sections showed that the correlations with the MSCI
the market betas are for crypto currencies. Recall from Worlds, commodities, private equity and infrastructure
the methodology section that the betas measure the ex- indices were statistically significant. Therefore, the focus
pected responsiveness of an asset relative to market should be placed on the betas corresponding to those
movements. Since beta analysis is only meaningful for a indices. The betas of Bitcoin appear to be slightly lower
liquid market, the analysis focusses on the sample where compared to the betas of Ethereum. For example, a 1%
daily trading volumes have reached 500 million USD return of the MSCI World (excl. emerging markets) is
for the respective crypto currency. The results are pre- likely to lead to a 0,79% return of Bitcoin, but a 1.43%
sented in Table 8. return of Ethereum.
Table 8 : Crypto Currency Betas with Market Indices (500 million USD trading volume)
MXWO MXWD IGLO LN FXNAX BCOM MXWO0RE PSPIV IGF US WOOD US
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
Tezos Hasn‘t reached trading volume of 500 million USD yet
Chainlink 1.40 1.47 1.83 3.69 1.98 1.04 1.04 1.32 1.10
UNUS SED
Hasn‘t reached trading volume of 500 million USD yet
LEO
Note: The betas that are greyed out are not statistically significant
ICONIC FUNDS: Investigating the Myth of Zero Correlation Between Crypto Currencies and Market Indices 13ICONIC FUNDS: Investigating the Myth of Zero Correlation Between Crypto Currencies and Market Indices
Conclusion
The previous sections presented anal- activity and liquidity was so low in
ysis of the correlations of crypto cur- the early years of crypto currencies
rencies amongst each other as well that there could not have been any
as correlations and betas of crypto meaningful correlation with the rest
currencies with traditional market of the market due to a lack of price
indices. discovery.
It was found that the correlations When adjusting for crypto curren-
within the crypto currency basket are cy market liquidity, it was found that
high unless the coins are structurally crypto currencies are, indeed, slightly
different from the others, such as correlated with the traditional market.
Tether and LEO. Furthermore, it was found that like
most other asset classes these cor-
More importantly, the analysis of relations increase during a liquidity
correlations with regards to the tradi- crisis event. Market betas were found
tional market showed that the general to be in the range of 0.8 – 2.7, de-
public assumption of zero correlation pending on the crypto currency. In
between crypto currencies and the fi- any event, this analysis disproves the
nancial markets is not true. Whilst the assumption that crypto currencies are
overall correlations were found to be uncorrelated with financial markets
statistically insignificant, the under- and shows that they are more intri-
lying reason was not that the assets cately linked than is generally
are truly uncorrelated, but that market believed.
14References
Coin Kurier, 2019. UNUS SED Kaplan University, 2013. Schwes-
LEO: Warum dieser Token aus dem er Notes 2014 CFA Level 1 Book 4:
Nichts in die Top 15 stieg!. [Online] Corporate Finance, Portfolio Man-
Available at: https://www.coinkuri- agement, and Equity Investments.
er.de/unus-sed-leo/ United States of America: Kaplan,
[Accessed 10 06 2020]. Inc..
CoinMarketCap, 2020. Top 100 Simon, C. & Blume, L., 2010.
Cryptocurrencies by Market Capital- Mathematics for Economists, Interna-
ization. [Online] tional Student Edition. s.l.:Norton.
Available at: https://coinmarketcap.
com/
ICONIC FUNDS: Investigating the Myth of Zero Correlation Between Crypto Currencies and Market Indices
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