Editorial: Understanding Cryptocurrencies
Wolfgang Karl Härdle, Campbell R. Harvey, Raphael C. G. Reule
EEditorial: Understanding Cryptocurrencies*
Wolfgang Karl H¨ardle
Humboldt-Universit¨at 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.de
Campbell 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-Universit¨at zu Berlin, Germany.irtg1792.wiwi[at]hu-berlin.de
Abstract
Cryptocurrency refers to a type of digital asset that uses distributed ledger, orblockchain, technology to enable a secure transaction. Although the technology iswidely misunderstood, many central banks are considering launching their own na-tional cryptocurrency. In contrast to most data in financial economics, detailed dataon the history of every transaction in the cryptocurrency complex are freely avail-able. Furthermore, empirically-oriented research is only now beginning, presentingan extraordinary research opportunity for academia. We provide some insights intothe mechanics of cryptocurrencies, describing summary statistics and focusing onpotential future research avenues in financial economics.
JEL Classification: C01, C58, E42, E51, G10, K24, K42, L86, O31Keywords: 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. − X isacknowledged. a r X i v : . [ q -f i n . C P ] J u l Financial support from the Deutsche Forschungsgemeinschaft via CRC 649 ”EconomicRisk” and IRTG 1792 ”High Dimensional Non Stationary Time Series” and the EuropeanUnion’s Horizon 2020 research and innovation program ”FIN-TECH: A Financial super-vision and Technology compliance training programme” under the grant agreement No825215 (Topic: ICT-35-2018, Type of action: CSA), Humboldt- Universit¨at zu Berlin, isgrate-fully acknowledged. Introduction
In 2008, the pseudonymous “Satoshi Nakamoto” posted a white paper describing an im-plementation 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 functionsin finance. Cryptocurrencies embrace a peer-to-peer mechanism and effectively eliminatethe “middle man”, which could be a financial institution. For example, no bank accountor credit card is needed to transact in the world of cryptocurrencies. Indeed, a crypto-currency “wallet” serves the same function as a bank vault. With a smart phone and theinternet, the potential exists for a revolution in financial inclusion — given that over twobillion people are unbanked (GlobalFindex, 2017; World Bank, 2017).The technology, however, goes well beyond providing banking services to the un-banked. It holds the potential for cheap, secure, and near-instant transactions, allowingbillions of people to join the world of internet commerce, paying, and being paid, forgoods or services, outside of the traditional banking and credit card infrastructure.Cryptocurrencies transactions potentially enable near real-time micropayments. Creditcards 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 micropay-ments 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 organi-zation (DAO), an open-source peer-to-peer digital network that enforces the rules it is setup with. In this DAO setting, the money supply is set by an algorithmic rule, and the in-tegrity of the network replaces the need to trust the integrity of human participants. Thegrowth of crypotcurrency technology therefore poses a challenge to traditional monetaryauthorities and central banks, as Facebook’s “Libra” coin pre-emission market accept-ance suggests (Taskinsoy, 2019). Central banks understand this, and many banks haveinitiated their own national cryptocurrency initiatives (Bech and Garratt, 2017).As with any new technology, risks are present. In the nascent cryptocurrency mar-ket, 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 broaderthreat to our society and institutions (Foley et al., 2018). The benefits, such as low3ransaction 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, innovation is con-strained (Catalini and Gans, 2016). Cryptocurrencies have, in contrast to many markets,a plethora of available and free data, ripe for empirical investigation. We are just nowseeing the genesis of academic research focusing on this emerging technology (Harvey,2014, 2017a, 2017b; H¨ardle et al., 2018; Kim et al., 2019).We have four goals in this paper. First, we explain the mechanics of cryptocurrenciesat a high level. Second, we detail useful data sources for researchers. Third, we providebasic summary statistics given the available data. Finally, we offer a list of possibleresearch applications.
The concept of supplementary (Delmolino et al., 2016), alternative (Ametrano, 2016), ordigital currencies (Chaum, 1983) is not new, but the concept of an open-source currencywithout 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 amedium of exchange using cryptography to secure transactions, to control the creation ofadditional value units, and to verify the transfer of assets. Many different cryptocurrenciesexist, each with their own set of rules, see, for example, coinmarketcap.com (Iwamuraet al., 2014; Abraham et al., 2016; Bartos, 2015; Park et al., 2015). Differences amongthe cryptocurrencies may involve, for example, the choice of the consensus mechanism,the latency, or the cryptographic hashing algorithms.
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 centrallymanaged traditional ledger (Babich and Hilary, 2018a). A blockchain can be implementedin 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 1shows 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 thedatabase. Distributed databases are not new, and most distributed databases are not4lockchains. The key difference between a regular distributed database and one set on ablockchain 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 historicalblock, the digest changes for that block and it will not match the first line of the nextblock. When the network detects such an inconsistency, it throws out the corruptedblock 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 beingappend-only, based on the respective consensus mechanism), it is extremely unlikely thathistory 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. (github.com/QuantLet/CrixToDate)All presented numerical and pictoral examples shown are reproducible and can befound on (Borke and H¨ardle, 2018).
A hash function is a one-way mathematical algorithm that takes an input and transformsit 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 beconfused with encryption. With encryption, a file is encrypted with a key and decryptedwith a key. Hashing has no decryption step. Additionally, a good hashing algorithmmakes 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).One common cryptographic hashing algorithm, the Secure Hash Algorithm (SHA-256), has a maximum input size of 2 -1 bits (more than 2 million terabytes) and an5utput of 256 bits. We usually represent the SHA-256 output in hexadecimal form, alsocalled base 16 (the characters 0-9 and a-f). In order to make the theoretical maximalinput size more visual, we assume that 1 bit equals to 1 mm . A soccer field has thedimensions of 7,140 m , therefore 2 -1 bits could theoretically fill 2,583,577,601 soccerfields. As the whole surface of the earth equals to 510,000,000,000,000 m , we could alsocover around 36170 times the earth with the theoretical input size. This input informa-tion will be stored in a very short output, the hash. If just one piece of the input, likefor example a blank space or a comma, is changed, then the hash output be completelydifferent.Importantly, the digest does not reveal the original information. For example, supposewe want to send an electronic document via email, but are worried that the documentcould be corrupted and the content altered. One way to verify the integrity of the emailis 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 thedocument. 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. Postingthe 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” (cran.r-project.org/web/packages/digest), the Python libary “hashlib” (docs.python.org/2/library/hashlib.html), or many online programs such as interactive Github-based repositories(emn178.github.io/online-tools/sha256.htm). Input: H e llo CRIX Output: f9a2b57d86cc4ba463a3bedbbe0c7e850da5b34c6bcc1a92b794308ceaf93761
Input: H a llo CRIX Output:
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 timestampingthe data itself, the privacy of the content was preserved. Haber and Stornetta’s time-stamping proposal also solved the potential problems of collusion and lack of trust bylinking hash values together and using digital signatures, which uniquely identify the6igner.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 expendedfor calculating the stamp prior to sending the email. Whereas an individual email couldbe 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 termfirst used by Jakobsson and Juels (1999).Many applications of blockchain technology exist, but we focus our attention oncryptocurrencies. Bitcoin (cryptocurrency known as BTC; bitcoin.org) was the first ex-ample of a digital asset, which has no backing or intrinsic value, based on blockchaintechnology (Nakamoto, 2008; B¨ohme et al., 2015, for a review).A common characteristic of cryptocurrencies is a network of peers with equal stand-ing. Each participant has a copy of the ledger and offers an algorithmic consent on thecorrect ledger (i.e., which new block is accepted and which block is rejected to form anew part of the blockchain). It is unneccesary to know your peers in a blockchain orto trust them. It is also possible to design a blockchain so that only specific trustedparties have the ability to add to the ledger. Private, permissioned blockchains are asource of considerable interest for many central banks (MAS, 2017; Bundesbank, 2017;SARB, 2018). In contrast to cryptocurrencies such as bitcoin, trust is necessary in thepermissioned blockchain, because the central banks actually “own” the coins, i.e. as agoverning layer they have the 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 changeto 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.7 .4 Cryptocurrencies
A currency without an intrinsic value, such as a cryptocurrency like bitcoin, can only func-tion if sufficient market acceptance is present and if the belief exists that the currencyhas the value attributed to it. With a conventional fiat system, money has value becausepeople trust the central bank. For a cryptocurrency, additions to the public ledger areconfirmed 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 theinput, that is, the transactor actually has the funds she or he wants to transfer. The onlyexceptions 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 tothe next. Why is it then that massive computing power is needed to maintain the bitcoinnetwork? The power required has to do with the proof-of-work consensus concept. Thedanger 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. Tomake this unlikely, Nakamoto (2008) proposed the idea of requiring “work”. Thus, in-stead of simply providing any SHA-256 output, a special SHA-256 output, which hasmany 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 thenetwork as the next block to be added to the blockchain. This “difficulty” ensures that anew block is added on average every 10 minutes (bitinfocharts.com/comparison/bitcoin-confirmationtime.html)to the bitcoin blockchain (so-called block time). To find this spe-cial hash, certain nodes, called miners , will take a candidate group of verified transactionsand cycle through numbers, say, 1, 2, 3, . . . [very large number], until the output of theSHA-256 has some leading zeros. This number, which is added to a digest of the trans-actions, is called a nonce .The computing power requirement arises because the leading zeros are determinedvia 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 / . 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, presentsits group of transactions and the nonce to the network. Verifying that the transactionsplus 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 consensusmechanism. The miner that found the winning block is rewarded with freshly “minted”8itcoin. If technology advances or additional computing joins the mining efforts in thenetwork so that blocks are being solved in less than 10 mintues, the algorithm adjuststhe 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 ex-pensive. Cryptocurrency miners spend computing power to find the hash as describedabove. A gold miner only gets rewarded if gold is found. Cryptocurrency miners only getrewarded if they are the first to find the winning hash. Like mining for gold, mining forcryptocurrency is risky. The continuous expenditure of resources such as for hardwareand energy (see also subsection 4.9) for a prolonged period without being rewarded is aninherent 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 historyis 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 ex-ist and they may not entail the high equipment and energy costs that bitcoin minersface. The second leading cryptocurrency, Ethereum (ethereum.org), uses a similar proof-of-work mechanism. Ethereum, however, has committed to change to a proof-of-stakemechanism (Franco, 2015; ETH, 2018). Instead of allocating block mining proportionallyto the relative hashing power, the proof-of-stake protocol allocates blocks proportionallyto the current holdings (Buterin, 2014; Cotillard, 2015). As a result, the participantswith the most cryptocurrency are particularly incented to do the right thing to keepthe system running and healthy. Such a method holds the promise of much-improvedlatency and substantially less energy consumption. A participant who possesses 1% ofthe cryptocurrency could mine 1%, on average, of the proof-of-stake blocks. Ethereumhas a number of other differences from bitcoin. Ethereum blocks are added approxim-ately every 14 seconds (etherscan.io/chart/blocktime) rather than every 10 minutes, andimportantly, ethereum allows for smart contracts , or small computer programs, to bedeployed in its blockchain. These smart contracts are run 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.ioBluepaper; steem.io/steem-bluepaper.pdf) and Slimcoin’s proof of burn bootstraps onecryptocurrency off another by demonstrating proof of having “burnt” some units of valueby sending a specific amount to a verifiable unspendable address (Slimcoin Whitepaper;9ithub.com/slimcoin-project/slimcoin-project.github.io/raw/master/whitepaperSLM.pdf),or different implementations of the Byzantine fault tolerance, which was first described asthe Byzantine Generals’ Problem by Lamport et al. (1982), are used by systems such asNEO (neo.org), Stellar (stellar.org) and Hyperledger Fabric (hyperledger-fabric.readthedocs.io). We can group cryptocurrencies into seven broad classes. Bitcoin falls into the first cat-egory; it was originally designed as a transaction mechanism . Think of it as Gold 2.0.Litecoin (litecoin.org) is very similar to bitcoin and was one of the first alternatives tobitcoin. Litecoin’s blocks are added every 2.5 minutes (bitinfocharts.com/comparison/litecoin-confirmationtime.html), on average, compared to every 10 minutes for bitcoin.Ethereum falls into the second class: a distributed computation token. As mentionedearlier, it is possible to run a computer program on the ethereum network. Think ofit as an Internet computer where small programs, smart contracts, are executed whencalled upon, on every node. Other examples in this class include Tezos (tezos.com), EOS(eos.io) and DFinity (dfinity.org).The third class of cryptocurrency is called a utility token. A utility token is a pro-grammable blockchain asset. One example is Golem (golem.network), a currency thatallows the user to buy computing power from a network of users or to sell excess capacityto others. Storj (storj.io)is similar and allows the user to rent out unused disk storage.Other examples in this class are Sia (sia.tech) and FileCoin (filecoin.io).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 and settle-ment.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 somethingdifferent – 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-fungibletokens are Cryptokitties (cryptokitties.co) and Decentraland (LAND; decentraland.org).10he final class of cryptocurrencies are called stablecoins . There are four categories.The first category is collateralized with fiat currency. This includes stablecoins such astether (USDT)(tether.to) and Circle’s USDC (circle.com). These cryptocurrencies aredesigned to be fully collaterized by US dollar deposits. LBXPeg (lbx.com/blog/lbx-peg)is tied to pound sterling. An emerging market, Mongolia has a cryptocurrency calledCandy (candy.mn) tied to their currency. This class also includes national cryptofiats.As mentioned earlier, many central bank are investigating the potential Fedcoin (USFederal Reserve), Eurocoin (European Central Bank), CADCoin (Bank of Canada), forexample. Venezuela already issued a national crypto called Petro (petro.gob.ve).The second category of stablecoins are collateralized with real assets. Examples in-clude currencies that are collateralized by gold (Digix Gold, DGX; digix.global), a basketof seven precious metals used in technology (Tiberius coin, TCX; tiberiuscoin.com) oreven Swiss real estate (Swiss Real Coin, SRC; swissrealcoin.io).The third category of stablecoins are cryptocurrency collateralized. The leading ex-ample is the collateralized debt positions that MakerDAO offers that enable their DAIcoin (makerdao.com/en/dai) to be pegged to the US dollar.The final category of stablecoins are uncollateralized. An example of this type ofinititive is the Basis project (basis.io) and their basecoin which has been put on holdgiven regulatory concerns.This list of classifications is not exhaustive because many cryptocurrency concepts,such as Overlay (overlay.market) or Facebook’s Libra (libra.org), do not easily fit withinour seven-category taxonomy. Our point is simple: cryptocurrencies have many usesand characteristics that extend beyond the traditional cryptocurrencies of bitcoin andethereum. We will now focus on an econometric analysis of the currently most liquid cryptocur-rencies. Valuation of currencies that are not collateralized or linked to real assets is achallenge. These currencies are highly volatile and subject to bubble-like behavior. Thesecurrencies, however, provide an ideal testing ground for economic theory. In the fall of2017, bitcoin rose to over $19,000. The bubble burst in 2018. Because every bitcointransaction is freely available, we are provided with an extraordinary research opportun-ity. We begin with a simple benchmarking analysis using the S&P 500 Index (S&P 500),11PDR Gold Shares (GOLD), and CBOE Volatility Index (VIX; cboe.com/vix), whichmeasures the implied volatility of the S&P 500 index.
Many, sometimes very generic, data sources are available for cryptocurrencies, which un-like traditional assets trade 24/7, creating a vast amount of data to capture. Blockchain-based systems — most of which are open to the public for participation — have datathat are readily available using basic API’s (application programming interface). Insubsection 4.3, we discuss exchange APIs, which provide the data for actual crypto-currency market transactions (Guo and Li, 2017). Several of the more important datasources include CoinGecko (coingecko.com), a cryptocurrency ranking and evaluationsite that breaks down quantitative and qualitative data for a number of different met-rics, as well as Coinmarketcap (coinmarketcap.com), Onchainfx (onchainfx.com), Crypto-compare (crypt ocompare.com), BitInfoCharts (bitinfocharts.com), CoinCheckup (co-incheckup.com), and Coincodex (coincodex.com). Each has unique attributes.
While there are thousands of cryptocurrencies, we focus our analysis on three: bitcoin(BTC), ethereum (ETH), and Ripple (XRP; ripple.com). To represent traditional assets,we have chosen S&P 500, GOLD, and VIX. Each cryptocurrency has a different imple-mentation. Some, like Litecoin, are very similar to BTC. As previously mentioned, ETHallows for distributed computation. In contrast to BTC, ETH may be easier to valuebecause it has a 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 differentnetworks, geographic borders, and currencies (Aranda and Zagone, 2015). The Ripplesystem’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 high For this brief analysis, we are using cryptocurrency data provided by the CRIX database (thecrix.de)and the Cryptocompare API (cryptocompare.com/api), as well as data for the traditional assets providedthrough the Bloomberg Terminal. Further information can also be found at Jameson Lopp’s BitcoinResources (lopp.net/bitcoin.html). C u m u l a ti v e r e t u r n Cumulative return over time
Figure 2: Cumulative return over time between May 1, 2017 and Jun. 30, 2019 of
BTC , XRP , ETH , GOLD and
S&P 500 . (github.com/QuantLet/UCC)XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXLXXTables 1 and 2, as well as Figure 3, provide the correlations of the daily and monthlyreturns from May 1, 2017 to Jun. 30, 2019 for the five assets and VIX. Red indicates apositive correlation and blue indicates a negative correlation, with significant correlationsbeing marked in a darker color. The correlations are likely time varying. Figure 3 showsthe correlation time series of rolling windows of one trading year (250 days) relative toBTC. Both XRP and ETH are positively correlated with BTC. No evidence is shown ofa significant correlation with S&P 500, GOLD or the VIX.13able 1: Daily Correlation, May 1, 2017 to Jun. 30, 2019.Daily BTC ETH XRP GLD SP500 VIXBTC 0.42 0.21 0.04 0.04 -0.06ETH 0.42 0.20 0.06 0.01 -0.01XRP 0.21 0.20 0.04 -0.01 -0.02GLD 0.04 0.06 0.04 -0.15 0.13SP500 0.04 0.01 -0.01 -0.15 -0.80VIX -0.06 -0.01 -0.02 0.13 -0.80Table 2: Monthly Correlation, May 1, 2017 to Jun. 30 2019.BTC ETH XRP GLD SP500 VIXBTC 0.48 0.45 0.08 0.13 -0.08ETH 0.48 0.58 0.26 0.12 -0.19XRP 0.45 0.58 0.15 -0.08 0.02GLD 0.08 0.26 0.15 -0.10 0.17SP500 0.13 0.12 -0.08 -0.10 -0.75VIX -0.08 -0.19 0.02 0.17 -0.75(github.com/QuantLet/UCC)We note, first, the cryptocurrencies are positively correlated, which is especially evid-ent in an analysis of the monthly data. Second, the correlations of the cryptocurrencieswith both S&P 500 and GOLD are relatively low over the limited sample. We also includethe 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 ofGOLD and S&P 500. 14
017 2018 2019 − . . . . . . Time C o rr e l a ti on
250 days Rolling Window Correlation to BTC
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. (github.com/QuantLet/UCC)XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXLXX . . . . Time S t a nd a r d D e v i a ti on
100 days Rolling Window Standard Deviation
Figure 4: 100 days Rolling Window Standard Deviation of
BTC , XRP , ETH , GOLD and
S&P 500 ; daily data, May 1, 2017 to Jun. 30, 2019. (github.com/QuantLet/UCC)XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXLXX15urther insights into the distributional properties of cryptocurrencies can be gainedby studying the higher moments of returns, for example, excess kurtosis and skewness, asshown in Table 3. Not surprisingly, the higher moments of the cryptocurrencies are farfrom what we would expect for a normal distribution. This observation is also evident inthe QQ plots for BTC and GOLD shown in Figure 5.Table 3: Log Daily Returns Statistics, May 1, 2017 to Jun. 30, 2019.Mean Std. Dev. Skewness e. Kurtosis Min. Max.BTC 0.0028 0.0454 0.0452 2.8227 -0.1892 0.2276ETH 0.0019 0.0594 0.1501 2.0517 -0.2228 0.2602XRP 0.0028 0.0767 1.6053 10.3886 -0.3671 0.6183GLD 0.0002 0.0062 0.1681 1.0159 -0.0172 0.0254SP500 0.0004 0.0086 -0.5997 5.1430 -0.0418 0.0484(github.com/QuantLet/UCC) l lll ll ll lll lll l lllll l lll ll lll lllll lll ll lll ll l l ll l ll lll ll lllll llllll lll l llll l llll ll ll l lll l ll l ll ll lll l lll ll lll lll ll llll l ll lll ll l lll ll llll lll ll ll l ll ll lll lllll lll lll l llll ll l l lllll lll l ll llll lll l lll ll ll lll l lll ll l lllll l ll lll lll l lll l l ll ll l ll ll l lll ll ll lll llll l ll lll ll l l l ll l l ll l llll ll l lll ll llll ll lll ll lll l ll l lll ll lllll l ll ll ll lll l llll ll lll ll l ll l ll ll lll ll ll l ll l ll lll l ll ll llll l ll lll l lll l lll ll ll llll lll ll ll ll lll llllll ll l ll ll lllll ll ll ll lll ll ll l llll ll l l l l ll ll ll l lll lllll l lll llll ll llll ll ll ll ll llll llll l lll ll l ll lll l ll lll ll l ll lll l lll lll l lll l llll lll ll ll l llllllllll l llll ll llll lllll ll l ll l ll ll ll l lll lll lll l l ll l ll l l l lll ll ll ll ll ll ll lll ll ll l ll lll ll l ll ll l ll ll lll l ll l ll l ll l ll ll lll l l l ll ll l ll ll ll llll llll lll lll l ll l lll llll l ll lll l lll ll lll ll ll ll ll ll ll l lll ll ll ll l llll l lll ll lllll l ll ll lll lllll ll ll l ll lll ll ll lll ll l l llll ll −3 −2 −1 0 1 2 3 − − − Standard Normal QQ plot for BTC returns
Theoretical Quantiles S a m p l e Q u a n til e s l ll l lll l lll l ll lll ll ll ll ll llll l ll l ll l ll ll lll ll lll ll ll ll ll l lll ll llll ll l l lllll lll ll ll l ll l ll ll lll ll lll lll l ll l lll l ll l llll lll l ll ll ll lll lll ll l ll lll l ll l ll llll lll ll ll l ll l ll ll l ll l l ll llll l l lll l ll l ll ll l lll ll l ll l l llll l ll ll ll ll ll l ll lll l ll ll llll l ll ll lll ll lllllll ll l lll l lllll llll l llll l ll l lll l lll lll lll lll l llll l ll lll lll ll ll ll ll l ll ll ll l ll lll ll ll l lllll lll ll ll lll l l lll ll lll l lll ll ll l ll l l ll ll l lll lll llll llll lll l l ll lll llll lll lll ll l lll lll ll ll llll ll ll ll lll lll lll ll lllll ll l ll ll ll lll ll ll ll l lll ll l ll ll l ll l llll ll ll ll l lll lll l lll ll ll ll l ll ll ll ll lll ll l lll l l lll ll ll l l lll l l ll ll −3 −2 −1 0 1 2 3 − − − Standard Normal QQ plot for GLD returns
Theoretical Quantiles S a m p l e Q u a n til e s XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXLXXFigure 5: Standard Normal QQ plots for BTC and GOLD, May 1, 2017 to Jun. 30, 2019.(github.com/QuantLet/UCC)Table 4: Johansen cointegration of gold and bitcoin, May 1, 2017 to Jun. 30, 2019. H Test statistic 10 % 5 % r ≤ r = 0 18.22 13.75 15.67XXXVariable r corresponds to the number of cointegration relations.(github.com/QuantLet/UCC) 16hile cryptocurrencies have many similarities to gold (e.g., no central supply, noofficial 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. H Test statistic 10 % 5 % r ≤ r = 0 8.12 12.91 14.90XXXVariable r corresponds to the number of cointegration relations.(github.com/QuantLet/UCC)By testing our data with a new method suitable for high-dimensional nonstationarytime series, as researched by Onatski and Chen (2018), we can underline the previousfinding of the non-existence of a cointegrating relationship as shown in Table 5. Cryptocurrencies, as a new type of asset, offer many research opportunities for financialeconometrics. 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). Wewill 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.
The acceptance of this new technology has risen rapidly and activity in cryptocur-rency trading has led to the establishment of more than 200 highly fragmented, mostlyunregulated cryptocurrency exchanges, which act more like broker-dealers than tradi-tional exchanges (Hansen, 2018). There is considerable “off chain” trading. This mightbe intrabroker trading matching or even dark pool trading which might lead to pricejumps at exchanges (Sharma, 2018). More types of cryptocurrency are being traded inparallel, on different exchanges, with different prices (see subsections 3.1 and 3.2.). These17arallel information sources yield dynamic high-dimensional interdependencies.Robinson et al. (2019) introduce a “cross chain” technique which allows transactionsto be executed and their respective value to be validated across sidechains. They outlinea programming model of a swap contract for exchanging value between sidechains, anddiscuss how this technology can be readily applied to many blockchain systems to providecross-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 thatensure a high throughput for the transactions generated in Internet-of-Things (IoT) sys-tems. Transactions are continuously appended to an acyclic structure called tangle andeach new transaction selects as parents two existing transactions (called tips) that itapproves. 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 (iota.org) 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 protocolas a stochastic game and analyse the equilibrium strategies of rational, strategic miners.They show how forks can be generated by information delays and software upgrades, andidentify negative externalities. Easley et al. (2018) investigate the role that transactionfees play in the evolution of bitcoin from a mining-based structure to a market-basedecology. They develop a game-theoretic model to explain the factors leading to the emer-gence 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 howmicrostructure features such as exogenous structural constraints influence the dynamicsand stability of the bitcoin blockchain. Cong et al. (2018a) develop a theory of miningpools that highlights risk sharing as a natural centralizing force.Bhambhwani et al. (2019) research if cryptocurrencies have an intrinsic value relatedto the networks’ computing power and network adoption. Their hypothesis is motivatedby the fact that miners expend real resources to generate the computing power requiredto secure and operate the blockchain. An optimally performing blockchain serves as amedium for transactions and attracts users, developers, and intermediaries, thereby lead-ing to an increase in the cryptocurrency’s network size. They find, that there is a positiveand statistically significant relationship among price, computing power, and network size(adoption levels respectively), which can be used to construct asset pricing factors.18ng 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) num-ber of merges, 3) number of active accounts, and 4) number of total comments. Thebiggest cryptocurrencies by market capitalization experience the most activity, which isto 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-partysources, like analyst reports and recommendations, and perhaps news flow and confer-ence calls. The different sources of information available for cryptocurrencies presentsnew opportunities.Research on trading patterns, herding effects, and economic decision making has star-ted on sentiment construction/projection and cryptocurrency-specific lexica. Naturallanguage processing techniques in combination with other machine learning techniquesallow researchers to build sentiment measures. Cretarola and Figa-Talamanca (2017) pro-pose a confidence-based model for asset and derivative prices in the bitcoin market withprices influenced by measures linked to the confidence in the underlying technology. Aste(2018) studies the dependency and causal structure of the current cryptocurrency marketand investigates the collective movements of both prices and social sentiment related toalmost 2,000 cryptocurrencies traded during the first six months of 2018. His resultsuncover a complex structure of interrelations, in which prices and sentiment influenceeach 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 learningmethods. Accounting for context-specific information and word similarity by learningword embeddings, they apply natural language processing methods for sentence-levelclassification and sentiment index construction. They argue that the constructed sen-timent indices are value-relevant in terms of its return and volatility predictability forcryptocurrency market indices, see subsection 4.5. Pagnotta and Buraschi (2018) also ad-dress the valuation of cryptocurrencies, and characterize the demand for bitcoins by theavailable hashrate and show that the equilibrium price is obtained by solving a fixed-pointproblem. They find, that “price/hashrate-spirals” amplify the demand and supply shocks.Schilling and Uhlig (2018) analyze the coexistence and the competition between theUSD and bitcoin. They analyze bitcoin price evolution and interaction between thebitcoin price and monetary policy which targets the USD, and obtain a fundamentalpricing equation, which in its simplest form implies that bitcoin prices form a martingale.19 .2 Monetary systems and financial development
Blockchain-based monetary systems hold the potential to impact the macroeconomy, asthe 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 behindcryptocurrencies has the potential to improve a central banks’ operations and can serveas a platform to launch their own cryptocurrencies (Raskin and Yermack, 2016). Thepetromondea (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 in 2020coined “Libra”. Central authorities were fast to criticize this step and governments, in-cluding the United States, France, the United Kingdom, Germany, and Japan, expressedtheir resentment and scepticism. However, the propagation of systemic banking crisesfostered 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 beingin the project, looks similar to the Board of Governors of the Federal Reserve Systemcomprising 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 respec-tive value competition by applying a matching function of money demand to the opera-tion of a blockchain to observe a monetary equilibrium. With cryptocurrencies alreadysubstituting for fiat money, Hendry and Zhu (2017) model the co-existence of differenttypes of transactions and show that monetary authorities’ coordination capabilities arebeing restricted by the use of nonregulated cryptocurrencies.The Catalini et. al (2019) paper on a market design for a blockchain-based financialsystem provides an extended abstract on a theory of long-run equilibrium in blockchain-based financial systems. Their theory elucidates the key market design features that sep-arate proof-of-work and proof-of-stake approaches in the long run and when each designmight each be appropriate (see subsection 2.4) and conclude, that with weak relationalcontracts or substantial concerns about outside interference, proof-of-work designs maybe preferable. With regions that have local institutions that are reliable enough to makedelegation feasible, proof-of-stake designs can lead to efficiency gains and improvementsin governanc. 20ryptocurrencies 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. There are hundred of cryptocurrency exchanges around the world. Some of the best-known are: Binance, Bitfinex (bitfinex.com), Kraken (kraken.com), Bitstamp (bitstamp.net),Coinbase (coinbase.com), Bitflyer (bitflyer.com), Gemini (gemini24.zendesk.com), itBit(itbit.com), Bittrex (international.bittrex.com) and Poloniex (poloniex.com). Each ofthese exchanges has its own specific traits. Kraken claims to be the largest bitcoin ex-change in EUR volume and liquidity as well as being a partner in the first cryptocurrencybank, collaborating with the German BaFin (bafin.de) -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 ethereum exchange, met its capitalrequirements by placing all USD deposits at a FDIC-insured bank.Coinmarketcap (coinmarketcap.com/exchanges/volume/24-hour/) lists 218 exchanges,but even the measurement of trading volume is controversial. A recent filing to the SEC(2019) argues that 95% of the trading volume in bitcoin is fake. The research identifies10 exchanges with actual volume (out of 81), Binance, Bitfinex, Kraken, Bitstamp, Coin-base, Bitflyer, Gemini, itBit, Bittrex and Poloniex.Due to the large number of exchanges with an ever increasing number of crypto-currencies, 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, whenone goes outside the ten exchanges with credible volume, some of the price discrepanciesmay not be real. Bistarelli et al. (2019) show via a theoretical model and via an empir-ical strategy, that arbitrage opportunities are possible by trading on different exchanges(Cretarola et al., 2017). Their approach is complementary to other theoretical studieson bitcoin arbitrage such as Barker (2017) or Pieters and Vivanco (2015), where the re-searchers 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 crypto-21urrency prices relative to fiat currencies across exchanges. These opportunities oftenpersist for several days or weeks, and the price dispersions exist even in the face of signi-ficant trading volumes on many of the exchanges. Makarov and Schoar find that spreadsare much smaller when cryptocurrencies are traded against each other, suggesting thatcross-border controls on fiat currencies play an important role in creating the arbitrageopportunities. By constructing a common component and an idiosyncratic component,they conclude that the order flow plays an important role in explaining the spreadsbetween 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 thatChinese high-frequency traders have used algorithms to identify mispricings and arbit-rage opportunities across numerous exchanges in China. However, later in 2017, Chinabanned all cryptocurrency exchanges.Hautsch et al. (2018) note that consensus protocols confront traders with randomwaiting times until the transfer of ownership is accomplished. This settlement processexposes arbitrageurs to price risk and imposes limits to arbitrage. They derive theoreticalarbitrage boundaries under general assumptions and show, by using high-frequency bit-coin data, that these increase with expected latency, latency uncertainty, spot volatility,and risk aversion. They conclude, that settlement through decentralized systems inducesnon-trivial frictions affecting market efficiency and price formation.
Cong et al. (2018b) provide the first fundamentals-based dynamic pricing model ofcryptocurrencies and platform tokens, taking into consideration the user-base extern-ality and endogenous user adoption. Because the expectation of token price appreciationinduces more agents to join the platform, tokens capitalize future user adoption, generallyenhancing 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 wediscuss 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 bitcoinpricing. Their paper relates to a variety of broad themes in the study of information tech-22ology adoption and usage. They conclude, that bitcoin presents a unique opportunity toobserve 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 wherethe 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 withorder aggressiveness, but that this pattern reverses in the outer layers of the book. Ag-gressive 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 liquidityappears to migrate outward in response to the information asymmetry.Griffin and Shams (2018) investigate whether tether — see subsection 2.5 — influencesbitcoin and other cryptocurrency prices, and whether the growth of a pegged crypto-currency is primarily driven by investor demand, or is supplied to investors as a schemeto profit from pushing cryptocurrency prices up. Their findings provide support for theview that price manipulation may be behind substantial distortive effects in cryptocur-rencies.Wildi and Bundi (2018), analyze momentum trading strategies, and claim that bit-coin markets have become much more efficient markets. The impact of high-frequencytrading combined with 24/7 trading opportunities has yet to be researched. It remainsto be seen if an increase in liquidity will reduce the heretofore observed harsh swings incryptocurrency prices.The launch of bitcoin futures on both the CME as well as the CBOE (XBT) providesopportunities to study price discovery (Karkkainen, 2018). Bitcoin futures trading givesmany institutional investors the ability to invest in bitcoin and also allows to settle con-tracts in fiat money, potentially boosting liquidity. Currently the bitcoin futures volumeis approximately the same as the largest exchange, Binance. Almost all of the volumeis in the CME contract. The CBOE has announces they will no longer offer a bitcoinfutures contract.Scaillet et al. (2018) identify high-frequency jump components in the bitcoin marketand 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 as23he result of a balance between idiosyncratic sentiments of noise traders and the effortsof arbitrageurs to equilibrate prices across exchanges.
Index construction poses unique challenges when it comes to cryptocurrencies. Tra-ditional indices, such as the S&P 500 or Russell 3000, gather data from stocks thatare traded over particular time intervals in a small numbers of venues. Cryptocurren-cies are traded 24/7 on hundreds of venues, like WorldCoinIndex (worldcoinindex.com),CoinMarketCap, CryptoCompare, CryptoCurrencyIndex30 (cci30.com), or the CME CFCryptocurrency Indices (cmegroup.com/trading/cryptocurrency-indices.html). Prelimin-ary research on index construction is made, for example, by Trimborn and H¨ardle (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 bitcoinfutures 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 oncertain 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 cryptocur-rency market (represented by CRIX) through the construction of an implied volatilityproxy in absence of the derivatives for the majority of cryptocurrencies. The “fear index”VIX of the United States stock market was selected as a guidance. Analysis of the rela-tionships between VIX and volatility of the underlying assets provide an insight for theselection of a respective proxy. The established VCRIX index provides a daily forecastfor the mean annualized 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 largecapitalized cryptocurrencies, should a smaller capitalized fork be included? Forks are anew concept that poses a challenge to financial engineering. The forking problem is alsoa challenge for the single currency futures contracts.
For millenia, gold has been an accepted store and measure of value, offering very long-term stability and security in the financial marketplace (Erb and Harvey, 2013). Bitcoinand gold are similar from both a psychological perspective and, especially, as a resource.24either 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 invest-igate the potential benefits of bitcoin during extremely volatile market periods. Theyfind that the correlation of extreme returns between bitcoin and US and European equitymarkets increases during stock market drawdowns and decreases during stock marketbooms. Their conclusion is that bitcoin can play an important role in asset managementand 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 usedtogether in turbulent times. That said, we suggest caution in interpreting these resultsgiven 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 crypto-currencies, and capture the cross-sectional expected cryptocurrency returns. By con-sidering a comprehensive list of price- and market-related factors in the stock market,they construct cryptocurrency counterparts. Their cryptocurrency factors claim to besuccessful long-short strategies that generate sizable and statistically significant excessreturns. The paper thus establishes a set of stylized facts on the cross-section of crypto-currencies that can be used to assess and develop theoretical models.
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 concludethat bitcoin-USD prices being a bubble is plausible, but the evidence is inconclusive.In contrast, Henry and Irrera (2017) argue that cryptocurrencies exhibit bubble-likebehavior. Recent research by Hafner (2018), contained in this special issue, extends tra-25itional bubble tests to the case of time-varying volatility. Dong et al. (2018) investigatethe positive and negative outcomes of a cryptocurrency model as risky and costly bubblesin an infinite-horizon production economy with incomplete markets that has the followingframework for bitcoin: 1) enormous volatility, 2) price dynamics are significantly sensitiveto both investor sentiment and policy stances, and 3) the market exhibits diverse cyc-lical features for US and China. Their quantitative results, however, rely heavily on theseverity of the market distortion, i.e. the intervention in the given market by a governingbody, which, in turn, determines the size of the bitcoin bubbles.Shu and Zhu (2019) employ the log-periodic power law singularity (LPPLS) confid-ence indicator as a diagnostic tool for identifying bubbles using the daily data on bitcoinprice. The LPPLS confidence indicator fails to provide effective warnings for detect-ing the bubbles when the bitcoin price suffers from a large fluctuation in a short time,especially for positive bubbles. In order to diagnose the existence of bubbles and accur-ately 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 bubbleburts. 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 weekto 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.
The year 2017 brought a surge in initial coin offerings (ICOs), similar to initial publicofferings (SEC-approved stock offerings). ICO’s are a potentially new financing channelfor entrepreneurs (Cong and He, 2017). The space has also generated a lot of attentionbecause some investors are buying into ICOs without fully understanding the technologyas well as some companies are offering an ICO without an economically meaningful usecase 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,26017a, 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 back-bone are being researched as well. Panwar et al. (2019) research a blockchain-basedcredit 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 creditworthy, with the extended credit proportionate to the amount of trust that exists betweenthe 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 ana-lyses based on their proposed DCN framework.
As we have detailed, there are many different consensus mechanisms. Bitcoin uses a par-ticularly energy-intensive method, which raises environmental concerns, especially withthe prevalence of bitcoin mining dependent on coal-fired power plants in China (Hilemanand 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 theirresearch output regarding decentralized mining in centralized pools. As of April 2018,aggregate energy devoted to bitcoin mining alone exceeded 60 TWh, roughly the annualenergy consumed by Switzerland as a country (Lee, 2018). Mishra et al. (2018) investig-ate how the mining protocol of bitcoin impacts the computing capacity needs of minersand demonstrate, that the mining algorithm as well as the transaction volume increasecomputing resource needs, which in turn raises the energy consumption. Eventually theyargue resource requirements both from a computing hardware and energy consumptionneeds that the future growth of the bitcoin network and the use of bitcoin as a currencycould 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 entirenation 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 cryptocurrencyenergy-usage impacts the performance of energy sector, which emphasises the import-ance of further assessment of environmental impacts of cryptocurrency growth. Block-27hain technology offers a number of innovative environment-related research opportunities(Hayes, 2017; Pop et al., 2018).
Cryptocurrencies are an intriguing financial innovation and offer many possible researchavenues. 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 blockchaintechnology behind the cryptocurrencies. Second, we want to emphasize that there aremany different classes of cryptocurrencies — too often cryptocurrency is summarized asbitcoin. Cryptocurrencies vary, however, and can be tokens representing shares of tra-ditional assets, provide direct utility such as computational power, and even represent afiat 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.
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