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2011.13240#0
Blockchain mechanism and distributional characteristics of cryptos
Blockchain mechanism and distributional characteristics of cryptos∗ Min-Bin Lin†Kainat Khowaja‡Cathy Yi-Hsuan Chen§ Wolfgang Karl H ardle¶ Abstract We investigate the relationship between underlying blockchain mechanism of cryptocurren- cies and its distributional characteristics. In addition to price, we emphasise on using actual block size and block time as the operational features of cryptos.
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We use distributional charac- teristics such as fourier power spectrum, moments, quantiles, global we optimums, as well as the measures for long term dependencies, risk and noise to summarise the information from crypto time series.
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With the hypothesis that the blockchain structure explains the distribu- tional characteristics of cryptos, we use characteristic based spectral clustering to cluster the selected cryptos into ve groups.
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We scrutinise these clusters and nd that indeed, the clus- ters of cryptos share similar mechanism such as origin of fork, diculty adjustment frequency, and the nature of block size.
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This paper provides crypto creators and users with a better un- derstanding toward the connection between the blockchain protocol design and distributional characteristics of cryptos.
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Keywords: Cryptocurrency, price, blockchain mechanism, distributional characteristics, clustering JEL Classi cation: C00 ∗Financial support of the European Union's Horizon 2020 research and innovation program \FIN- TECH: A Finan- cial supervision and Technology compliance training programme" under the grant agreement No 825215 (Topic: ICT- 35-2018, Type of action: CSA), the European Cooperation in Science & Technology COST Action grant CA19130 - Fintech and Arti cial Intelligence in Finance - Towards a transparent nancial industry, the Deutsche Forschungs- gemeinschaft's IRTG 1792 grant, the Yushan Scholar Program of Taiwan and the Czech Science Foundation's grant no.
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19-28231X / CAS: XDA 23020303 are greatly acknowledged.
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†International Research Training Group 1792, Humboldt-Universit at zu Berlin, Spandauer Str.
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1, 10178 Berlin, Germany.
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Email: min-bin.lin@hu-berlin.de ‡International Research Training Group 1792, Humboldt-Universit at zu Berlin, Spandauer Str.
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1, 10178 Berlin, Germany.
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Email: kainat.khowaja@hu-berlin.de §Adam Smith Business School, University of Glasgow, United Kingdom; IRTG 1792 High Dimensional Non Stationary Time Series, Humboldt-Universit at zu Berlin. Email: cathyyi-hsuan.chen@glasgow.ac.uk ¶BRC Blockchain Research Center, Humboldt-Universit at zu Berlin, Berlin, Germany; Sim Kee Boon Institute, Singapore Management University, Singapore; WISE Wang Yanan Institute for Studies in Economics, Xiamen Uni- versity, Xiamen, China; Dept.
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Information Science and Finance, National Chiao Tung University, Hsinchu, Taiwan, ROC; Dept.
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Mathematics and Physics, Charles University, Prague, Czech Republic, Grants{DFG IRTG 1792 grate- fully acknowledged.
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Email: haerdle@hu-berlin.de 1arXiv:2011.13240v2 [cs.CR] 24 Aug 2021 1 Introduction Cryptocurrency (crypto) is a digital asset designed to be as a medium of exchange wherein individual coin ownership is recorded in a digital ledger or computerised database.
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Its creation of monetary units and veri cation of fund transactions are secured using encryption techniques and distributed across several nodes (devices) on a peer-to-peer network.
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Such technology-enhanced and privacy- preserving features make it potentially di erent to other existing nancial instruments and has attracted attention of many investors and researchers (H ardle et al., 2020).
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Many studies have investigated the similarity between a pool of cryptocurrencies in order to classify the important features of digital currencies.
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For example, Blau et al.
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(2020) has concluded that the top sixteen most active cryptocurrencies co-move with bitcoin.
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Researchers have also focused on describing the price behaviour of cryptos using economic factors (Ciaian et al., 2016; Sovbetov, 2018).
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However, owing to the unique technology of cryptocurrencies, there still exists a gap between the creators of blockchain mechanism and users operating the nancial market of the crytocurrencies and through this research, we aim to take a step towards mitigating that gap.
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We specialise our research on the following research questions.
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First, we characterise crypto behaviour using distributional characteristics of time series data.
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Also, instead of using the prices alone, we use actual block time and block size to incorporate the operational features of cryp- tos.
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Second, we hypothesise that the blockchain structure that the coin attaches plays a pivotal role in explaining the behaviour.
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More explicitly, we investigate the extent to which blockchain structure leads to explain the distributional characteristics.
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Using a characteristic based clustering coupled with spectral clustering technique, we group the selected cryptos into a number of clusters and stratify the mechanisms that make the coins within the particular cluster showing the same behaviour in price, actual block time, and actual block size, respectively.
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When studying cryptocurrencies, many researchers only focus on crypto price and daily returns (Trimborn and H ardle, 2018; Hou et al., 2020).
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While price is important when cryptos are used as a medium of payment, it is de nitely not the only measure for evaluation of cryptocurrencies.
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For example, many low price coins are highly traded and many coins that are not used as medium 2 of payment have low prices, e.g., XPR and Dogecoin.
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Cryptos were introduced to serve various purposes and the purpose of the coin does matter.
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This makes it necessary to use other time series while studying crypto markets.
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In this research, we propose to use actual block size and actual block time alongside price.
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Actual block size is the average actual size "usage" of a single block in data storage for one day.
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Since a block comprises of transaction data, it can represent the status of how a blockchain mechanism allocates transactions to a block.
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We consider it a measure of scalability of the system.
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A well-functioning blockchain should be able to level the transaction arrivals.
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Transaction distribution within a day for any crypto needs such balancing because it a ects miners rewards and hence the demand of the coin.
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An ideal block size would keep con rmation times from ballooning while keeping fees and security reasonable.
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Therefore, actual block size of cryptos can provide insight into the behaviour of cryptos.
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Actual block time, on the other hand, measures the consistency and performance of the system.
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It is de ned as the mean time required in minutes for each day to create the next block.
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In other words, it is the average amount of time for the day a user has to wait, after broadcasting their transaction, to see this transaction appear on the blockchain.
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Think of crypto markets as a fast food franchise and miners as customers who have to wait a certain time to make the purchase.
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If the waiting time is shorter on certain days while on other instances, the customers have to wait much longer, there is a discrepancy in the system.
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Analogously, the time series of block time, which is the distribution of waiting time, can be seen as a service level of the whole system and it is necessary to maintain as the users' expectation or target block time set by the system depend on it.
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The idea of investigating the underlying blockchain mechanism, a cornerstone of crypto technol- ogy, and its connection to the crypto behaviour is still in its infancy.
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One of the rst endeavours in explaining this relationship was made by Guo et al.
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(2018) who highlight that the the fundamental characteristics of cryptocurrencies (e.g., algorithm and proof type) have a vital role in di eren- tiating the performance of cryptocurrencies.
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They develop a spectral clustering methodology to 3 group cryptos in a dynamic fashion, but their research is limited in the exploitation of blockchain characteristics.
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With a similar spirit, Iwamura et al.
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(2019) start by claiming that high uctuation is a re ection of the lack of exibility in the Bitcoin supply schedule.
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They further strengthen their arguments by considering the predetermined algorithm of cryptos (speci cally, the proof of work) to explain the volatility in cryptocurrency market.
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Zimmerman (2020) argue in their work that the higher congestion in blockchain technology leads to higher volatility in crypto prices.
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They claim that the limited settlement space in blockchain architecture makes users compete with one another, a ecting the demand.
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In his model, the value of cryptos is governed by its demand, making the price sensitive to blockchain capacity.
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These research results, albeit true, are limited to a particular set of cryptocurrency mechanism and do not thoroughly explain the dynamics of cryptocurrencies.
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Also, most of the papers only use price as a proxy of behaviour.
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We advance the previous ndings by incorporating a rich set of underlying mechanisms and connecting them to multiple time series.
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We take a deep dive into eighteen cryptos with a variety of mechanisms- concluded in Garriga et al.
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(2020))- from a technical perspective to summarise their mechanism and algorithm designs using variables, such as consensus algorithm, type of hashing algorithm, diculty adjustment frequency and so on.
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We investigate a relationship between underlying blockchain mechanism of cryptocurrencies and the distributional characteristics.
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Using the a characteristic-based clustering technique, we cluster the selected coins into a number of clusters and scrutinise the compositions of fundamental characteristics in each group.
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We observe that the clusters obtained from these time series indeed share common underlying mechanism.
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Through empirical evidence, we show that the cryptos forked from same origin and same consensus mechanism tend to become part of same clustering group.
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Furthermore, the clusters obtained by the time series of block time have same hashing algorithms and diculty adjustment algorithms.
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Also, a similar nature (static or dynamic) of block size was observed within clusters obtained by the time series of actual block size.
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We conclude with empirical evidence that the crypto behaviour is actually linked with their blockchain protocol architectures.
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The implications of this study are abundant.
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The creators of cryptocurrencies can manage the 4 impact of blockchain underlying mechanisms on the corresponding distributional characteristics, in a consideration of adoption rate of invented coins.
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From the users' perspective, they can make an optimal decision in which coins should be adopted while concerning the price uctuation.
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This paper proceeds as follows.
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Section 2 discusses data source and the underlying mechanisms of the cryptos.
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Section 3 presents the methodology used for classifying characteristics of time series and clustering algorithm.
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Section 4 provides an illustration of analysis results.
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Section 5 concludes and provides several avenues for future research.
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2 Data Source and Description According to CoinMarketCap (https://coinmarketcap.com), currently there are over 7,000 cryp- tocurrencies and their total market capitalisation has surpassed USD $400 billion as of November 09, 2020.
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Most of studies have focused on the mainstream coins (e.g., Bitcoin, Ethereum), and little has been investigated on the coins which have been introduced and featured with a diverse blockchain mechanisms and invented technologies.
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The work of Guo and Donev (2020) is one of exceptions.
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In this study, 18 cryptos with di erent set of blockchain mechanisms have been ex- amined {Bitcoin, Bitcoin Cash, Bitcoin Gold, Bitcoin SV, Blackcoin, Dash, Dogecoin, Ethereum, Ethereum Classic, Feathercoin, Litecoin, Monero, Novacoin, Peercoin, Reddcoin, Vertcoin, XRP (Ripple), and Zcash.
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We explore an interplay between distributional characteristics of crpytos and blockchain mechanism.
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We discuss the key characteristics of blockchain mechanisms and the time series data in this section.
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2.1 Underlying Mechanism Most of cryptos nowadays apply blockchain-based systems in which transactions are grouped into blocks and cryptographically interlinked to form a back-linked list of blocks containing transactions.
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The transactions are validated using the nodes within the crypto peer-to-peer network through a majority consensus directed by algorithms instead of a central authority's approval.
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In such an operation process, many algorithmic mechanisms are required to govern the performance and 5 outcome of a crypto system.
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Some key blockchain-based characteristics are discussed below: Fork: It occurs as user base or developers conduct a fundamental or signi cant software change, see as in Figure 1.
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There are two types of forks { soft and hard forks.
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The former is an update to the protocol architecture and then all the nodes are enforced to follow in order to proceed with the operations of a crypto.
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The latter one creates a duplicate copy of the origin blockchain and modi es the copy to meet the desired quality (e.g., safety, scalability).
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In this case, a new crypto can be generated accordingly.
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For example, Peercoin network facilitates an alternative consensus mechanism {proof-of-stake (PoS) to Bitcoin's proof-of-work (PoW) system for reducing dependency on energy consumption from mining process (King and Nadal, 2012).
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Going beyond a digital currency, Ethereum establishes an open-ended decentralised platform for diverse applications such as decentralised applications (dapps) and smart contracts (Buterin, 2014).
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Consensus mechanism: In order to establish an agreement on a speci c subset of the candi- date transactions, consensus mechanism provides a protocol for a large number of trust-less nodes in a decentralised blockchain network.
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For instance, PoW (Proof-of-Work, as adopted by e.g., Bitcoin, Litecoin) achieves consensus with a competition among miners on solving computational puzzles, which consume numerous computational resources; and PoS (Proof-of-Stake, as adopted by e.g., Peercoin, Blackcoin) randomly assigns a block creator (transaction validator) with probability proportional to their coins staked.
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Hashing algorithm: It is a mathematical algorithm that encrypts a new transaction (or a new block) into a xed length character string, known as hash value, and later interlinks this string with a given blockchain to ensure the security and immutability of a crypto.
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Various hashing algorithms are implemented in cryptos such as SHA-256, Scrypt and Equihash.
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These provide di erent degree of complexity to blockchain operations.
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Diculty adjustment algorithm: It is an adaptive mechanism which periodically adjusts the diculty toward hashrate to target an average time interval between blocks, known as target block 6 Figure 1: Blockchain software forks in cryptocurrency.
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time or target con rmation time.
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