Understanding
Cryptocurrencies
Wolfgang Karl Härdle*
Campbell R. Harvey *²Raphael C. G. Reule *³

*

*²
*3

Humboldt-Universität zu Berlin, Germany.
Wang Yanan Institute for Studies in Economics, Xiamen University, China.
Sim Kee Boon Institute for Financial Economics, Singapore Management University, Singapore.
Faculty of Mathematics and Physics, Charles University, Czech Republic.
Duke University, Durham, NC, USA.
National Bureau of Economic Research, Cambridge MA, USA.
Humboldt-Universität zu Berlin, Germany.

This research was supported by the Deutsche
Forschungsgemeinschaft through theInternational Research Training Group 1792
"High Dimensional Nonstationary Time Series".
http://irtg1792.hu-berlin.de
ISSN 2568-5619

International Research Training Group 1792

IRTG 1792 Discussion Paper 2018-44

Understanding Cryptocurrencies
Wolfgang Karl Härdle
Humboldt-Universität zu Berlin, Germany.
Wang Yanan Institute for Studies in Economics, Xiamen University, China.
Sim Kee Boon Institute for Financial Economics, Singapore Management University, Singapore.
Faculty of Mathematics and Physics, Charles University, Czech Republic.
haerdle[at]hu-berlin.deCampbell R. Harvey
Duke University, Durham, NC, USA.
National Bureau of Economic Research, Cambridge MA, USA.
cam.harvey[at]duke.edu

Raphael C. G. Reule
Humboldt-Universität zu Berlin, Germany.
irtg1792.wiwi[at]hu-berlin.de

3rd August 2019
Abstract
Cryptocurrency refers to a type of digital asset that uses distributed ledger, or
blockchain, technology to enable a secure transaction. Although the technology
is widely misunderstood, many central banks are considering launching their own
national cryptocurrency. In contrast to most data in financial economics, detailed
data on the history of every transaction in the cryptocurrency complex are freely
available. Furthermore, empirically-oriented research is only now beginning, presenting an extraordinary research opportunity for academia. We provide some insights
into the mechanics of cryptocurrencies, describing summary statistics and focusing
on potential future research avenues in financial economics.
JEL Classification: C01, C58, E42, E51, G10, K24, K42, L86, O31
Keywords: Cryptocurrency, Blockchain, bitcoin, Economic bubble, Peer-to-Peer, Finance,
Cryptographic hashing, Consensus, Proof-of-work, Proof-of-stake, Volatility

T he f inancial support of Czech Science F oundation under grant no. 19 − 28231X is
acknowledged.

1

1

Introduction

In 2008, the pseudonymous “Satoshi Nakamoto” posted a white paper describing animplementation of a digital currency called bitcoin that used blockchain technology. Morethan ten years later, hundreds of cryptocurrencies and innumerable other applications ofblockchain technology are readily available.
The rise of cryptocurrencies poses an existential threat to many traditional functions
in finance. Cryptocurrencies embrace a peer-to-peer mechanism and effectively eliminate
the “middle man”, which could be a financial institution. For example, no bank account or
credit card is needed to transact in the world of cryptocurrencies. Indeed, a cryptocurrency“wallet” serves the same function as a bank vault. With a smart phone and the internet,
the potential exists for a revolution in financial inclusion — given that over two billion
people are unbanked (GlobalFindex, 2017; World Bank, 2017).
The technology, however, goes well beyond providing banking services to the unbanked.
It holds the potential for cheap, secure, and near-instant transactions, allowing billions
of people to join the world of internet commerce, paying, and being paid, for goods orservices, outside of the traditional banking and credit card infrastructure.
Cryptocurrencies transactions potentially enable near real-time micropayments. Credit
cards are not designed to be used for a one-cent charge to download, for example, aproduct or service from the internet. Cryptocurrency systems promise to make micropayments seamless and allow businesses to offer real-time pay-per-use consumption of theirproducts, such as video, audio, cell phone service, utilities, and so forth.
A cryptocurrency like bitcoin can be thought of as a decentralized autonomous organization (DAO), an open-source peer-to-peer digital network that enforces the rules it is
set up with. In this DAO setting, the money supply is set by an algorithmic rule,and the integrity of the network replaces the need to trust the integrity of humanparticipants. The growth of crypotcurrency technology therefore poses a challenge to
traditional monetary authorities and central banks, as Facebook’s “Libra” coin preemission market acceptance suggests (Taskinsoy, 2019). Central banks understand this,and many banks have initiated their own national cryptocurrency initiatives (Bech andGarratt, 2017).
As with any new technology, risks are present. In the nascent cryptocurrency market,
one concern involves the anonymous nature of transactions in some cryptocurrencies,
which could allow nefarious actors to conduct illegal business, or worse, to pose a broader

2threat to our society and institutions (Foley et al., 2018). The benefits, such as lowtransaction cost, security and the promise of quick processing, are readily measurable,
but quantifying the risks is less straightforward.
In our view, any new technology involves risks; if we require no risk, innovationis constrained (Catalini and Gans, 2016). Cryptocurrencies have, in contrast to manymarkets, a plethora of available and free data, ripe for empirical investigation. We arejust now seeing the genesis of academic research focusing on this emerging technology(Harvey, 2014, 2017a, 2017b; Härdle et al., 2018; Kim et al., 2019).
We have four goals in this paper. First, we explain the mechanics of cryptocurrencies
at a high level. Second, we detail useful data sources for researchers. Third, we provide
basic summary statistics given the available data. Finally, we offer a list of possible
research applications.

2

Cryptocurrencies and Blockchains

The concept of supplementary (Delmolino et al., 2016), alternative (Ametrano, 2016), or
digital currencies (Chaum, 1983) is not new, but the concept of an open-source currency
without a central point of trust, such as a central distribution agency or state lead control,
is new (King and Nadal, 2012). A cryptocurrency is a digital asset designed to work as a
medium of exchange using cryptography to secure transactions, to control the creation of
additional value units, and to verify the transfer of assets. Many different cryptocurrencies
exist, each with their own set of rules, see, for example, coinmarketcap.com (Iwamura
et al., 2014; Abraham et al., 2016; Bartos, 2015; Park et al., 2015). Differences among
the cryptocurrencies may involve, for example, the choice of the consensus mechanism,
the latency, or the cryptographic hashing algorithms.

2.1

High-level description of blockchain

Abadi and Brunnermeier (2018) describe a blockchain trilemma, i.e. that no ledger cansatisfy all ideal qualities of any recordkeeping system — correctness, decentralization,and cost efficiency — simultaneously. Yet, a blockchain is more efficient than a centrally
managed traditional ledger (Babich and Hilary, 2018a). A blockchain can be implemented
in many ways, but most share several common features. We can think of a blockchain asa very special database. A blockchain’s structure is shared, or distributed, rather thancentralized, and thus is often referred to as distributed ledger technology (DLT). Figure 1
shows a distributed network. As we discuss later, the distributed network provides somelevel of security, because it is unlikely an attack can be launched on every copy of the3database. Distributed databases are not new, and most distributed databases are notblockchains. The key difference between a regular distributed database and one set on a
blockchain is the structure (Babich and Hillary, 2018b).
A blockchain is divided into subsheets of data, each one called a block. At the end ofeach block is a digest that summarizes the contents of the block. The digest is repeatedas the first line of the next block. If any change is made in the content of a historical
block, the digest changes for that block and it will not match the first line of the next
block. When the network detects such an inconsistency, it throws out the corrupted
block and replaces the block with the original. In this sense, the database is immutable.
Given this structure (i.e., data organized in blocks with updates to the blockchain being
append-only, based on the respective consensus mechanism), it is extremely unlikely that
history can be rewritten. The digest at the end of a block and at the beginning of thenext is generated by a cryptographic hashing function.

(a) Centralized(b) Decentralized(c) Distributed

Figure 1: Types of networks.
All presented numerical and pictoral examples shown are reproducible and can befound onwww.quantlet.de (Borke and Härdle, 2018).

2.2

Hashing

A hash function is a one-way mathematical algorithm that takes an input and transforms
it into an output, known as the hash or digest. Hashing functions have a long history incomputer science and are integral to the blockchain technology. Hashing should not be
confused with encryption. With encryption, a file is encrypted with a key and decryptedwith a key. Hashing has no decryption step. Additionally, a good hashing algorithm
makes it computationally infeasible to find two input values that produce the same hashvalue (output); this is known as collision resistance (Paar and Pelzl, 2010; Derose, 2015;
Harvey, 2016).

4

One common cryptographic hashing algorithm, the Secure Hash Algorithm (SHA256), has a maximum input size of 264 -1 bits (more than 2 million terabytes) and an
output of 256 bits. We usually represent the SHA-256 output in hexadecimal form, also
called base 16 (the characters 0-9 and a-f). In order to make the theoretical maximal input
size more visual, we assume that 1 bit equals to 1 mm2 . A soccer field has the dimensionsof 7,140 m2 , therefore 264 -1 bits could theoretically fill 2,583,577,601 soccer fields. As thewhole surface of the earth equals to 510,000,000,000,000 m2 , we could also cover around36170 times the earth with the theoretical input size. This input information will be
stored in a very short output, the hash. If just one piece of the input, like for example ablank space or a comma, is changed, then the hash output be completely different.
Importantly, the digest does not reveal the original information. For example, suppose
we want to send an electronic document via email, but are worried that the document
could be corrupted and the content altered. One way to verify the integrity of the email
is to use a hashing function, such as the SHA-256. Before sending the email, we obtaina SHA-256 of the document and post the SHA-256 on our website. We then send the
document. The recipient also hashes the document to verify the hash is the same as thehash on our website. If they are identical, we have securely sent the document. Posting
the hash on our website does not reveal the content of the email.
Here are some examples, which you can try by using the R package “digest”, the Python
libary “hashlib”, or many online programs such as interactive Github-based repositories.
Input: Hello CRIX
Output: f9a2b57d86cc4ba463a3bedbbe0c7e850da5b34c6bcc1a92b794308ceaf93761
Input: Hallo CRIX
Output: 0198c2ea3632efd2758cd40a5609037fe4aa1590850339ad6d6a7fd3e518ec65
Note that changing a single letter from “e” to “a” completely changes the hash.

2.3

Blockchains

Haber and Stornetta (1991) were the first to propose a linear hash chain or blockchain.
They solved the problem of how to certify when a digital document was created or lastchanged by timestamping a cryptographic hash of the document. By not timestamping
the data itself, the privacy of the content was preserved. Haber and Stornetta’s timestamping proposal also solved the potential problems of collusion and lack of trust by
linking hash values together and using digital signatures, which uniquely identify thesigner.

5

A year later, Dwork and Naor (1992) proposed a proof-of-work system to combat junkemail. Their idea was to provide each email with a header containing virtual postage inthe form of a single calculation, which the receiver could verify with very little effort.
This postage stamp was to be proof that a modest amount of CPU time was expended
for calculating the stamp prior to sending the email. Whereas an individual email could
be sent at a very low cost, the intent was to defeat spammers, who send millions ofemails. Spamming would come at a high price. Back (2002) coined the term hashcash
to describe this proof of work, the computational cost of producing each hash, a term
first used by Jakobsson and Juels (1999).
Many applications of blockchain technology exist, but we focus our attention on
cryptocurrencies. Bitcoin (cryptocurrency known as BTC) was the first example of adigital asset, which has no backing or intrinsic value, based on blockchain technology(Nakamoto, 2008; Böhme et al., 2015, for a review).
A common characteristic of cryptocurrencies is a network of peers with equal standing.
Each participant has a copy of the ledger and offers an algorithmic consent on the correct
ledger (i.e., which new block is accepted and which block is rejected to form a new partof the blockchain). It is unneccesary to know your peers in a blockchain or to trust them.
It is also possible to design a blockchain so that only specific trusted parties have theability to add to the ledger. Private, permissioned blockchains are a source of considerable
interest for many central banks (MAS, 2017; Bundesbank, 2017; SARB, 2018). In contrast
to cryptocurrencies such as bitcoin, trust is necessary in the permissioned blockchain,because the central banks actually “own” the coins, i.e. as a governing layer they havethe right to change the supply of coins (Bordo and Levin, 2017).
Any type of transaction, for example, a financial contract for any type of propertytransfer, can be put into a blockchain. Given its immutability, a blockchain provides anofficial record of the contract and a single agreed-upon version of the contract, which isunlikely to be disputed.
To summarize, a blockchain is distinguished from an ordinary distributed databaseby its unique structure, which linearly connects smaller pieces of the database, or theblocks. The chaining comes in the form of a cryptographic hashing function. Any change
to history will break the chain on a particular copy of the database. When a chain isbroken, the network fixes it by replacing any corrupted block with a valid block.

6

2.4

Cryptocurrencies

A currency without an intrinsic value, such as a cryptocurrency like bitcoin, can only
function if sufficient market acceptance is present and if the belief exists that the currency
has the value attributed to it. With a conventional fiat system, money has value because
people trust the central bank. For a cryptocurrency, additions to the public ledger are
confirmed by a crowd of participants. There is no central bank and participants do notneed to trust each other — trust only applies to the algorithm and the network thatdefines the particular blockchain. A transaction is only valid if the output is equal to the
input, that is, the transactor actually has the funds she or he wants to transfer. The only
exceptions are new issues of the cryptocurrency, which are algorithmicly predetermined.
We have demonstrated the simplicity of creating a SHA-256 hash to link one block to
the next. Why is it then that massive computing power is needed to maintain the bitcoin
network? The power required has to do with the proof-of-work consensus concept. The
danger of using a simple SHA-256 is that a nefarious actor could change a historical blockand all subsequent blocks, essentially rewriting history, by ensuring all hashes match.
To make this unlikely, Nakamoto (2008) proposed the idea of requiring “work”. Thus,
instead of simply providing any SHA-256 output, a special SHA-256 output, which has
many leading zeros, is required. In other words, the proposed SHA-256 hash needs to belower than or equal to the current target in order for the block to be accepted by the
network as the next block to be added to the blockchain. This “difficulty” ensures thata new block is added on average every 10 minutes to the bitcoin blockchain (so-calledblock time). To find this special hash, certain nodes, called miners, will take a candidate
group of verified transactions and cycle through numbers, say, 1, 2, 3, . . . [very large
number], until the output of the SHA-256 has some leading zeros. This number, whichis added to a digest of the transactions, is called a nonce.
The computing power requirement arises because the leading zeros are determined
via a brute-force search. The probability of one leading zero is 1/16, but the probabilityof, for example, 18 leading zeros is a very small number, (1/16)18 . The search is why thevast computing power is needed, see subsection 2.2.
The first miner that finds the (currently) 18 leading zeros, as in our example, presents
its group of transactions and the nonce to the network. Verifying that the transactions
plus nonce delivers the leading zeros is easy. Once each node verifies the candidate block,the new block is added to the bitcoin blockchain. This process is the bitcoin consensus
mechanism. The miner that found the winning block is rewarded with freshly “minted”
bitcoin. If technology advances or additional computing joins the mining efforts in the7network so that blocks are being solved in less than 10 mintues, the algorithm adjusts
the difficulty to, perhaps, 19 leading zeros. If computing power leaves the network, thedifficulty can be reduced.
Cryptocurrency mining is therefore analogous to gold mining. Gold mining is expensive.
Cryptocurrency miners spend computing power to find the hash as described above. A
gold miner only gets rewarded if gold is found. Cryptocurrency miners only get rewarded ifthey are the first to find the winning hash. Like mining for gold, mining for cryptocurrency
is risky. The continuous expenditure of resources such as for hardware and energy (seealso subsection 4.9) for a prolonged period without being rewarded is an inherent risk.
Proof of work makes it unlikely that a historical block and all subsequent blocks canbe altered, but securing the highly specialized computing power needed to rewrite history
is not currently likely. Nakamoto (2008) states that if a single entity gains 51% of thecomputing power, it is possible.
Proof of work is only one approach to consensus, many alternative mechanisms exist
and they may not entail the high equipment and energy costs that bitcoin miners face.
The second leading cryptocurrency, ethereum, uses a similar proof-of-work mechanism.
Ethereum, however, has committed to change to a proof-of-stake mechanism (Franco,
2015; ETH, 2018). Instead of allocating block mining proportionally to the relative
hashing power, the proof-of-stake protocol allocates blocks proportionally to the current
holdings (Buterin, 2014; Cotillard, 2015). As a result, the participants with the mostcryptocurrency are particularly incented to do the right thing to keep the system runningand healthy. Such a method holds the promise of much-improved latency and substantiallyless energy consumption. A participant who possesses 1% of the cryptocurrency could
mine 1%, on average, of the proof-of-stake blocks. Ethereum has a number of other
differences from bitcoin. Ethereum blocks are added approximately every 14 seconds
rather than every 10 minutes, and importantly, ethereum allows for smart contracts, or
small computer programs, to be deployed in its blockchain. These smart contracts arerun redundantly on each node.
Many other consensus mechanisms are currently available: STEEM’s proof of brain
rewards participants for creating and curating content in their social network (STEEM.io
Bluepaper) and Slimcoin’s proof of burn bootstraps one cryptocurrency off another bydemonstrating proof of having “burnt” some units of value by sending a specific amountto a verifiable unspendable address (Slimcoin Whitepaper), or different implementationsof the Byzantine fault tolerance, which was first described as the Byzantine Generals’
Problem by Lamport et al. (1982), are used by systems such as NEO, Stellar and8Hyperledger Fabric.

2.5

Not all cryptocurrencies are the same

We can group cryptocurrencies into seven broad classes. Bitcoin falls into the firstcategory; it was originally designed as a transaction mechanism. Think of it as Gold 2.0.
Litecoin is very similar to bitcoin and was one of the first alternatives to bitcoin. Litecoin’s
blocks are added every 2.5 minutes, on average, compared to every 10 minutes for bitcoin.
Ethereum falls into the second class: a distributed computation token. As mentioned
earlier, it is possible to run a computer program on the ethereum network. Think of
it as an Internet computer where small programs, smart contracts, are executed whencalled upon, on every node. Other examples in this class include Tezos, EOS and DFinity.
The third class of cryptocurrency is called a utility token. A utility token is a
programmable blockchain asset. One example is Golem, a currency that allows the user
to buy computing power from a network of users or to sell excess capacity to others. Storj
is similar and allows the user to rent out unused disk storage. Other examples in thisclass are Sia and FileCoin.
The fourth class of cryptocurrency is a security token, a token that represents stocks,
bonds, derivatives, or other financial assets. New security token offerings are called
STOs. This type of token could lead to substantial efficiency gains in both clearing andsettlement.
The fifth class is called f ungible tokens. The most popular is called ERC-20 which isissued on the ethereum blockchain. Here a small amount of ETH represents something
different – and more valuable.
A non-f ungible token is the sixth classification. In this case, each token is uniqueand not interchangeable with another. One popular protocol is ethereum’s ERC-721.
Dhrama debt agreements fall into this classification. Two other eamples of non-fungible
tokens are Cryptokitties and Decentraland (LAND).
The final class of cryptocurrencies are called stablecoins. There are four categories.
The first category is collateralized with fiat currency. This includes stablecoins suchas tether (USDT) and Circle’s USDC. These cryptocurrencies are designed to be fully
collaterized by US dollar deposits. LBXPeg is tied to pound sterling. An emerging
market, Mongolia has a cryptocurrency called Candy tied to their currency. This class also

9includes national cryptofiats. As mentioned earlier, many central bank are investigating
the potential Fedcoin (US Federal Reserve), Eurocoin (European Central Bank), CADCoin(Bank of Canada), for example. Venezuela already issued a national crypto called Petro.
The second category of stablecoins are collateralized with real assets. Examples
include currencies that are collateralized by gold (Digix Gold, DGX), a basket of sevenprecious metals used in technology (Tiberius coin, TCX) or even Swiss real estate (Swiss
Real Coin, SRC).
The third category of stablecoins are cryptocurrency collateralized. The leading
example is the collateralized debt positions that MakerDAO offers that enable their DAI
coin to be pegged to the US dollar.
The final category of stablecoins are uncollateralized. An example of this type of
inititive is the Basis project and their basecoin which has been put on hold given regulatory
concerns.
This list of classifications is not exhaustive because many cryptocurrency concepts,
such as Overlay or Facebook’s Libra, do not easily fit within our seven-category taxonomy.
Our point is simple: cryptocurrencies have many uses and characteristics that extend
beyond the traditional cryptocurrencies of bitcoin and ethereum.

3

Summary Analysis of Cryptocurrencies

We will now focus on an econometric analysis of the currently most liquid cryptocurrencies.
Valuation of currencies that are not collateralized or linked to real assets is a challenge.
These currencies are highly volatile and subject to bubble-like behavior. These currencies,
however, provide an ideal testing ground for economic theory. In the fall of 2017, bitcoinrose to over $19,000. The bubble burst in 2018. Because every bitcoin transaction isfreely available, we are provided with an extraordinary research opportunity. We begin
with a simple benchmarking analysis using the S&P 500 Index (S&P 500), SPDR Gold
Shares (GOLD), and CBOE Volatility Index (VIX), which measures the implied volatilityof the S&P 500 index.

3.1

Cryptocurrency data sources

Many, sometimes very generic, data sources are available for cryptocurrencies, which
unlike traditional assets trade 24/7, creating a vast amount of data to capture. Blockchainbased systems — most of which are open to the public for participation — have data10

that are readily available using basic API’s (application programming interface).1 In
subsection 4.3, we discuss exchange APIs, which provide the data for actual cryptocurrency
market transactions (Guo and Li, 2017). Several of the more important data sources
include CoinGecko, a cryptocurrency ranking and evaluation site that breaks down quantitative and qualitative data for a number of different metrics, as well as Coinmarketcap,
Onchainfx, Cryptocompare, BitInfoCharts, CoinCheckup, and Coincodex. Each has
unique attributes.

3.2

Statistical overview of cryptocurrencies

While there are thousands of cryptocurrencies, we focus our analysis on three: bitcoin(BTC), ethereum (ETH), and Ripple (XRP). To represent traditional assets, we havechosen S&P 500, GOLD, and VIX. Each cryptocurrency has a different implementation.
Some, like Litecoin, are very similar to BTC. As previously mentioned, ETH allows for
distributed computation. In contrast to BTC, ETH may be easier to value because it hasa tangible component (i.e., running a computer program on a network).
XRP focuses on the banking sector with the promise of fast and secure transfers oftokens, whether in fiat, cryptocurrency, commodity, or other unit of value, across different
networks, geographic borders, and currencies (Aranda and Zagone, 2015). The Ripple
system’s efficiency and security challenges the traditional SWIFT system for transfers,
which is now also interested in blockchain-based technologies (Arnold, 2018).
In Figure 2, we show the cumulative return over time for BTC, XRP, ETH, SPDRGOLD Shares and S&P 500 from May 1, 2017 to Jun. 30, 2019. We chose this shorttime period because, prior to this, the cryptocurrency market was substantially illiquid;
it was not until 2016 that the initial influx of exchanges and users entered the market.
By May 2017, all three cryptocurrencies were active and had achieved sufficiently highmarket capitalizations. Although BTC has the highest market capitalization and hadreceived intensive media exposure prior to our sample period, this time period allows
us to capture both the liquid trading period and the full sample of ETH and XRP in
addition to BTC.
1

For this brief analysis, we are using cryptocurrency data provided by the CRIX database (thecrix.de)
and the Cryptocompare API, as well as data for the traditional assets provided through the Bloomberg
Terminal. Further information can also be found at Jameson Lopp’s Bitcoin Resources.

11

Cumulative return over time

Cumulative return

2.0

1.5

1.0

0.5

0.0
2018

2019
Date

Figure 2: Cumulative return over time between May 1, 2017 and Jun. 30, 2019 of BTC,XRP, ETH, GOLD and S&P 500.
XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXLXX
Tables 1 and 2, as well as Figure 3, provide the correlations of the daily and monthly
returns from May 1, 2017 to Jun. 30, 2019 for the five assets and VIX. Red indicates a
positive correlation and blue indicates a negative correlation, with significant correlations
being marked in a darker color. The correlations are likely time varying. Figure 3 shows
the correlation time series of rolling windows of one trading year (250 days) relative to
BTC. Both XRP and ETH are positively correlated with BTC. No evidence is shown ofa significant correlation with S&P 500, GOLD or the VIX.

Table 1: Daily Correlation, May 1, 2017 to Jun. 30, 2019.
Daily BTCBTC
ETH 0.42
XRP 0.21
GLD 0.04
SP500 0.04
VIX -0.06

ETH
0.42
0.20
0.06
0.01
-0.01

XRP
0.21
0.20
0.04
-0.01
-0.02

12

GLD
0.04
0.06
0.04
-0.15
0.13

SP500 VIX
0.04 -0.06
0.01 -0.01-0.01 -0.02-0.15 0.13
-0.80
-0.80

Table 2: Monthly Correlation, May 1, 2017 to Jun. 30 2019.
BTC
BTC
ETH
XRP
GLD
SP500
VIX

0.48
0.45
0.08
0.13
-0.08

ETH
0.48

XRP GLD
0.45 0.08
0.58 0.26
0.58
0.15
0.26 0.15
0.12 -0.08 -0.10-0.19 0.02 0.17

SP500 VIX
0.13 -0.08
0.12 -0.19-0.08 0.02
-0.10 0.17
-0.75
-0.75

We note, first, the cryptocurrencies are positively correlated, which is especiallyevident in an analysis of the monthly data. Second, the correlations of the cryptocurrencies
with both S&P 500 and GOLD are relatively low over the limited sample. We also include
the correlation with VIX, which largely hovers around zero.
Figure 4 plots the 100-day rolling window standard deviations for each asset and VIX.
Most cryptocurrencies are an extremely risky store of value given their volatility, whichis evident from the volatility of the cryptocurrencies being much higher than those of
GOLD and S&P 500.

0.4
0.2
−0.2

0.0

Correlation

0.6

0.8

250 days Rolling Window Correlation to BTC

2017

2018

2019

Time

Figure 3: 250 days Rolling Windows Correlations of XRP, ETH, GOLD, S&P 500
and VIX to BTC; daily data, May 1, 2017 to Jun. 30, 2019.
XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXLXX

13

0.10
0.00

0.05

Standard Deviation

0.15

100 days Rolling Window Standard Deviation

2018

2019
Time

Figure 4: 100 days Rolling Window Standard Deviation of BTC, XRP, ETH, GOLDand S&P 500; daily data, May 1, 2017 to Jun. 30, 2019.
XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXLXX
Further insights into the distributional properties of cryptocurrencies can be gainedby studying the higher moments of returns, for example, excess kurtosis and skewness, as
shown in Table 3. Not surprisingly, the higher moments of the cryptocurrencies are far
from what we would expect for a normal distribution. This observation is also evident in
the QQ plots for BTC and GOLD shown in Figure 5.

Table 3: Log Daily Returns Statistics, May 1, 2017 to Jun. 30, 2019.

BTC
ETH
XRP
GLD
SP500

Mean

Std. Dev.

Skewness

0.00280.00190.00280.00020.00040.04540.05940.07670.00620.00860.04520.15011.60530.1681-0.5997

14

e. Kurtosis

Min.

Max.

2.8227 -0.1892 0.2276
2.0517 -0.2228 0.2602
10.3886 -0.3671 0.6183
1.0159 -0.0172 0.0254
5.1430 -0.0418 0.0484

●●Standard Normal QQ plot for BTC returns●

Standard Normal QQ plot for GLD returns

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Theoretical Quantiles

Theoretical Quantiles

XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXLXX
Figure 5: Standard Normal QQ plots for BTC and GOLD, May 1, 2017 to Jun. 30, 2019.
Table 4: Johansen cointegration of gold and bitcoin, May 1, 2017 to Jun. 30, 2019.
H0

Test statistic

10 %

5%

r≤1
r=0

3.11
18.22

7.52
13.75

9.24
15.67

XXXVariable r corresponds to the number of cointegration relations.
While cryptocurrencies have many similarities to gold (e.g., no central supply, no
official price, and they can be mined), the correlation analysis suggests very little evidenceof co-movement - at least over our limited sample. Not surprisingly, as shown in Table 4,
we find no evidence that a cointegrating relationship exists (Johansen and Juselius, 1990;
Johansen, 1991; Dwyer, 2015). Our findings are consistent with the findings of Klein etal. (2018), see subsection 4.6.
Table 5: Onatski and Wang cointegration of gold and bitcoin, May 1, 2017 to Jun. 30,
2019.
H0
r≤1
r=0

Test statistic

10 %

5%

0.52 6.50
8.12 12.91

8.18
14.90

XXXVariable r corresponds to the number of cointegration relations.
By testing our data with a new method suitable for high-dimensional nonstationary
time series, as researched by Onatski and Chen (2018), we can underline the previous
finding of the non-existence of a cointegrating relationship as shown in Table 5.
15

4

Potential Research Areas

Cryptocurrencies, as a new type of asset, offer many research opportunities for financial
econometrics. For example, research on the dynamics of cryptocurrency trading, pricing,and volatility forecasting is advancing at a rapid pace (Briere et al., 2013; Gronwald,
2014; Cheung et al., 2015; Fry and Cheah, 2016; Chan et al., 2017; Kim et al., 2019). We
will focus on the areas of network design, sentiment, and valuation; monetary systems andfinancial development; institutions; adoption, price discovery and high-frequency data;index construction; portfolio diversification; bubbles; alternative methods to raise capital;
and the role of energy in consensus mechanisms.

4.1

Network design, sentiment, and valuation

The acceptance of this new technology has risen rapidly and activity in cryptocurrency
trading has led to the establishment of more than 200 highly fragmented, mostly unregulated cryptocurrency exchanges, which act more like broker-dealers than traditional exchanges (Hansen, 2018). There is considerable “off chain” trading. This might be intrabroker
trading matching or even dark pool trading which might lead to price jumps at exchanges(Sharma, 2018). More types of cryptocurrency are being traded in parallel, on different
exchanges, with different prices (see subsections 3.1 and 3.2.). These parallel information
sources yield dynamic high-dimensional interdependencies.
Robinson et al. (2019) introduce a “cross chain” technique which allows transactions
to be executed and their respective value to be validated across sidechains. They outline
a programming model of a swap contract for exchanging value between sidechains, anddiscuss how this technology can be readily applied to many blockchain systems to provide
cross-blockchain transactions.
A major problem for blockchain applications is the respective networks’ scalability.
Bu et al. (2019) research a distributed ledger system run by targeting algorithms that
ensure a high throughput for the transactions generated in Internet-of-Things (IoT)
systems. Transactions are continuously appended to an acyclic structure called tangle
and each new transaction selects as parents two existing transactions (called tips) that
it approves. This new metamorphic algorithm for tip selection by approving left behindtips, and improving confidence within the main tangle offers the best guaranties of bothconstructions called IOTA and its proposed improved version G-IOTA.
A number of papers study the economic incentives of current consensus methods(Huberman et al., 2017). Biais et al. (2018) model the proof-of-work blockchain protocol
as a stochastic game and analyse the equilibrium strategies of rational, strategic miners.
16

They show how forks can be generated by information delays and software upgrades, andidentify negative externalities. Easley et al. (2018) investigate the role that transaction
fees play in the evolution of bitcoin from a mining-based structure to a market-based
ecology. They develop a game-theoretic model to explain the factors leading to theemergence of transactions fees, as well as to explain the strategic behavior of miners andusers. They highlight the role of mining rewards and trading volume, and examine how
microstructure features such as exogenous structural constraints influence the dynamics
and stability of the bitcoin blockchain. Cong et al. (2018a) develop a theory of mining
pools that highlights risk sharing as a natural centralizing force.
Bhambhwani et al. (2019) research if cryptocurrencies have an intrinsic value related
to the networks’ computing power and network adoption. Their hypothesis is motivatedby the fact that miners expend real resources to generate the computing power required
to secure and operate the blockchain. An optimally performing blockchain serves asa medium for transactions and attracts users, developers, and intermediaries, thereby
leading to an increase in the cryptocurrency’s network size. They find, that there isa positive and statistically significant relationship among price, computing power, andnetwork size (adoption levels respectively), which can be used to construct asset pricing
factors.
Ong et al. (2015) use social media data and find four key variables related to themarket capitalization of a cryptocurrency: 1) merged pull requests on GitHub, 2) numberof merges, 3) number of active accounts, and 4) number of total comments. The biggest
cryptocurrencies by market capitalization experience the most activity, which is to be
expected. However, the collection of this information is unique to cryptocurrencies. In
the equity market, for example, similar information can be gleaned from third-party
sources, like analyst reports and recommendations, and perhaps news flow and conference
calls. The different sources of information available for cryptocurrencies presents newopportunities.
Research on trading patterns, herding effects, and economic decision making has
started on sentiment construction/projection and cryptocurrency-specific lexica. Natural
language processing techniques in combination with other machine learning techniques
allow researchers to build sentiment measures. Cretarola and Figa-Talamanca (2017)
propose a confidence-based model for asset and derivative prices in the bitcoin market
with prices influenced by measures linked to the confidence in the underlying technology.
Aste (2018) studies the dependency and causal structure of the current cryptocurrency
market and investigates the collective movements of both prices and social sentiment
related to almost 2,000 cryptocurrencies traded during the first six months of 2018.
17

His results uncover a complex structure of interrelations, in which prices and sentimentinfluence each other across different currencies both instantaneously and with lead–lag
relations.
Nasekin and Chen (2019) study investor sentiment on cryptocurrencies using acryptocurrency-specific lexicon proposed in Chen et al. (2018b) and statistical learning
methods. Accounting for context-specific information and word similarity by learning
word embeddings, they apply natural language processing methods for sentence-levelclassification and sentiment index construction. They argue that the constructed sentimentindices are value-relevant in terms of its return and volatility predictability for cryptocurrency market indices, see subsection 4.5. Pagnotta and Buraschi (2018) also address
the valuation of cryptocurrencies, and characterize the demand for bitcoins by the available
hashrate and show that the equilibrium price is obtained by solving a fixed-point problem.
They find, that “price/hashrate-spirals” amplify the demand and supply shocks.
Schilling and Uhlig (2018) analyze the coexistence and the competition between the
USD and bitcoin. They analyze bitcoin price evolution and interaction between the
bitcoin price and monetary policy which targets the USD, and obtain a fundamental
pricing equation, which in its simplest form implies that bitcoin prices form a martingale.

4.2

Monetary systems and financial development

Blockchain-based monetary systems hold the potential to impact the macroeconomy, as
the new payment systems challenge the traditional roles that banks have always played.
Cryptocurrencies may be viable competition for fiat currencies during periods when acentral bank is perceived as weak or untrustworthy. However, the technology behind
cryptocurrencies has the potential to improve a central banks’ operations and can serve
as a platform to launch their own cryptocurrencies (Raskin and Yermack, 2016). The
petromondea (petro) issued by the government of Venezuela is an early example of theseso-called central bank digital currencies (CBDC) (Keister and Sanches, 2018).
On June 18, 2019 Facebook announced to release a cryptocurrency on it’s own in2020 coined “Libra”. Central authorities were fast to criticize this step and governments,
including the United States, France, the United Kingdom, Germany, and Japan, expressed
their resentment and scepticism. However, the propagation of systemic banking crises
fostered by too-big-to-f ail financial institutions’ neverending propensity to take greaterrisks was a compelling reason behind the birth of cryptocurrencies over a decade ago andthe design of Libra with its governance network of 28 high-profile firms already being
in the project, looks similar to the Board of Governors of the Federal Reserve System

18

comprising the twelve Federal Reserve Banks in the United States - with the differenceof being in the hand of private actors and not a governement (Taskinsoy, 2019).
Almosova (2018) studies the economics of blockchain currency systems and the respective value competition by applying a matching function of money demand to the operationof a blockchain to observe a monetary equilibrium. With cryptocurrencies already substituting for fiat money, Hendry and Zhu (2017) model the co-existence of different typesof transactions and show that monetary authorities’ coordination capabilities are being
restricted by the use of nonregulated cryptocurrencies.
The Catalini et. al (2019) paper on a market design for a blockchain-based financial
system provides an extended abstract on a theory of long-run equilibrium in blockchainbased financial systems. Their theory elucidates the key market design features that
separate proof-of-work and proof-of-stake approaches in the long run and when each
design might each be appropriate (see subsection 2.4) and conclude, that with weakrelational contracts or substantial concerns about outside interference, proof-of-work
designs may be preferable. With regions that have local institutions that are reliable
enough to make delegation feasible, proof-of-stake designs can lead to efficiency gains
and improvements in governanc.
Cryptocurrencies may also increase financial inclusion and fuel economic activity inemerging markets, especially in sub-Saharan Africa, where only 34% of adults had a bankaccount in 2014 (Blockchain Africa Conference 2018, blockchainafrica.co; GlobalFindex,
2017; World Bank, 2017). The possibility of using this technology for inclusion of theunbanked could allow billions to join the modern world of internet commerce and spurthe creation of new businesses.

4.3

Institutions

There are hundred of cryptocurrency exchanges around the world. Some of the bestknown are: Binance, Bitfinex, Kraken, Bitstamp, Coinbase, Bitflyer, Gemini, itBit,
Bittrex and Poloniex. Each of these exchanges has its own specific traits. Kraken claims
to be the largest bitcoin exchange in EUR volume and liquidity as well as being a partnerin the first cryptocurrency bank, collaborating with the German BaFin-regulated bank
Fidor. Shapeshift, in contrast, is an exchange that allows trades without signing up
for an account. Gemini, being a fully US-regulated and licensed bitcoin and ethereumexchange, met its capital requirements by placing all USD deposits at a FDIC-insured
bank. Coinmarketcap lists 218 exchanges, but even the measurement of trading volume iscontroversial. A recent filing to the SEC (2019) argues that 95% of the trading volume in19bitcoin is fake. The research identifies 10 exchanges with actual volume (out of 81),
Binance, Bitfinex, Kraken, Bitstamp, Coinbase, Bitflyer, Gemini, itBit, Bittrex andPoloniex.
Due to the large number of exchanges with an ever increasing number of cryptocurrencies, price discrepancies due to market inefficiencies inherently exist. The low levelof regulation and sentiment driven prices make pricing discrepancies larger than in otherfinancial markets, such as fiat currency exchanges and stock exchanges. However, when
one goes outside the ten exchanges with credible volume, some of the price discrepancies
may not be real. Bistarelli et al. (2019) show via a theoretical model and via an empiricalstrategy, that arbitrage opportunities are possible by trading on different exchanges(Cretarola et al., 2017). Their approach is complementary to other theoretical studies
on bitcoin arbitrage such as Barker (2017) or Pieters and Vivanco (2015), where theresearchers study triangular arbitrage with bitcoin, i.e., buying bitcoin in USD and sellingthem in RMB.
Makarov and Schoar (2018) observe large recurrent arbitrage opportunities in cryptocurrency prices relative to fiat currencies across exchanges. These opportunities often
persist for several days or weeks, and the price dispersions exist even in the face ofsignificant trading volumes on many of the exchanges. Makarov and Schoar find thatspreads are much smaller when cryptocurrencies are traded against each other, suggestingthat cross-border controls on fiat currencies play an important role in creating the arbitrage
opportunities. By constructing a common component and an idiosyncratic component,
they conclude that the order flow plays an important role in explaining the spreads
between exchanges. Further research in regards to arbitrage in bitcoin markets is doneby Krueckeberg and Scholz (2018).
Bistarelli et al. (2018) show that cryptocurrency arbitrage strategies are profitablebecause the exchanges have different prices in the short run. Indeed, the fragmentationof exchanges is ideal for high-frequency trading bots. Bloomberg (2017) reports that
Chinese high-frequency traders have used algorithms to identify mispricings and arbitrage
opportunities across numerous exchanges in China. However, later in 2017, China banned
all cryptocurrency exchanges.
Hautsch et al. (2018) note that consensus protocols confront traders with random
waiting times until the transfer of ownership is accomplished. This settlement process
exposes arbitrageurs to price risk and imposes limits to arbitrage. They derive theoreticalarbitrage boundaries under general assumptions and show, by using high-frequency bitcoin
data, that these increase with expected latency, latency uncertainty, spot volatility, and20

risk aversion. They conclude, that settlement through decentralized systems induces
non-trivial frictions affecting market efficiency and price formation.

4.4

Adoption, price discovery and high-frequency data

Cong et al. (2018b) provide the first fundamentals-based dynamic pricing model ofcryptocurrencies and platform tokens, taking into consideration the user-base externality
and endogenous user adoption. Because the expectation of token price appreciationinduces more agents to join the platform, tokens capitalize future user adoption, generally
enhancing welfare and reducing user-base volatility (Sockin and Xiong, 2018). Cataliniand Gans (2019) show that entrepreneurs have an incentive to use subsequent productpricing choices to ensure that crypto tokens issued to fund start-up costs — a subject we
discuss in subsection 4.8 — retain their value even when they do not confer the typicalrights associated with equity.
Athey et al. (2016) develop a theoretical framework for bitcoin adoption and bitcoin
pricing. Their paper relates to a variety of broad themes in the study of information
technology adoption and usage. They conclude, that bitcoin presents a unique opportunity
to observe both the adoption and micro-level user-to-user transaction and interaction datain the context of a new information technology product, and in an environment where
the usage data is publicly available.
Ghysels and Nguyen (2018) examine price discovery and liquidity provision in thesecondary market for bitcoin and find that order informativeness generally increases
with order aggressiveness, but that this pattern reverses in the outer layers of the book.
Aggressive orders are more attractive to informed agents in a volatile market as reflectedby the increased information content of such orders. They also find that market liquidity
appears to migrate outward in response to the information asymmetry.
Griffin and Shams (2018) investigate whether tether — see subsection 2.5 — influences
bitcoin and other cryptocurrency prices, and whether the growth of a pegged cryptocurrency is primarily driven by investor demand, or is supplied to investors as a scheme toprofit from pushing cryptocurrency prices up. Their findings provide support for the viewthat price manipulation may be behind substantial distortive effects in cryptocurrencies.
Wildi and Bundi (2018), analyze momentum trading strategies, and claim that bitcoin
markets have become much more efficient markets. The impact of high-frequency trading
combined with 24/7 trading opportunities has yet to be researched. It remains tobe seen if an increase in liquidity will reduce the heretofore observed harsh swings in21

cryptocurrency prices.
The launch of bitcoin futures on both the CME as well as the CBOE (XBT) provides
opportunities to study price discovery (Karkkainen, 2018). Bitcoin futures trading gives
many institutional investors the ability to invest in bitcoin and also allows to settle
contracts in fiat money, potentially boosting liquidity. Currently the bitcoin futures
volume is approximately the same as the largest exchange, Binance. Almost all of thevolume is in the CME contract. The CBOE has announces they will no longer offer a
bitcoin futures contract.
Scaillet et al. (2018) identify high-frequency jump components in the bitcoin market
and link them to new information arrival over time. Guo et al. (2018) perform a spectralclustering analysis of dynamic return-based network structures with coin attributions.
This latent group structure in the cryptocurrency market leads them to conclude thatcomovements are influenced by the type of algorithm used. Makarov and Schoar (2018)
study price deviations across cryptocurrency exchanges and interpret the deviations as
the result of a balance between idiosyncratic sentiments of noise traders and the efforts
of arbitrageurs to equilibrate prices across exchanges.

4.5

Index construction

Index construction poses unique challenges when it comes to cryptocurrencies. Traditional
indices, such as the S&P 500 or Russell 3000, gather data from stocks that are tradedover particular time intervals in a small numbers of venues. Cryptocurrencies are traded24/7 on hundreds of venues, like WorldCoinIndex, CoinMarketCap, CryptoCompare,
CryptoCurrencyIndex30, or the CME CF Cryptocurrency Indices. Preliminary researchon index construction is made, for example, by Trimborn and Härdle (2018), Chen et al.
(2018a) and Kim et al. (2019).
While there are many exchanges, liquidity widely varies. Hence, the first challenge iswhat price should be used for individual cryptocurrencies. Even the original two bitcoin
futures contracts (CME and CBOE) use different data sources for the price of bitcoin.
Indeed, bitcoin is the most liquid cryptocurrency and there is no agreement on the “spot”
price. The difficulty in establishing a price and the possibility of price manipulation on
certain exchanges has lead the SEC to block the creation of cryptocurrency ETFs.
Kim et al. (2019) have set the goal of capturing the expectations on the cryptocurrency
market (represented by CRIX) through the construction of an implied volatility proxy inabsence of the derivatives for the majority of cryptocurrencies. The “fear index” VIX of

22

the United States stock market was selected as a guidance. Analysis of the relationships
between VIX and volatility of the underlying assets provide an insight for the selection ofa respective proxy. The established VCRIX index provides a daily forecast for the meanannualized volatility of the next 30 days.
There are other issues that provide a challenge in index construction such as forking.
When are forked cryptocurrencies added to the index? If the index focuses on large
capitalized cryptocurrencies, should a smaller capitalized fork be included? Forks are a
new concept that poses a challenge to financial engineering. The forking problem is alsoa challenge for the single currency futures contracts.

4.6

Portfolio diversification

For millenia, gold has been an accepted store and measure of value, offering very longterm stability and security in the financial marketplace (Erb and Harvey, 2013). Bitcoin
and gold are similar from both a psychological perspective and, especially, as a resource.
Neither can be created arbitrarily: each must be mined and each has a finite supply (atleast on planet Earth). That said, gold has fundamental value when used for jewelry andart as well as electronic or medical components. The limited supply of “digital gold”,
combined with the market’s current acceptance of it, suggests that bitcoin and othercryptocurrencies may be able to serve a similar role as gold. Klein et al. (2018) showthat the volatility dynamics of cryptocurrencies do share some similarities with those ofgold and silver.
Gkillas and Longin (2018) argue that bitcoin is the new digital gold and they investigate
the potential benefits of bitcoin during extremely volatile market periods. They findthat the correlation of extreme returns between bitcoin and US and European equity
markets increases during stock market drawdowns and decreases during stock market
booms. Their conclusion is that bitcoin can play an important role in asset management
and provide similar results as those of gold. Furthermore, Gkillas and Longin find alow extreme correlation between bitcoin and gold, implying that the assets can be used
together in turbulent times. That said, we suggest caution in interpreting these results
given the very limited data.
Petukhina et al. (2018) find that due to the volatility structure of cryptocurrencies,
the application of traditional risk-based portfolios — such as equal-risk contribution,
minimum-variance and minimum-CVaR portfolios — does not boost the performance ofinvestments significantly. Liu et al. (2019a) examine common risk factors in cryptocurrencies, and capture the cross-sectional expected cryptocurrency returns. By con-23

sidering a comprehensive list of price- and market-related factors in the stock market,
they construct cryptocurrency counterparts. Their cryptocurrency factors claim to be
successful long-short strategies that generate sizable and statistically significant excess
returns. The paper thus establishes a set of stylized facts on the cross-section of cryptocurrencies that can be used to assess and develop theoretical models.

4.7

Bubbles

Chaim and Laurini (2019) analyze daily returns of bitcoin between January 2015 andMarch 2018 to empirically investigate the price bubble hypothesis. Bitcoin returns havecharacteristics one would expect of a bubble: it is very volatile, exhibits large kurtosis,
and negative skewness (Camerer, 1989). By following previous research, they conclude
that bitcoin-USD prices being a bubble is plausible, but the evidence is inconclusive.
In contrast, Henry and Irrera (2017) argue that cryptocurrencies exhibit bubblelike behavior. Recent research by Hafner (2018) extends traditional bubble tests tothe case of time-varying volatility. Dong et al. (2018) investigate the positive andnegative outcomes of a cryptocurrency model as risky and costly bubbles in an infinitehorizon production economy with incomplete markets that has the following framework
for bitcoin: 1) enormous volatility, 2) price dynamics are significantly sensitive to bothinvestor sentiment and policy stances, and 3) the market exhibits diverse cyclical features
for US and China. Their quantitative results, however, rely heavily on the severity of themarket distortion, i.e. the intervention in the given market by a governing body, which,in turn, determines the size of the bitcoin bubbles.
Shu and Zhu (2019) employ the log-periodic power law singularity (LPPLS) confidence
indicator as a diagnostic tool for identifying bubbles using the daily data on bitcoin price.
The LPPLS confidence indicator fails to provide effective warnings for detecting the
bubbles when the bitcoin price suffers from a large fluctuation in a short time, especiallyfor positive bubbles. In order to diagnose the existence of bubbles and accurately
predict the bubble crashes in the cryptocurrency market, their research proposes anadaptive multilevel time series detection methodology based on the LPPLS model andhigh frequency data, which effectively detects bubbles and accurately forecasts bubble
burts. On a day to week scale, the LPPLS confidence indicator has a stable performancein terms of effectively monitoring the bubble status on a longer time scale - on a week
to month scale. Their adaptive multilevel time series detection methodology claims toprovide real-time detection of bubbles and advanced forecast of crashes to warn of theimminent risk.

24

4.8

Alternative methods to raise capitalThe year 2017 brought a surge in initial coin offerings (ICOs), similar to initial public
offerings (SEC-approved stock offerings). ICO’s are a potentially new financing channel
for entrepreneurs (Cong and He, 2017). The space has also generated a lot of attention
because some investors are buying into ICOs without fully understanding the technology
as well as some companies are offering an ICO without an economically meaningful use
case for the cryptocurrency (Ernst & Young, 2017; Amsden and Schweitzer, 2018).
Indeed, cryptocurrencies hold the potential to significantly reduce cost, complexity,
and simultaneously increase the speed of trading and settlement processes in a securemanner. Cryptocurrencies are tokens, but other assets such as shares of a company cansimilarly be tokenized and traded.
In summary, 329 ICOs out of 2027 ICOs listed on tokendata.io have failed (16.23%).
Extensive research is maintained regarding ICOs (Santo et al., 2016; Bajpai, 2017; SEC,
2017a, 2017b; Adhami et al., 2018; Momtaz, 2018; Kostovetsky, 2018; Guegan and Henot,
2018; Howell et al., 2018; Liu et al., 2019b). In the sample used by Bourveau et al. (2018)
approximately 85% of ICOs are successful.
Basic alternatives to traditional banking services that have a cryptocurrency backbone
are being researched as well. Panwar et al. (2019) research a blockchain-based credit
network where credit transfer between a sender-receiver pair happens on demand. Distributed credit networks (DCNs) are distributed systems of trust between users, wherea user extends financial credit, or guarantees assets to other users whom it deems credit
worthy, with the extended credit proportionate to the amount of trust that exists between
the users — essentially peer-to-peer lending networks, where users extend credit, borrowmoney and commodities from each other directly, while minimizing the role of banks,
clearing-houses, or bourses. They present preliminary experiments and scalability analyses
based on their proposed DCN framework.

4.9

The role of energy in consensus mechanisms

As we have detailed, there are many different consensus mechanisms. Bitcoin uses aparticularly energy-intensive method, which raises environmental concerns, especiallywith the prevalence of bitcoin mining dependent on coal-fired power plants in China(Hileman and Rauchs, 2017). Cong et al. (2018a) show that mining pools, as a financial
innovation, significantly exacerbate energy consumption for proof-of-work-based blockchains in their research output regarding decentralized mining in centralized pools. As of
April 2018, aggregate energy devoted to bitcoin mining alone exceeded 60 TWh, roughly
25

the annual energy consumed by Switzerland as a country (Lee, 2018). Mishra et al.
(2018) investigate how the mining protocol of bitcoin impacts the computing capacity
needs of miners and demonstrate, that the mining algorithm as well as the transaction
volume increase computing resource needs, which in turn raises the energy consumption.
Eventually they argue resource requirements both from a computing hardware and energy
consumption needs that the future growth of the bitcoin network and the use of bitcoin
as a currency could be questionable.
As the annual electricity consumption for cryptocurrency mining is growing yearly.
Total carbon production from mining now likely exceeds that generated by the entire
nation of Portugal. Corbet et al. (2019) investigate how Bitcoin’s price volatility and theunderlying dynamics of cryptocurrency’s mining characteristics affect the energy markets,
utilities companies, and green ETFs. The results claim that continued cryptocurrency
energy-usage impacts the performance of energy sector, which emphasises the importanceof further assessment of environmental impacts of cryptocurrency growth. Blockchain
technology offers a number of innovative environment-related research opportunities(Hayes, 2017; Pop et al., 2018).

5

Closing Remarks

Cryptocurrencies are an intriguing financial innovation and offer many possible research
avenues. As with many new technologies, considerable confusion exists about both theunderlying concept of cryptocurrencies and the approaches for valuing them.
Our first goal in this paper is to provide a high level understanding of the blockchain
technology behind the cryptocurrencies. Second, we want to emphasize that there aremany different classes of cryptocurrencies — too often cryptocurrency is summarized
as bitcoin. Cryptocurrencies vary, however, and can be tokens representing shares of
traditional assets, provide direct utility such as computational power, and even represent
a fiat currency.
Finally, we would like to emphasize the large number of research avenues available inthe cryptocurrency space. In 2018, we witnessed the bursting of a bubble in the mostliquid cryptocurrencies, but the research opportunities go well beyond bubbles. There ismuch to do in this new field of finance and economics.

26

6

References

Abadi, J., and M. Brunnermeier. 2018. “Blockchain Economics”. Princeton University. Retrieved on
the 24.01.2019 from https://scholar.princeton.edu/sites/default/files/markus/files/blockchain_paper_v3g.pdf.
Abraham, I., D. Malkhi, K. Nayak, L. Ren, and A. Spiegelman. 2016. “Solidus: An
incentive-compatible cryptocurrency based on permissionless Byzantine consensus.” Retrieved on the24.11.2018 from https://arxiv.org/abs/1612.02916.
Adhami, S., G. Giudici, and S. Martinazzi 2018. “Why Do Businesses Go Crypto? An Empirical
Analysis of Initial Coin Offerings.” Journal of Economics and Business. Forthcoming.
Almosova, A. 2018. “A Monetary Model of Blockchain.” IRTG 1792 DP 2018-008. Retrieved on the12.02.2018 from https://www.wiwi.hu-berlin.de/de/forschung/irtg/results/discussionpapers/discussion-papers-2017-1/irtg1792dp2018-008.pdf.
Ametrano, F. 2016. “Hayek money: The cryptocurrency price stability solution.” Retrieved on the24.11.2018 from http://dx.doi.org/10.2139/ssrn.2425270.
Amsden, R., and D. Schweizer 2018. “Are Blockchain Crowdsales the New ’Gold Rush’ ? Success
Determinants of Initial Coin Offerings.” Retrieved on the 12.10.2018 fromhttps://papers.ssrn.com/sol3/papers.cfm?abstract_id=3163849
Athey, S., Parashkevov, I., Sarukkai, V., and J. Xia. 2016. “Bitcoin Pricing, Adoption, and Usage:
Theory and Evidence”. Stanford University. Retrieved on the 11.03.2019 fromhttps://siepr.stanford.edu/sites/default/files/publications/17-033_1.pdf.
Aranda, D., and R. Zagone. 2015. “The ‘Ripple’ Effect: Why an Open Payments Infrastructure
Matters.” Consultative Group to Assist the Poor. Retrieved on the 26.11.2018 fromhttp://www.cgap.org/blog/%E2%80%98ripple%E2%80%99-effect-why-open-paymentsinfrastructure-matters.
Arnold, M. 2018. “Ripple and Swift slug it out over cross-border payments.” Financial Times.
Retrieved on the 25.08.2018 fromhttps://www.ft.com/content/631af8cc-47cc-11e8-8c77-ff51caedcde6.
Aste, T. 2018. “Cryptocurrency market structure: connecting emotions and economics. Special Issue of
Digital Finance on Cryptocurrencies.” Digital Finance. Smart Data Analytics, Investment Innovation,
and Financial Technology. ISSN 2524-6186.
Babich, V., and G. Hilary. 2018a. “Blockchain and Other Distributed Ledger Technologies in
Operations”. SSRN. Retrieved on the 15.01.2019 fromhttps://papers.ssrn.com/sol3/papers.cfm?abstract_id=3232977.
Babich, V., and G. Hilary. 2018b. “Distributed Ledgers and Operations: What Operations
Management Researchers Should Know about Blockchain Technology.” Georgetown McDonough Schoolof Business Research Paper No. 3131250.
Back, A. 2002. “Hashcash - A Denial of Service Counter-Measure.” Retrieved on the 24.12.2018 fromhttp://www.hashcash.org/papers/hashcash.pdf.
Bajpai, P. 2008. Bitcoin: A peer-to-peer electronic cash system. Retrieved on the 24.11.2018 fromhttp://www.bitcoin.org/bitcoin.pdf.
Barker, J. 2017. “Triangular arbitrage with Bitcoin.” Texas A&M University. Retrieved on the27

24.02.2019 from https://oaktrust.library.tamu.edu/bitstream/handle/1969.1/164589/BARKERDOCUMENT-2017.pdf?sequence=1&isAllowed=y.
Bartos, J. 2015. “Does bitcoin Follow the Hypothesis of Efficient Market?” International Journal of
Economic Sciences, IV, 2, 10–23.
Bech, M., and R. Garratt. 2017. “Central bank cryptocurrencies”. BIS Quarterly Review. Volume 55,
September 2017.
Benedetti, H., and L. Kostovetsky. 2018. “Digital Tulips? Returns to Investors in Initial Coin
Offerings”. SSRN. Retrieved on the 16.02.2019 fromhttps://papers.ssrn.com/sol3/papers.cfm?abstract_id=3182169.
Biais, B., Bisière, Ch., Bouvard, M., and C. Casamatta. 2018. “The blockchain folk theorem”. SSRN.
Retrieved on the 08.01.2019 fromhttps://papers.ssrn.com/sol3/papers.cfm?abstract_id=3108601.
Bistarelli, S., A. Cretarola, G. Figa-Talamanca, and M. Patacca. 2018. “Model-based arbitrage in
multi-exchange models for bitcoin price dynamics.” Special Issue of Digital Finance on
Cryptocurrencies. Digital Finance. Smart Data Analytics, Investment Innovation, and Financial
Technology. ISSN 2524-6186.
Bistarelli, S., A. Cretarola, G. Figà-Talamanca, I. Mercanti, and M. Patacca. 2019. “Is Arbitrage
Possible in the Bitcoin Market?” (Work-In-Progress Paper). In: M. Coppola, E. Carlini, D.
D’Agostino, J. Altmann, and J. Bañares (eds) Economics of Grids, Clouds, Systems, and Services.
GECON 2018. Lecture Notes in Computer Science, vol 11113. Springer, Cham.
Bloomberg. 2017. “High-Speed Traders Are Taking Over bitcoin.” Bloomberg. Retrieved on the
16.02.2018 fromhttps://www.bloomberg.com/news/articles/2017-01-16/high-speed-traders-are-takingover-bitcoin-as-easy-money-beckons.
Böhme, R., N. Christin, B. Edelman, and T. Moore. 2015. “bitcoin: Economics, technology, andgovernance.” Journal of Economic Perspectives, 29(2), pp.213-38.
Bordo, M., Levin, A. 2017. “Central Bank Digital Currency and the Future of Monetary Policy”.
National Bureau of Economic Research. NBER Working Paper No. 23711.
Borke, L., and W. Härdle 2018. “Q3-D3-LSA.” In Härdle, W., Lu, H., Shen, X. (Eds.). Handbook of
Big data Analytics. Springer-Verlag Berlin Heidelberg. ISBN 978-3-319-18284-1, DOI:
10.1007/978-3-319-18284-1.
Bourveau, T., De George, E., Ellahie, A., and D. Macciocchi. 2018. Initial Coin Offerings: Early
Evidence on the Role of Disclosure in the Unregulated Crypto Market. SSRN. Retrieved on the24.11.2018 from https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3193392.
Briere, M., K. Oosterlinck, and A. Szafarz. 2013. “Virtual Currency, Tangible Return: Portfolio
Diversification with bitcoins.” Retrieved on the 24.11.2018 from:https://econpapers.repec.org/paper/solwpaper/2013_2f149159.htm.
Bu, G., Hana, W., and M. Potop-Butucaru. 2019. “Metamorphic IOTA.” Retrieved on the 18.06.2019
from https://arxiv.org/pdf/1907.03628.pdf.
Bundesbank. 2017. “Monatsbericht: Bundesbank sieht Vorteile in Blockchain-Technologie.” Deutsche
Bundesbank. Retrieved on the 14.12.2018 from28https://www.bundesbank.de/Redaktion/DE/Themen/2017/2017_09_18_monatsbericht_dlt.html/.
Buterin, V. 2014. “On Stake. Ethereum Blog.” Retrieved on the 24.11.2018 fromhttps://blog.ethereum.org/2014/07/05/stake/.
Bhambhwani, S., Delikouras, S. and G. Korniotis. 2019. “Do Fundamentals Drive Cryptocurrency
Prices?” SSRN. Retrieved on the 30.07.2019 from https://ssrn.com/abstract=3342842.
Camerer, C. 1989. “Bubbles and fads in asset prices.” Journal of Economic Surveys 3 (1): 3–41. DOI:
https://doi.org/10.1111/j.1467-6419.1989.tb00056.x.
Catalini, Ch., and J. Gans. 2016. “Some Simple Economics of the Blockchain.” MIT Sloan Research
Paper No. 5191-16. Retrieved on the 12.05.2018 from http://dx.doi.org/10.2139/ssrn.2874598.
Catalini, Ch., Jagadeesan, R., and S. Duke Kominers. 2019. “ Market Design for a Blockchain-Based
Financial System.” SSRN. Retrieved on the 10.07.2019 fromhttps://papers.ssrn.com/sol3/papers.cfm?abstract_id=3396834.
Catalini, Ch., and J. Gans. 2019. “Initial Coin Offerings and the Value of Crypto Tokens.” MIT Sloan
Research Paper No. 5347-18. Retrieved on the 11.03.2019 fromhttp://dx.doi.org/10.2139/ssrn.3137213.
Chan, S., J. Chu, S. Nadarajah, and J. Osterrieder. 2017. “A Statistical Analysis of Cryptocurrencies.”
Journal of Risk and Financial Management, 2017, 10(2), 12.
Chaim, P., and M. Laurini. 2019. “Is Bitcoin a bubble?” Physica A: Statistical Mechanics and its
Applications. Volume 517, 1 March 2019, Pages 222-232. DOI:
https://doi.org/10.1016/j.physa.2018.11.031.
Chaum, D. 1983. “Blind signatures for untraceable payments.” Advances in Cryptology Proceedings ofCrypto. 82 (3): 199–203. doi:10.1007/978-1-4757-0602-4 18.
Cheung, A., E. Roca, and J. Su. 2015. “Crypto-currency bubbles: an application of the Phillips–Shi–Yu(2013) methodology on Mt. Gox bitcoin prices.” Applied Economics, 47(23), 2348-2358.
Chen, C. YH., W. Härdle, A. Hou, and W. Wang. 2018a. “Pricing Cryptocurrency Options: The Caseof CRIX and Bitcoin”. Journal of Financial Economics. Revised and Resubmitted.
Chen, C. YH., R. Després, L. Guo, and T. Renault. 2018b. “What makes cryptocurrencies special?
Investor sentiment and price predictability in the absence of fundamental value.” IRTG 1792
Discussion Paper. ISSN: 2568-5619. Forthcoming.
Cretarola, A., G. Figa-Talamanca. 2017. “A Confidence-Based Model for Asset and Derivative Prices
in the bitcoin Market.” Retrieved on the 24.01.2018 from http://dx.doi.org/10.2139/ssrn.2908921.
Cretarola, A., Figà-Talamanca, G., and M. Patacca. 2017. “A Sentiment-Based Model for the Bitcoin:
Theory, Estimation and Option Pricing.” SSRN. Retrieved on the 19.03.2019 fromhttps://papers.ssrn.com/sol3/papers.cfm?abstract_id=3042029.
Cong, L., and He, Z. 2017. “Blockchain Disruption and Smart Contracts”. SSRN. Retrieved on the24.11.2018 from https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2985764.
Cong, L., He, Z., and J. Li. 2018a. “Decentralized Mining in Centralized Pools”. SSRN. Retrieved on
the 24.11.2018 from https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3143724.
Cong, L., Li, Y., and N. Wang. 2018b. “Tokenomics: Dynamic Adoption and Valuation ”. SSRN.
Retrieved on the 24.02.2019 from29https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3153860.
Corbet, S., Lucey, B., and L. Yarovaya. 2019. “The Financial Market Effects of Cryptocurrency
Energy Usage”. SSRN. Retrieved on the 24.06.2019 fromhttps://papers.ssrn.com/sol3/papers.cfm?abstract_id=3412194.
Cotillard, M. 2015. “bitcoin’s Block Size Debate Tests Its Community Governance.” Brave New Coin.
18 August. Retrieved on the 24.11.2018 from https://bravenewcoin.com/news/bitcoins-blocksize-debate-tests-its-community-governance/.
Delmolino, K., M. Arnett, A. Kosba, A. Miller, and E. Shi. 2016. “Step by step towards creating a safe
smart contract: Lessons and insights from a cryptocurrency lab.” Retrieved on the 24.11.2018 fromhttps://eprint.iacr.org/2015/460.pdf.
Derose, C. 2015. “Blockchain for Beginners - Behind the Ingenious Security Feature that Powers theBlockchain.” American Banker. Retrieved on the 24.11.2018 fromhttps://www.americanbanker.com/opinion/behind-the-ingenious-security-feature-thatpowers-the-blockchain.
Dong, F., Z. Xu, and Y. Zhang. 2018. “Bubbly bitcoin.” SSRN. Retrieved on the 07.01.2019 fromhttps://papers.ssrn.com/sol3/papers.cfm?abstract_id=3290125.
Dwork, C., and M. Naor. 1992. “Pricing via Processing or Combatting Junk Mail.” Annual
International Cryptology Conference. CRYPTO 1992: Advances in Cryptology. CRYPTO 92, pp.
139-147.
Dwyer, G. 2015. “The Johansen Tests for Cointegration.” Retrieved on the 24.02.2018 fromhttp://www.jerrydwyer.com/pdf/Clemson/Cointegration.pdf.
Easley, D., O’Hara, M, and S. Basu. 2018. “From Mining to Markets: The Evolution of Bitcoin
Transaction Fees”. Journal of Financial Economics (JFE). Forthcoming.
Erb, C., and C. Harvey. 2013. “The Golden Dilemma.” Financial Analysts Journal, 2013:69:4,
July-August, pp. 10-42.
Ernst & Young. 2017. “EY Research: Initial Coin Offerings (ICOs).” Retrieved on the 12.12.2018 fromhttps://www.ey.com/Publication/vwLUAssets/ey-research-initial-coin-offeringsicos/$File$/ey-research-initial-coin-offerings-icos.pdf.
Franco, P. 2015. “Understanding bitcoin.” Chichester, West Sussex: John Wiley & Sons Ltd.
Foley, S., Karlsen, J., and T. Putnins. 2018. “Sex, Drugs, and Bitcoin: How Much Illegal Activity Is
Financed Through Cryptocurrencies?”. Review of Financial Studies, Forthcoming.
Fry, J., and E. Cheah. 2016. “Negative bubbles and shocks in cryptocurrency markets.” International
Review of Financial Analysis, 47, 343-352.
Ghysels, E., and G. Nguyen. 2018. “Price Discovery of a Speculative Asset: Evidence from a bitcoin
Exchange.” SSRN. Retrieved on the 15.10.2018 fromhttps://papers.ssrn.com/sol3/papers.cfm?abstract_id=3258508.
Gkillas, K., and F. Longin. 2018. “Is bitcoin the New Digital Gold? Evidence From Extreme Price
Movements in Financial Markets.” SSRN. Retrieved on the 15.10.2018 fromhttps://papers.ssrn.com/sol3/papers.cfm?abstract_id=3245571.
GlobalFindex. 2017. “The Global Findex Database 2017.” The World Bank. Retrieved on the05.06.2018 from https://globalfindex.worldbank.org.

30

Gronwald, M. 2014. “The Economics of bitcoins - Market Characteristics and Price Jumps.” CESifo
Working Paper, No. 5121.
Griffin, J., and A. Shams. 2018. “Is Bitcoin Really Un-Tethered?”. SSRN. Retrieved on the 11.03.2019
from https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3195066.
Guegan, D., and C. Henot 2018. A Probative Value for Authentication Use Case Blockchain. Special
Issue of Digital Finance on Cryptocurrencies. Digital Finance. Smart Data Analytics, Investment
Innovation, and Financial Technology. ISSN 2524-6186. Submitted.
Guo, L., and X. Li. 2017. “Risk Analysis of Cryptocurrency as an Alternative Asset Class.” In Härdle,
W., Chen, C. YH., Overbeck, L. (Eds.). Applied Quantitative Finance. Third edition. Springer-Verlag
Berlin Heidelberg. ISBN 978-3-662-54485-3, e-ISBN 978-3-662-54486-0,
DOI:10.1007/978-3-662-54486-0, pp. 309-329.
Guo, L., Y. Tao, and W. Härdle. 2018. “A Dynamic Network for Cryptocurrencies.” Journal of theAmerican Statistical Association. Submitted.
Haber, S., and W. Stornetta. 1991. “How to Time-Stamp a Digital Document.” Journal of Cryptology.
Volume 3, Issue 2, pp. 99–111
Hafner, Ch. 2018. “Testing for Bubbles in Cryptocurrencies with Time-Varying Volatility.” SSRN.
Retrieved on the 15.10.2018 fromhttps://papers.ssrn.com/sol3/papers.cfm?abstract_id=3105251
Hansen, S. 2018. “Guide To Top Cryptocurrency Exchanges.” Forbes. Retrieved on the 15.08.2018
from https://www.forbes.com/sites/sarahhansen/2018/06/20/forbes-guide-tocryptocurrency-exchanges/#503cf5ce2572
Harvey, C. 2014. “Bitcoin Myths and Facts.” Retrieved on the 26.11.2018 fromhttps://ssrn.com/abstract=2479670
Harvey, C. 2016. “Cryptofinance.” Retrieved on the 9.02.2018 fromhttps://papers.ssrn.com/sol3/papers.cfm?abstract_id=2438299
Harvey, C. 2017a. “Breaking Down bitcoin.” Retrieved on the 26.05.2018 fromhttps://www.fuqua.duke.edu/duke-fuqua-insights/breaking-down-bitcoin-%E2%80%93professor-campbell-harvey-digital-currency%E2%80%99s-prospects
Harvey, C. 2017b. “Blockchain 2.0.” Global Risk Institute. Summit 2017. Retrieved on the 12.03.2018
from http://globalriskinstitute.org/wp-content/uploads/2017/10/Campbell-HarveyBlockchain-2.0.pdf
Hautsch, N., Scheuch, Ch., and S. Voigt. 2018. “Limits to Arbitrage in Markets With Stochastic
Settlement Latency.” CFS Working Paper, No. 616, 2018.
Hayes, A. 2017. “Cryptocurrency Value Formation: An empirical study leading to a cost of production
model for valuing Bitcoin.” Telematics and Informatics. 34. 1308-1321. 10.1016/j.tele.2016.05.005.
Hendry, S., and Y. Zhu. 2017. “A Framework for Analyzing Monetary Policy in an Economy with
Emoney.” Bank of Canada. Retrieved on the 12.11.2018 from https://editorialexpress.com/cgibin/conference/download.cgi?db_name=mmmspr2017&paper_id=104
Henry, D. and A. Irrera. 2017. “JPMorgan’s Dimon says bitcoin ’is a fraud’.” Retrieved on the15.12.2018 from https://www.reuters.com/article/legal-us-usa-banks-conferencejpmorgan/jpmorgans-dimon-says-bitcoin-is-a-fraud-idUSKCN1BN2PN

31

Hileman, G., and M. Rauchs. 2017. “Global Cryptocurrency Benchmarking Study”. Retrieved on the06.01.2018 fromhttps://www.jbs.cam.ac.uk/fileadmin/user_upload/research/centres/alternativefinance/downloads/2017-global-cryptocurrency-benchmarking-study.pdf.
Howell, S., Niessner, M., and D. Yermack. 2018. “Initial Coin Offerings: Financing Growth with
Cryptocurrency Token Sales”. European Corporate Governance Institute (ECGI) - Finance Working
Paper No. 564/2018.
Hu, A., Parlour, C., and U. Rajan. 2018. “Cryptocurrencies: Stylized Facts on a New Investible
Instrument”. SSRN. Retrieved on the 06.03.2019 fromhttps://papers.ssrn.com/sol3/papers.cfm?abstract_id=3182113.
Huberman, G., Leshno, J., and C. Moallemi. 2017. “Monopoly Without a Monopolist: An Economic
Analysis of the Bitcoin Payment System”. Bank of Finland Research Discussion Paper No. 27/2017.
Iwamura, M., Y. Kitamura, and T. Matsumoto. 2014. “Is bitcoin the Only Cryptocurrency in the
Town?” Economics of Cryptocurrency and Friedrich A. Hayek. Retrieved on the 24.11.2018 fromhttps://econpapers.repec.org/paper/hithituec/602.htm.
Jakobsson, M., and A. Juels. 1999. “Proofs of Work and Bread Pudding Protocols. Communications
and Multimedia Security.” Kluwer Academic Publishers: 258–272.
Johansen, S., and K. Juselius. 1990. “Maxium likelihood estimation and inference on cointegration with applications to the demand for money.” Oxford Bulletin of economics and statistics. Vol. 52, Issue2, pp. 169-210.
Johansen, S. 1991. “Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector
Autoregressive Models.” Econometrica 59: 1551–1580.
Karkkainen, T. 2018. “Price Discovery in the bitcoin Futures and Cash Markets.” SSRN. Retrieved on
the 15.10.2018 from https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3243969.
Keister, T.,and D. Sanches. 2018. “Managing Aggregate Liquidity: The Role of a Central Bank Digital
Currency.” Semanticscholar. Retrieved on the 15.11.2018 fromhttps://www.semanticscholar.org/paper/Managing-Aggregate-Liquidity-%3A-The-Role-of-aBank-%E2%88%97-Keister-Sanches/feb3a1b19ea9b7915d5ede69fc75d77e5bf81d99.
Kim, A., S. Trimborn, and W. Härdle. 2019. “VCRIX - volatility index for crypto-currencies on thebasis of CRIX.” Forthcoming.
King, S., and S. Nadal. 2012. “PPCoin: Peer-to-Peer Crypto-Currency with Proof-of-Stake.” Retrievedon the 24.11.2018 from https://decred.org/research/king2012.pdf.
Klein, T., P. Hien, and T. Walther. 2018. “bitcoin Is Not the New Gold: A Comparison of Volatility,
Correlation, and Portfolio Performance.” Retrieved on the 25.03.2018 fromhttp://dx.doi.org/10.2139/ssrn.3146845.
Krueckeberg, S., and P. Scholz. 2018. “Decentralized Efficiency? Arbitrage in bitcoin Markets”.
http://dx.doi.org/10.2139/ssrn.3292127.
Lamport, L., R. Shostak, M. Pease. 1982. “The Byzantine Generals Problem.” ACM Transactions on
Programming Languages and Systems. 4 (3): 382–401. doi:10.1145/357172.357176.
Lee, S. 2018. “Bitcoin’s Energy Consumption Can Power An Entire Country – But EOS Is Trying To
Fix That.” Forbes. Retrieved on the 14.01.2019 from32

https://www.forbes.com/sites/shermanlee/2018/04/19/bitcoins-energy-consumption-canpower-an-entire-country-but-eos-is-trying-to-fix-that/#79121fd01bc8.
Liu, Y., Tsyvinski, A., and X. Wu. 2019a. “Common Risk Factors in Cryptocurrency.” SSRN.
Retrieved on the 15.07.2019 fromhttps://papers.ssrn.com/sol3/papers.cfm?abstract_id=3379131.
Liu, Y., Sheng, J, and W. Wang. 2019b. “How Much Tech in FinTech? Evidence from Initial Coin
Offerings.” Working Paper. University of California Irvine.
Makarov, I., and A. Schoar. 2018. “Trading and Arbitrage in Cryptocurrency Markets.” The Journalof Financial Economics. Forthcoming.
MAS. 2017. “Project Ubin: Central Bank Digital Money using Distributed Ledger Technology.”
Monetary Authority of Singapore. Retrieved on the 14.12.2018 from http://www.mas.gov.sg/Singapore-Financial-Centre/Smart-Financial-Centre/Project-Ubin.aspx.
Mishra, S., V. Jacob, and S. Radhakrishnan. 2018. “Energy Consumption – Bitcoin’s Achilles Heel.”
Retrieved on the 14.01.2019 fromhttps://ssrn.com/abstract=3076734 or http://dx.doi.org/10.2139/ssrn.3076734.
Momtaz, P. 2018. “Initial Coin Offerings.” SSRN. Retrieved on the 12.12.2018 fromhttps://ssrn.com/abstract=3166709.
Nasekin, S., and C. YH. Chen. 2019. “Deep Learning-Based Cryptocurrency Sentiment Construction.”
Digital Finance. Smart Data Analytics, Investment Innovation, and Financial Technology. ISSN
2524-6186. Forthcoming.
Nakamoto, S. 2008. “Bitcoin: A Peer-to-Peer Electronic Cash System.” Retrieved on the 14.12.2018
from https://bitcoin.org/bitcoin.pdf.
Onatski, A., and C. Wang. 2018. “Alternative Asymptotics for Cointegration Tests in Large VARs.”
Econometrica, Volume 86, Issue 4. P.p 1465-1478. https://doi.org/10.3982/ECTA14649.
Ong, B., L. Guo, and K. Lee. 2015. “Evaluating the Potential of Alternative Cryptocurrencies.”
Elsevier, Academic Press. Pp. 81–135.
Paar, C., and J. Pelzl. 2010. “Understanding Cryptography.” Berlin: Springer-Verlag.
Panwar, G., Misra, S., and R. Vishwanathan. 2019. “BlAnC: Blockchain-based Anonymous andDecentralized Credit Networks.” Cryptology ePrint Archive: Report 2019/014. DOI:
10.1145/3292006.3300034.
Pagnotto, E., and A. Buraschi. 2018. “An Equilibrium Valuation of Bitcoin and Decentralized Network
Assets.” SSRN. Retrieved on the 20.01.2019 fromhttps://papers.ssrn.com/sol3/papers.cfm?abstract_id=3142022.
Park, S., K. Pietrzak, J. Alwen, G. Fuchsbauer, and P. Gazi. 2015. “Spacecoin: A cryptocurrency
based on proofs of space.” Cryptology ePrint Archive. Retrieved on the 24.11.2018 fromhttps://eprint.iacr.org/2015/528/20150623:221402.
Petukhina, A., Trimborn, S., Härdle, W., and H. Elendner. 2018. “Investing With Cryptocurrencies –Evaluating the Potential of Portfolio Allocation Strategies”. SSRN. Retrieved on the 12.01.2019 fromhttps://papers.ssrn.com/sol3/papers.cfm?abstract_id=3274193.
Pieters, G., and S. Vivianco. 2015. “Bitcoin arbitrage and unofficial exchange rates.” Trinity
University. Retrieved on the 10.03.2019 from33

http://www.ginapieters.com/uploads/1/0/2/8/10288430/bitcoin.pdf.
Pop, C., T. Cioara, M. Antal, I. Anghel, I. Salomie, and M. Bertoncini. 2018. “Blockchain Based
Decentralized Management of Demand Response Programs in Smart Energy Grids.” MDPI. Sensors
2018, 18, 162; doi:10.3390/s18010162.
Raskin, M., and D. Yermack. 2016. “Digital Currencies, Decentralized Ledgers, and the Future ofCentral Banking”. National Bureau of Economic Research. NBER Working Paper No. 22238.
Robinson, P., Hyland-Wood, D., Saltini, R., Johnson, S., and J. Brainard. 2019. “Atomic Crosschain
Transactions for Ethereum Private Sidechains.” Retrieved on the 12.06.2019 fromhttps://arxiv.org/pdf/1904.12079.pdf.
Santo, A., Minowa, I., Hosaka, G., Hayakawa, S., Kondo, M., Ichiki, S., and Y. Kaneko. 2016.
Applicability of Distributed Ledger Technology to Capital Market Infrastructure. JPX Working Paper.
Japan Exchange Group. Retrieved on the 12.12.2018 fromhttps://www.cnbc.com/2017/10/13/bitcoin-get-serious-about-digital-currency-imfchristine-lagarde-says.html.
SARB. 2018. “FinTech Programme - Media statement, Tuesday, 13 February 2018.” South African
Reserve Bank. Retrieved on the 15.12.2018 fromhttps://www.cnbc.com/2017/10/13/bitcoin-get-serious-about-digital-currency-imf-christine-lagarde-says.html.
Scaillet, O., A. Treccani, and C. Trevisan. 2018. “High-Frequency Jump Analysis of the bitcoin
Market.” Journal of Financial Econometrics. Forthcoming.
Schilling, L., and H. Uhlig. 2018. “Some simple Bitcoin Economics”. Becker Friedman Institute for
Research in Economics Working Paper No. 2018-21.
Schuster, B. 2017. “Ripple – Why You Shouldn’t Invest (and Not Because It’s a Scam).” Retrieved on
the 12.12.2018 fromhttp://hivergent.com/you-shouldnt-invest-in-ripple-and-not-because-its-a-scam/.
SEC. 2017a. SEC Issues Investigative Report Concluding DAO Tokens, a Digital Asset, Were
Securities. U.S. Securities and Exchange Commission. Retrieved on the 14.12.2018 fromhttps://www.sec.gov/news/press-release/2017-131.
SEC. 2017b. Investor Bulletin: Initial Coin Offerings. Retrieved on the 28.02.2018 fromhttps://www.investor.gov/additional-resources/news-alerts/alerts-bulletins/investorbulletin-initial-coin-offerings.
SEC. 2019. MEMORANDUM: Meeting with Bitwise Asset Management, Inc., NYSE Arca, Inc., andVedder Price P.C. Retrieved on the 23.03.2019 fromhttps://www.sec.gov/comments/sr-nysearca-2019-01/srnysearca201901-5164833-183434.pdf.
Sharma, R. 2018. “The Number Of Dark Pools In Cryptocurrency Trading Is Increasing.”
Investopedia. Retrieved on the 14.10.2018 from https://www.investopedia.com/news/number-darkpools-cryptocurrency-trading-increasing/.
Shilov, K. 2018. “Top 10 Jurisdictions for a Worry-Free ICO in the Current Regulatory Climate.”
Hackernoon. Retrieved on the 23.11.2018 from https://hackernoon.com/top-10-jurisdictionsfor-a-worry-free-ico-in-the-current-regulatory-climate-40bb04661454.
Shu, S. and Z. Zhu. 2019. “Real-time Prediction of Bitcoin Bubble Crashes.” arXiv. Retrieved on the34

28.05.2019 from https://arxiv.org/abs/1905.09647.
Sockin, M., and W. Xiong. 2018. “A Model of Cryptocurrencies.” Princeton University. Retrieved on
the 14.02.2019 from http://wxiong.mycpanel.princeton.edu/papers/Crypto.pdf.
Taskinsoy, J. 2019. “Facebook’s Project Libra: Will Libra Sputter Out or Spur Central Banks to
Introduce Their Own Unique Cryptocurrency Projects?” Retrieved on the 26.07.2019 fromhttps://ssrn.com/abstract=3423453 .
Trimborn, S., and W. Härdle. 2018. “CRIX an Index for cryptocurrencies.” Journal of Empirical
Finance. https://doi.org/10.1016/j.jempfin.2018.08.004 .
Wildi, M., and N. Bundi. 2018. “bitcoin and Market-(In)Efficiency: a Systematic Time Series
Approach.” Special Issue of Digital Finance on Cryptocurrencies. Digital Finance. Smart Data
Analytics, Investment Innovation, and Financial Technology. ISSN 2524-6186.
World Bank. 2017. “UFA2020 Overview: Universal Financial Access by 2020.” The World Bank.
Retrieved on the 26.04.2018 fromhttp://www.worldbank.org/en/topic/financialinclusion/brief/achieving-universalfinancial-access-by-2020 .

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IRTG 1792 Discussion Paper Series 2018
For a complete list of Discussion Papers published, please visit
irtg1792.hu-berlin.de.
001
002
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005
006
007
008
009
010
011
012
013
014
015
016
017
018
019

"Data Driven Value-at-Risk Forecasting using a SVR-GARCH-KDE Hybrid"by Marius Lux, Wolfgang Karl Härdle and Stefan Lessmann, January
2018.
"Nonparametric Variable Selection and Its Application to Additive
Models" by Zheng-Hui Feng, Lu Lin, Ruo-Qing Zhu asnd Li-Xing Zhu,
January 2018.
"Systemic Risk in Global Volatility Spillover Networks: Evidence from
Option-implied Volatility Indices " by Zihui Yang and Yinggang Zhou,
January 2018.
"Pricing Cryptocurrency options: the case of CRIX and Bitcoin" by Cathy
YH Chen, Wolfgang Karl Härdle, Ai Jun Hou and Weining Wang, January
2018.
"Testing for bubbles in cryptocurrencies with time-varying volatility" by
Christian M. Hafner, January 2018.
"A Note on Cryptocurrencies and Currency Competition" by Anna
Almosova, January 2018.
"Knowing me, knowing you: inventor mobility and the formation oftechnology-oriented alliances" by Stefan Wagner and Martin C. Goossen,
February 2018.
"A Monetary Model of Blockchain" by Anna Almosova, February 2018.
"Deregulated day-ahead electricity markets in Southeast Europe: Price
forecasting and comparative structural analysis" by Antanina Hryshchuk,
Stefan Lessmann, February 2018.
"How Sensitive are Tail-related Risk Measures in a Contamination
Neighbourhood?" by Wolfgang Karl Härdle, Chengxiu Ling, February
2018.
"How to Measure a Performance of a Collaborative Research Centre" by
Alona Zharova, Janine Tellinger-Rice, Wolfgang Karl Härdle, February
2018.
"Targeting customers for profit: An ensemble learning framework to
support marketing decision making" by Stefan Lessmann, Kristof
Coussement, Koen W. De Bock, Johannes Haupt, February 2018.
"Improving Crime Count Forecasts Using Twitter and Taxi Data" by Lara
Vomfell, Wolfgang Karl Härdle, Stefan Lessmann, February 2018.
"Price Discovery on Bitcoin Markets" by Paolo Pagnottoni, Dirk G. Baur,
Thomas Dimpfl, March 2018.
"Bitcoin is not the New Gold - A Comparison of Volatility, Correlation,
and Portfolio Performance" by Tony Klein, Hien Pham Thu, Thomas
Walther, March 2018.
"Time-varying Limit Order Book Networks" by Wolfgang Karl Härdle, Shi
Chen, Chong Liang, Melanie Schienle, April 2018.
"Regularization Approach for NetworkModeling of German EnergyMarket"
by Shi Chen, Wolfgang Karl Härdle, Brenda López Cabrera, May 2018.
"Adaptive Nonparametric Clustering" by Kirill Efimov, Larisa Adamyan,
Vladimir Spokoiny, May 2018.
"Lasso, knockoff and Gaussian covariates: a comparison" by Laurie
Davies, May 2018.

IRTG 1792, Spandauer Straße 1, D-10178 Berlin
http://irtg1792.hu-berlin.deThis research was supported by the Deutsche
Forschungsgemeinschaft through the IRTG 1792.

IRTG 1792 Discussion Paper Series 2018
For a complete list of Discussion Papers published, please visit
irtg1792.hu-berlin.de.
020
021
022
023
024
025
026
027
028
029
030
031
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035
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"A Regime Shift Model with Nonparametric Switching Mechanism" by
Haiqiang Chen, Yingxing Li, Ming Lin and Yanli Zhu, May 2018.
"LASSO-Driven Inference in Time and Space" by Victor Chernozhukov,
Wolfgang K. Härdle, Chen Huang, Weining Wang, June 2018.
" Learning from Errors: The case of monetary and fiscal policy regimes"
by Andreas Tryphonides, June 2018.
"Textual Sentiment, Option Characteristics, and Stock Return
Predictability" by Cathy Yi-Hsuan Chen, Matthias R. Fengler, Wolfgang
Karl Härdle, Yanchu Liu, June 2018.
"Bootstrap Confidence Sets For Spectral Projectors Of Sample
Covariance" by A. Naumov, V. Spokoiny, V. Ulyanov, June 2018.
"Construction of Non-asymptotic Confidence Sets in 2 -WassersteinSpace" by Johannes Ebert, Vladimir Spokoiny, Alexandra Suvorikova,
June 2018.
"Large ball probabilities, Gaussian comparison and anti-concentration"
by Friedrich Götze, Alexey Naumov, Vladimir Spokoiny, Vladimir
Ulyanov, June 2018.
"Bayesian inference for spectral projectors of covariance matrix" by Igor
Silin, Vladimir Spokoiny, June 2018.
"Toolbox: Gaussian comparison on Eucledian balls" by Andzhey Koziuk,
Vladimir Spokoiny, June 2018.
"Pointwise adaptation via stagewise aggregation of local estimates for
multiclass classification" by Nikita Puchkin, Vladimir Spokoiny, June
2018.
"Gaussian Process Forecast with multidimensional distributional entries"
by Francois Bachoc, Alexandra Suvorikova, Jean-Michel Loubes, Vladimir
Spokoiny, June 2018.
"Instrumental variables regression" by Andzhey Koziuk, Vladimir
Spokoiny, June 2018.
"Understanding Latent Group Structure of Cryptocurrencies Market: A
Dynamic Network Perspective" by Li Guo, Yubo Tao and Wolfgang Karl
Härdle, July 2018.
"Optimal contracts under competition when uncertainty from adverse
selection and moral hazard are present" by Natalie Packham, August
2018.
"A factor-model approach for correlation scenarios and correlationstress-testing" by Natalie Packham and Fabian Woebbeking, August
2018.
"Correlation Under Stress In Normal Variance Mixture Models" by Michael
Kalkbrener and Natalie Packham, August 2018.
"Model risk of contingent claims" by Nils Detering and Natalie Packham,
August 2018.
"Default probabilities and default correlations under stress" by Natalie
Packham, Michael Kalkbrener and Ludger Overbeck, August 2018.
"Tail-Risk Protection Trading Strategies" by Natalie Packham, Jochen
Papenbrock, Peter Schwendner and Fabian Woebbeking, August 2018.

IRTG
SFB
IRTG
1792,
649,
1792,
Spandauer
Spandauer
Spandauer
StraßeStraßeStraße
1,1,D-10178
1,
D-10178
D-10178
BerlinBerlinBerlinhttp://sfb649.wiwi.hu-berlin.de
http://irtg1792.hu-berlin.de
http://irtg1792.hu-berlin.de
This
This
research
researchwas
was
supported
supported
byby
thetheDeutscheDeutscheForschungsgemeinschaft
Forschungsgemeinschaft
Forschungsgemeinschaft
throughthroughthethrough
SFBthe649
the
IRTG"Economic
IRTG
1792.
1792.Risk".

IRTG 1792 Discussion Paper Series 2018
For a complete list of Discussion Papers published, please visit
irtg1792.hu-berlin.de.
039
040
041
042
043
044

"Penalized Adaptive Forecasting with Large Information Sets andStructural Changes" by Lenka Zbonakova, Xinjue Li and Wolfgang Karl
Härdle, August 2018.
"Complete Convergence and Complete Moment Convergence for Maximal
Weighted Sums of Extended Negatively Dependent Random Variables"by Ji Gao YAN, August 2018.
"On complete convergence in Marcinkiewicz-Zygmund type SLLN for
random variables" by Anna Kuczmaszewska and Ji Gao YAN, August
2018.
"On Complete Convergence in Marcinkiewicz-Zygmund Type SLLN for
END Random Variables and its Applications" by Ji Gao YAN, August
2018.
"Textual Sentiment and Sector specific reaction" by Elisabeth Bommes,
Cathy Yi-Hsuan Chen and Wolfgang Karl Härdle, September 2018.
"Understanding Cryptocurrencies" by Wolfgang Karl Härdle, Campbell R.
Harvey, Raphael C. G. Reule, September 2018.

IRTG
SFB
IRTG
1792,
649,
1792,
Spandauer
Spandauer
Spandauer
StraßeStraßeStraße
1,1,D-10178
1,
D-10178
D-10178
BerlinBerlinBerlinhttp://sfb649.wiwi.hu-berlin.de
http://irtg1792.hu-berlin.de
http://irtg1792.hu-berlin.de
This
This
research
researchwas
was
supported
supported
byby
thetheDeutscheDeutscheForschungsgemeinschaft
Forschungsgemeinschaft
Forschungsgemeinschaft
throughthroughthethrough
SFBthe649
the
IRTG"Economic
IRTG
1792.
1792.Risk".