Existing cryptocurrencies are too volatile to be used as currencies for daily payments. Stable coins, which are cryptocurrencies pegged to other stable financial assets such as the U.S. dollar, are desirable for payments within blockchain networks, whereby being often called the “Holy Grail of cryptocurrency.” By using the option pricing theory and the Ethereum platform that allows running smart contracts, we design several dual-class structures that are written on the ETH cryptocurrency and offer a fixed income crypto asset (class A coin), a stable coin (class A′ coin) pegged to a traditional currency, and leveraged investment instruments (class B and B′ coins). Our investigation of the values of stable coins in presence of jump risk and black-swan type events shows the robustness of the design. The design has been implemented on the Ethereum platform.
{"title":"Designing Stable Coins","authors":"Yizhou Cao, M. Dai, S. Kou, Lewei Li, Chen Yang","doi":"10.2139/ssrn.3856569","DOIUrl":"https://doi.org/10.2139/ssrn.3856569","url":null,"abstract":"Existing cryptocurrencies are too volatile to be used as currencies for daily payments. Stable coins, which are cryptocurrencies pegged to other stable financial assets such as the U.S. dollar, are desirable for payments within blockchain networks, whereby being often called the “Holy Grail of cryptocurrency.” By using the option pricing theory and the Ethereum platform that allows running smart contracts, we design several dual-class structures that are written on the ETH cryptocurrency and offer a fixed income crypto asset (class A coin), a stable coin (class A′ coin) pegged to a traditional currency, and leveraged investment instruments (class B and B′ coins). Our investigation of the values of stable coins in presence of jump risk and black-swan type events shows the robustness of the design. The design has been implemented on the Ethereum platform.","PeriodicalId":385335,"journal":{"name":"Cryptocurrencies eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115216647","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
XRP is a modern crypto-asset (crypto-currency) developed by Ripple Labs, which has been increasing its financial presence. We study its transaction history available as ledger data. An analysis of its basic statistics, correlations, and network properties are presented. Motivated by the behavior of some nodes with histories of large transactions, we propose a new index: the ``Flow Index.'' The Flow Index is a pair of indices suitable for characterizing transaction frequencies as a source and destination of a node. Using this Flow Index, we study the global structure of the XRP network and construct bow-tie/walnut structure.
{"title":"XRP Network and Proposal of Flow Index","authors":"H. Aoyama","doi":"10.7566/JPSCP.36.011003","DOIUrl":"https://doi.org/10.7566/JPSCP.36.011003","url":null,"abstract":"XRP is a modern crypto-asset (crypto-currency) developed by Ripple Labs, which has been increasing its financial presence. We study its transaction history available as ledger data. An analysis of its basic statistics, correlations, and network properties are presented. Motivated by the behavior of some nodes with histories of large transactions, we propose a new index: the ``Flow Index.'' The Flow Index is a pair of indices suitable for characterizing transaction frequencies as a source and destination of a node. Using this Flow Index, we study the global structure of the XRP network and construct bow-tie/walnut structure.","PeriodicalId":385335,"journal":{"name":"Cryptocurrencies eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125137094","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study explores whether the technical analysis based on moving average indicator can predict Bitcoin returns during January 2014 and October 2019. First, we find that Bitcoin weekly returns are well predictable by the technical indicator defined as the difference between the log moving averages and log current price in both in-sample and out-of-sample tests. However, the return predictability is not significant in daily frequency. We further show that the term structure of moving-average indicator provides significantly predictive power to Bitcoin weekly returns, especially for the lower correlated moving-average indicators.
{"title":"Does Moving-average Indicators Work Well on the Dynamic of Bitcoin Prices","authors":"Kuang-Chieh Yen, Yu-Li Lin, Wei-Ying Nie","doi":"10.2139/ssrn.3836454","DOIUrl":"https://doi.org/10.2139/ssrn.3836454","url":null,"abstract":"This study explores whether the technical analysis based on moving average indicator can predict Bitcoin returns during January 2014 and October 2019. First, we find that Bitcoin weekly returns are well predictable by the technical indicator defined as the difference between the log moving averages and log current price in both in-sample and out-of-sample tests. However, the return predictability is not significant in daily frequency. We further show that the term structure of moving-average indicator provides significantly predictive power to Bitcoin weekly returns, especially for the lower correlated moving-average indicators.","PeriodicalId":385335,"journal":{"name":"Cryptocurrencies eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117334729","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
New cryptocurrencies are emerging daily, providing alternatives to traditional forms of payments and enabling new mediums of exchange such as cash. These currencies span Bitcoin, Litecoin and Etherium. In less than a decade, bitcoin has gone from being an obscure curiosity to a household name. In recent times, bitcoin has risen – with ups and downs – from a few cents per coin to over $4,000. In the meantime, hundreds of other cryptocurrencies – equalling bitcoin in market value – have emerged (Graph 1, left-hand panel). While it seems unlikely that bitcoin or its sisters will displace sovereign currencies, they have demonstrated the viability of the underlying blockchain or distributed ledger technology (DLT).
Venture capitalists and financial institutions are investing heavily in DLT projects that seek to provide new financial services and deliver old ones more efficiently. Bloggers, central bankers, and academics predict transformative or disruptive implications for payments, banks, and the financial system at large.
Findings from Andolfatto (2015, 2016), Broadbent (2016), Raskin and Yermack (2016) and Skingsley (2016) attest to this sea-change in mediums of exchange as consumers. Transformative implications for economies, financial systems and consumer investment behaviour is already changing, reflecting a macroeconomic backdrop characterised by ultra-accommodative interest rates, low economic growth and a dearth of investment opportunities with over 20% of global debt yielding negative returns.
The transformative changes likely embedded in the increased use and &adoption of cryptocurrencies across economies suggest a need to understand the nature of cryptocurrencies better.
{"title":"African Economies and the Rise of Crypt-Currencies","authors":"Professor Kelly Kingsly","doi":"10.2139/ssrn.3792082","DOIUrl":"https://doi.org/10.2139/ssrn.3792082","url":null,"abstract":"New cryptocurrencies are emerging daily, providing alternatives to traditional forms of payments and enabling new mediums of exchange such as cash. These currencies span Bitcoin, Litecoin and Etherium. In less than a decade, bitcoin has gone from being an obscure curiosity to a household name. In recent times, bitcoin has risen – with ups and downs – from a few cents per coin to over $4,000. In the meantime, hundreds of other cryptocurrencies – equalling bitcoin in market value – have emerged (Graph 1, left-hand panel). While it seems unlikely that bitcoin or its sisters will displace sovereign currencies, they have demonstrated the viability of the underlying blockchain or distributed ledger technology (DLT). <br><br>Venture capitalists and financial institutions are investing heavily in DLT projects that seek to provide new financial services and deliver old ones more efficiently. Bloggers, central bankers, and academics predict transformative or disruptive implications for payments, banks, and the financial system at large. <br><br>Findings from Andolfatto (2015, 2016), Broadbent (2016), Raskin and Yermack (2016) and Skingsley (2016) attest to this sea-change in mediums of exchange as consumers. Transformative implications for economies, financial systems and consumer investment behaviour is already changing, reflecting a macroeconomic backdrop characterised by ultra-accommodative interest rates, low economic growth and a dearth of investment opportunities with over 20% of global debt yielding negative returns. <br><br>The transformative changes likely embedded in the increased use and &adoption of cryptocurrencies across economies suggest a need to understand the nature of cryptocurrencies better.<br>","PeriodicalId":385335,"journal":{"name":"Cryptocurrencies eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122077411","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We investigate the relationship between underlying blockchain mechanism of cryptocurrencies and its distributional characteristics. In addition to price, we emphasise on using actual block size and block time as the operational features of cryptos. We use distributional characteristics 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. With the hypothesis that the blockchain structure explains the distributional characteristics of cryptos, we use characteristic based spectral clustering to cluster the selected cryptos into five groups. We scrutinise these clusters and find that indeed, the clusters of cryptos share similar mechanism such as origin of fork, difficulty adjustment frequency, and the nature of block size. This paper provides crypto creators and users with a better understanding toward the connection between the blockchain protocol design and distributional characteristics of cryptos.
{"title":"Blockchain Mechanism and Distributional Characteristics of Cryptos","authors":"Min-Bin Lin, Kainat Khowaja, C. Chen, W. Härdle","doi":"10.2139/ssrn.3784776","DOIUrl":"https://doi.org/10.2139/ssrn.3784776","url":null,"abstract":"We investigate the relationship between underlying blockchain mechanism of cryptocurrencies and its distributional characteristics. In addition to price, we emphasise on using actual block size and block time as the operational features of cryptos. We use distributional characteristics 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. With the hypothesis that the blockchain structure explains the distributional characteristics of cryptos, we use characteristic based spectral clustering to cluster the selected cryptos into five groups. We scrutinise these clusters and find that indeed, the clusters of cryptos share similar mechanism such as origin of fork, difficulty adjustment frequency, and the nature of block size. This paper provides crypto creators and users with a better understanding toward the connection between the blockchain protocol design and distributional characteristics of cryptos.","PeriodicalId":385335,"journal":{"name":"Cryptocurrencies eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128117032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-07-04DOI: 10.1108/SEF-10-2018-0313
Ikhlaas Gurrib
Purpose The purpose of this paper is to shed fresh light into whether an energy commodity price index (ENFX) and energy blockchain-based crypto price index (ENCX) can be used to predict movements in the energy commodity and energy crypto market. Design/methodology/approach Using principal component analysis over daily data of crude oil, heating oil, natural gas and energy based cryptos, the ENFX and ENCX indices are constructed, where ENFX (ENCX) represents 94% (88%) of variability in energy commodity (energy crypto) prices. Findings Natural gas price movements were better explained by ENCX, and shared positive (negative) correlations with cryptos (crude oil and heating oil). Using a vector autoregressive model (VAR), while the 1-day lagged ENCX (ENFX) was significant in estimating current ENCX (ENFX) values, only lagged ENCX was significant in estimating current ENFX. Granger causality tests confirmed the two markets do not granger cause each other. One standard deviation shock in ENFX had a negative effect on ENCX. Weak forecasting results of the VAR model, support the two markets are not robust forecasters of each other. Robustness wise, the VAR model ranked lower than an autoregressive model, but higher than a random walk model. Research limitations/implications Significant structural breaks at distinct dates in the two markets reinforce that the two markets do not help to predict each other. The findings are limited by the existence of bubbles (December 2017-January 2018) which were witnessed in energy blockchain-based crypto markets and natural gas, but not in crude oil and heating oil. Originality/value As per the authors’ knowledge, this is the first paper to analyze the relationship between leading energy commodities and energy blockchain-based crypto markets.
{"title":"Can Energy Commodities Affect Energy Blockchain-Based Cryptos?","authors":"Ikhlaas Gurrib","doi":"10.1108/SEF-10-2018-0313","DOIUrl":"https://doi.org/10.1108/SEF-10-2018-0313","url":null,"abstract":"\u0000Purpose\u0000The purpose of this paper is to shed fresh light into whether an energy commodity price index (ENFX) and energy blockchain-based crypto price index (ENCX) can be used to predict movements in the energy commodity and energy crypto market.\u0000\u0000\u0000Design/methodology/approach\u0000Using principal component analysis over daily data of crude oil, heating oil, natural gas and energy based cryptos, the ENFX and ENCX indices are constructed, where ENFX (ENCX) represents 94% (88%) of variability in energy commodity (energy crypto) prices.\u0000\u0000\u0000Findings\u0000Natural gas price movements were better explained by ENCX, and shared positive (negative) correlations with cryptos (crude oil and heating oil). Using a vector autoregressive model (VAR), while the 1-day lagged ENCX (ENFX) was significant in estimating current ENCX (ENFX) values, only lagged ENCX was significant in estimating current ENFX. Granger causality tests confirmed the two markets do not granger cause each other. One standard deviation shock in ENFX had a negative effect on ENCX. Weak forecasting results of the VAR model, support the two markets are not robust forecasters of each other. Robustness wise, the VAR model ranked lower than an autoregressive model, but higher than a random walk model.\u0000\u0000\u0000Research limitations/implications\u0000Significant structural breaks at distinct dates in the two markets reinforce that the two markets do not help to predict each other. The findings are limited by the existence of bubbles (December 2017-January 2018) which were witnessed in energy blockchain-based crypto markets and natural gas, but not in crude oil and heating oil.\u0000\u0000\u0000Originality/value\u0000As per the authors’ knowledge, this is the first paper to analyze the relationship between leading energy commodities and energy blockchain-based crypto markets.\u0000","PeriodicalId":385335,"journal":{"name":"Cryptocurrencies eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133137155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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