The emergence of financial technology in the last 10 years has created a new type of asset that is Cryptocurrency. Cryptocurreny offers a small transaction fee without involving a third party in its transaction and the ability to make its users anonymous. It became one of its main selling points and was quickly accepted widely in the financial world. Cryptocurrency price movements become volatile. For examples, Bitcoin issued in 2009, the value is not more than USD 10, but in early June 2017, Bitcoin is worth about USD 3000 (Bloomberg, July 5th, 2017). Many investors are interested to invest in Cryptocurrency, especially investors with high risk tolerance. This study aims to find the effects of Cryptocurrency on well-formed portfolios. The assets we use are Foreign Currency, Commodity, Stock, and ETF. The Cryptocurrency we will use is Bitcoin, Ripple and Litecoin. Using the Modern Portfolio Theory approach, we can create an investment portfolio. The results show that the portfolio with Cryptocurrency indeed increases the effectiveness of the portfolio in two ways. The first is to minimize the standard deviation and the second is to create more allocation options for investors to choose from. The optimum allocation of Cryptocurrency is from 5% to 20% depending on the risk tolerance of the investor.
The world of money and finance is transforming before our eyes. Digitised assets and innovative financial channels, instruments and systems are creating new paradigms for financial transaction and forging alternative conduits of capital. The Cambridge Centre for Alternative Finance, since its founding in 2015, has been at the forefront of documenting, analysing and indeed critically challenging that digital financial transformation.
This Global Cryptocurrency Benchmarking Study is our inaugural research focused on alternative payment systems and digital assets. Led by Dr Garrick Hileman, it is the first study of its kind to holistically examine the burgeoning global cryptocurrency industry and its key constituents, which include exchanges, wallets, payments and mining.
The findings are both striking and thought-provoking. First, the user adoption of various cryptocurrencies has really taken off, with billions in market cap and millions of wallets estimated to have been ‘active’ in 2016. Second, the cryptocurrency industry is both globalised and localised, with borderless exchange operations, as well as geographically clustered mining activities. Third, the industry is becoming more fluid, as the lines between exchanges and wallets are increasingly ‘blurred’ and a multitude of cryptocurrencies, not just bitcoin, are now supported by a growing ecosystem, fulfilling an array of functions. Fourth, issues of security and regulatory compliance are likely to remain prevalent for years to come.
I hope this study will provide value to academics, practitioners, policymakers and regulators alike. We thank Visa very much for its generous support of independent academic research in this important area.
The aim of this chapter is to situate blockchain-based governance tools within a public value (PV) context, arguing that public governance on the blockchain requires elements of a “public value mindset,” which is comprised of transparency, efficiency, stakeholder salience and participation, innovative thinking, decentralization, and trust. To illustrate this PV mindset, the chapter analogizes the powerful solution put forth by cryptocurrencies towards the “double spending problem,” an issue that had once plagued electronic money, by arguing that the manner in which the double-spending problem was solved by blockchain technology (as best embodied by cryptocurrencies) also offers lessons for public governance on the blockchain, specifically in terms of the PV elements enumerated above. By elucidating the nature of the double-spending problem and the revolutionary solution offered by cryptocurrencies, the chapter advocates for a similar approach in the application of blockchain technologies to public management and governance.
Stefan Dziembowski, Lisa Eckey, Sebastian Faust, Daniel Malinowski
Payment channels emerged recently as an efficient method for performing cheap micropayments in cryptocurrencies. In contrast to traditional on-chain transactions, payment channels have the advantage that they allow for nearly unlimited number of transactions between parties without involving the blockchain. In this work, we introduce Perun, an off-chain channel system that offers a new method for connecting channels that is more efficient than the existing technique of ``routing transactions'' over multiple channels. To this end, Perun introduces a technique called ``virtual payment channels'' that avoids involvement of the intermediary for each individual payment. In this paper we formally model and prove security of this technique in the case of one intermediary, who can be viewed as a ``payment hub'' that has direct channels with several parties. Our scheme works over any cryptocurrency that provides Turing-complete smart contracts. As a proof of concept, we implemented Perun's smart contracts in Ethereum.
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Blockchain Technology Applications and Security
Cryptography and Data Security
Advanced Steganography and Watermarking Techniques
Bitcoin was the first cryptocurrency to use blockchain and has been the market leader since the first bitcoin was mined in 2009. After the birth of Bitcoin with the genesis block, more than 1,000 altcoins and crypto-tokens have been created, with at least 919 trading actively on unregulated or registered exchanges. This entire class of cryptocurrencies and tokens has been classified by some tax authorities as having the same status as commodities. If cryptocurrency is viewed in the same class as commodities, how different is it in terms of its risk and return structure? This article sets out to help readers understand cryptocurrencies and to explore their risk and return characteristics using a portfolio of cryptocurrency represented by the Cryptocurrency Index (CRIX). Substantial discussions are centered on Bitcoin and its close variants. Some questions are raised about the potential of cryptocurrencies as an investment class. Results show that the return correlations between cryptocurrencies and traditional assets are low and that adding CRIX returns to a traditional asset portfolio improves risk–return performance. Sentiment analysis also indicates the CRIX has a relatively high Sharpe ratio. Although we should view the results with care, a new form of financing for cryptocurrency and blockchain start-ups is born. The disruption brought about by Bitcoin may be felt beyond payments through what is known as initial crypto-token offerings or initial token sales. <b>TOPICS:</b>Currency, risk management, performance measurement, mutual funds/passive investing/indexing
Guglielmo Maria Caporale, Luis A. Gil‐Alana, Alex Plastun
This paper examines persistence in the cryptocurrency market. Two different long-memory methods (R/S analysis and fractional integration) are used to analyse it in the case of the four main cryptocurrencies (BitCoin, LiteCoin, Ripple, Dash) over the sample period 2013–2017. The findings indicate that this market exhibits persistence (there is a positive correlation between its past and future values), and that its degree changes over time. Such predictability represents evidence of market inefficiency: trend trading strategies can be used to generate abnormal profits in the cryptocurrency market.
The first global cryptocurrency benchmarking study presents a systematic and comprehensive picture of a rapidly evolving industry, illustrating how cryptocurrencies are being used, stored, transacted and mined. The study gathered non-public data from more than 100 cryptocurrency companies and over 30 individual cryptocurrency miners in 38 countries around the world via secure web-based questionnaires, capturing an estimated 75 per cent of the cryptocurrency industry. The study breaks down the cryptocurrency industry into four key sectors – exchanges, wallets, payments and mining. Key findings and highlights from the study include our estimate that over three million unique individuals are actively using cryptocurrency today, data on regulation and compliance practices and costs at firms, and a global map of cryptocurrency mining.
Ethereum represents the second generation of blockchain technology by providing\nan open and global computing platform which allows the exchange of cryptocurrency\n(Ether) and the development of self-verifying smart contract applications.\nSmart contracts present a foundation for possessing digital assets and a variety of\ndecentralized applications within the blockchain area. Ethereum and smart contracts\nare public, distributed and immutable, as such, they are prone to vulnerabilities\nsourcing from simple coding mistakes of developers.\n\nMotivated by the security breaches and recurring financial losses in smart contracts,\nwe aim to advance the field of security in smart contract programming.\nThe main objective is to aid smart contract developers by providing a taxonomy of\nall known security issues and by inspecting the security code analysis tools used\nto identify those vulnerabilities. Based on previous research as well as attacks on\nEthereum smart contracts, we propose an updated taxonomy which categorizes\nall known vulnerabilities within their architectural and severity level. Our second\nproposed taxonomy is a novel categorization of security tools on Ethereum.\n\nFurthermore, we conduct the investigation of security code analysis tools on\nEthereum by assessing their effectiveness and accuracy. In particular, we analyze\nfour security tools, namely, Oyente, Securify, Remix, and SmartCheck. The results\nindicate that there are overall inconsistencies between the tools on different security\nproperties. SmartCheck outperformed the other tools in terms of effectiveness,\nwhereas Oyente performed the best in terms of accuracy. Furthermore, based on\nthe limitations we identified, we propose future improvements within the user interfaces,\ninterpretation of results, and additional vulnerability checks.
The gas mechanism in Ethereum charges the execution of every operation to ensure that smart contracts running in EVM (Ethereum Virtual Machine) will be eventually terminated. Failing to properly set the gas costs of EVM operations allows attackers to launch DoS attacks on Ethereum. Although Ethereum recently adjusted the gas costs of EVM operations to defend against known DoS attacks, it remains unknown whether the new setting is proper and how to configure it to defend against unknown DoS attacks. In this paper, we make the first step to address this challenging issue by first proposing an emulation-based framework to automatically measure the resource consumptions of EVM operations. The results reveal that Ethereum's new setting is still not proper. Moreover, we obtain an insight that there may always exist exploitable under-priced operations if the cost is fixed. Hence, we propose a novel gas cost mechanism, which dynamically adjusts the costs of EVM operations according to the number of executions, to thwart DoS attacks. This method punishes the operations that are executed much more frequently than before and lead to high gas costs. To make our solution flexible and secure and avoid frequent update of Ethereum client, we design a special smart contract that collaborates with the updated EVM for dynamic parameter adjustment. Experimental results demonstrate that our method can effectively thwart both known and unknown DoS attacks with flexible parameter settings. Moreover, our method only introduces negligible additional gas consumption for benign users.
Ponzi schemes are financial frauds which lure users under the promise of high\nprofits. Actually, users are repaid only with the investments of new users\njoining the scheme: consequently, a Ponzi scheme implodes soon after users stop\njoining it. Originated in the offline world 150 years ago, Ponzi schemes have\nsince then migrated to the digital world, approaching first the Web, and more\nrecently hanging over cryptocurrencies like Bitcoin. Smart contract platforms\nlike Ethereum have provided a new opportunity for scammers, who have now the\npossibility of creating "trustworthy" frauds that still make users lose money,\nbut at least are guaranteed to execute "correctly". We present a comprehensive\nsurvey of Ponzi schemes on Ethereum, analysing their behaviour and their impact\nfrom various viewpoints.\n
Conrad Burchert, Christian Decker, Roger Wattenhofer
The Bitcoin network has scalability problems. To increase its transaction rate and speed, micropayment channel networks have been proposed; however, these require to lock funds into specific channels. Moreover, the available space in the blockchain does not allow scaling to a worldwide payment system. We propose a new layer that sits in between the blockchain and the payment channels. The new layer addresses the scalability problem by enabling trustless off-blockchain channel funding. It consists of shared accounts of groups of nodes that flexibly create one-to-one channels for the payment network. The new system allows rapid changes of the allocation of funds to channels and reduces the cost of opening new channels. Instead of one blockchain transaction per channel, each user only needs one transaction to enter a group of nodes-within the group the user can create arbitrarily many channels. For a group of 20 users with 100 intra-group channels, the cost of the blockchain transactions is reduced by 90% compared to 100 regular micropayment channels opened on the blockchain. This can be increased further to 96% if Bitcoin introduces Schnorr signatures with signature aggregation.
Bitcoin as concept was coined in 2009 and can be described as a partly open and shared transactional database. What makes bitcoin unique is that for the first time, we can prove and move ownership of anything digital without a central authority. The technology facilitates many benefits, one being a worldwide, digital currency and we observe that some stores allow payments in bitcoin. Drawing on concepts from the Diffusion of Innovation theory we investigated: What are the end-users’ motivations and barriers for using bitcoin as digital currency? Through a small survey, we collected 135 answers during the summer of 2016. Our findings include that the bitcoin users embrace bitcoin due to technological curiosity, thus an individual reason. The largest group, the non-users, state that they are awaiting for others to start using bitcoin, as they question the value and security issues. We conclude that we may witness a deadlock where “everybody waits for everybody”, and that more research is needed.
On-line commercial transactions involve an inherent mistrust between participant parties since, sometimes, no previous relation exists between them. Such mistrust may be a deadlock point in a trade transaction where the buyer does not want to perform the payment until the seller sends the goods and the seller does not want to do so until the buyer pays for the purchase. In this paper we present a fair protocol for data trading where the commercial deal, in terms of delivering the data and performing the payment, is atomic, since the seller cannot redeem the payment unless the buyer obtains the data and the buyer cannot obtain the data without performing the payment. The protocol is based on Bitcoin scripting language and the fairness of the protocol can be probabilistically enforced.
Bitcoin has the largest share in the total capitalization of cryptocurrency markets currently reaching above 70 billion USD. In this work we focus on the price of Bitcoin in terms of standard currencies and their volatility over the last five years. The average day-to-day return throughout this period is 0.328%, amounting in exponential growth from 6 USD to over 4,000 USD per 1 BTC at present. Multi-scale analysis is performed from the level of the tick data, through the 5 min, 1 hour and 1 day scales. Distribution of trading volumes (1 sec, 1 min, 1 hour and 1 day) aggregated from the Kraken BTCEUR tick data is provided that shows the artifacts of algorithmic trading (selling transactions with volume peaks distributed at integer multiples of BTC unit). Arbitrage opportunities are studied using the EUR, USD and CNY currencies. Whereas the arbitrage spread for EUR-USD currency pair is found narrow at the order of a percent, at the 1 hour sampling period the arbitrage spread for USD-CNY (and similarly EUR-CNY) is found to be more substantial, reaching as high as above 5 percent on rare occasions. The volatility of BTC exchange rates is modeled using the day-to-day distribution of logarithmic return, and the Realized Volatility, sum of the squared logarithmic returns on 5-minute basis. In this work we demonstrate that the Heterogeneous Autoregressive model for Realized Volatility Andersen et al. (2007) applies reasonably well to the BTCUSD dataset. Finally, a feed-forward neural network with 2 hidden layers using 10-day moving window sampling daily return predictors is applied to estimate the next-day logarithmic return. The results show that such an artificial neural network prediction is capable of approximate capture of the actual log return distribution; more sophisticated methods, such as recurrent neural networks and LSTM (Long Short Term Memory) techniques from deep learning may be necessary for higher prediction accuracy.
Luuc Van Der Horst, Kim‐Kwang Raymond Choo, Nhien‐An Le‐Khac
Bitcoin cryptocurrency is reportedly one widely used digital currency in criminal activities (e.g. used for online purchases of illicit drugs and paying of ransom in ransomware cases). However, there has been limited forensic research of bitcoin clients in the literature. In this paper, the process memory of two popular bitcoin clients, bitcoin Core and electrum, is examined with the aims of identifying potential sources and types of potential relevant data (e.g. bitcoin keys, transaction data and passphrases). Artefacts obtained from the process memory are also studied with other artefacts obtained from the client device (application files on disk and memory-mapped files and registry keys). Findings from this study suggest that both bitcoin Core and electrum's process memory is a valuable source of evidence, and many of the artefacts found in process memory are also available from the application and wallet files on the client device (disk).
The Bitcoin protocol allows to save arbitrary data on the blockchain through a special instruction of the scripting language, called OP_RETURN. A growing number of protocols exploit this feature to extend the range of applications of the Bitcoin blockchain beyond transfer of currency. A point of debate in the Bitcoin community is whether loading data through OP_RETURN can negatively affect the performance of the Bitcoin network with respect to its primary goal. This paper is an empirical study of the usage of OP_RETURN over the years. We identify several protocols based on OP_RETURN, which we classify by their application domain. We measure the evolution in time of the usage of each protocol, the distribution of OP_RETURN transactions by application domain, and their space consumption.
The meteoric rise of Bitcoin has led to heightened investment, academic, commercial, numismatic, transactional, and practitioner interest in that cryptocurrency, as well as in the growing array of such instruments worldwide. This leads to an accentuated need for an examination of the historical evolution of Bitcoin as the seminal instrument in the development of cryptocurrencies, and this discussion paper seeks to address that gap.
Christian Badertscher, Ueli Maurer, Daniel Tschudi, Vassilis Zikas
Abstract Bitcoin is one of the most prominent examples of a distributed cryptographic protocol that is extensively used in reality. Nonetheless, existing security proofs are property-based, and as such they do not support composition. In this work, we put forth a universally composable treatment of the Bitcoin protocol. We specify the goal that Bitcoin aims to achieve as an instance of a parameterizable ledger functionality and present a UC abstraction of the Bitcoin blockchain protocol. Our ideal functionality is weaker than the first proposed candidate by Kiayias, Zhou, and Zikas [EUROCRYPT’16], but unlike the latter suggestion, which is arguably not implementable by the UC Bitcoin protocol, we prove that the one proposed here is securely UC-realized by the protocol assuming access to a global clock, to model time-based executions, a random oracle, to model hash functions, and an idealized network, to model message dissemination. We further show how known property-based approaches can be cast as special instances of our treatment and how their underlying assumptions can be cast in UC as part of the setup functionalities and without restricting the environment or the adversary.