At the beginning of the 2020 global COVID-2019 pandemic, Chinese financial markets acted as the epicentre of both physical and financial contagion. Our results indicate that a number of characteristics expected during a "flight to safety" were present during the period analysed. The volatility relationship between the main Chinese stock markets and Bitcoin evolved significantly during this period of enormous financial stress. We provide a number of observations as to why this situation occurred. Such dynamic correlations during periods of stress present further evidence to cautiously support the validity of the development of this new financial product within mainstream portfolio design through the diversification benefits provided.
For users of the Ethereum network, the gas price is a crucial parameter that determines how swiftly the decentralized consensus protocol confirms a transaction. This paper studies the statistics of the Ethereum gas price. We start with some conceptual discussion of the gas price notion in view of the actual transaction-selection strategies used by Ethereum miners. Subsequently, we provide the descriptive statistics of what we call the threshold gas price. Finally, we identify and estimate a seasonal ARIMA (SARIMA) model for predicting the hourly median of the threshold gas price.
Ethereum accelerates the transaction process through a quicker block creation design. Since the time interval between the generation of blocks is very short (about 15s), block propagation time in an inefficient network is not negligible compared with the block time interval. This lead to the production of a large number of orphan blocks. In order to solve the security problems that may be caused by the orphan block and improve the transaction processing efficiency, Ethereum introduces the uncle block mechanism, i.e., an orphan block may get part of minted reward if it gets a reference by a regular block. In this paper, we show the weakness of the uncle block mechanism. Firstly, we describe the specific differences of Ethereum selfish and stubborn mining in every state from the ones in Bitcoin. Secondly, we simulate possible attacks, and the results show that the Ethereum selfish and stubborn mining strategies not only increase the reward of an attacker but also decrease the security threshold. The security threshold refers to the proportion of the attacker's computational power that needs to be achieved in order to obtain a higher reward than he should. In a practical network congestion rate, the security threshold are weakened to 0.129 and 0.216 against the Lead stubborn mining strategy and the original selfish mining strategy, respectively. When the congestion rate is rising, the reward is increasing and the threshold is decreasing. Thirdly, possible strategies are evaluated to find out the optimal one in different settings. Fourthly, we also extend the evaluation by combining three eclipse attack strategies with selfish or stubborn mining. Most of combinations bring more advantages to an attacker than a single strategy.
We report our experience in the formal verification of the deposit smart contract, whose correctness is critical for the security of Ethereum 2.0, a new Proof-of-Stake protocol for the Ethereum blockchain. The deposit contract implements an incremental Merkle tree algorithm whose correctness is highly nontrivial, and had not been proved before. We have verified the correctness of the compiled bytecode of the deposit contract to avoid the need to trust the underlying compiler. We found several critical issues of the deposit contract during the verification process, some of which were due to subtle hidden bugs of the compiler.
In the Ethereum network, miners are incentivized to include transactions in a block depending on the gas price specified by the sender. The sender of a transaction therefore faces a trade-off between timely inclusion and cost of his transaction. Existing recommendation mechanisms aggregate recent gas price data on a per-block basis to suggest a gas price. We perform an empirical analysis of historic block data to motivate the use of a predictive model for gas price recommendation. Subsequently, we propose a novel mechanism that combines a deep-learning based price forecasting model as well as an algorithm parameterized by a user-specific urgency value to recommend gas prices. In a comprehensive evaluation on real-world data, we show that our approach results on average in costs savings of more than 50% while only incurring an inclusion delay of 1.3 blocks, when compared to the gas price recommendation mechanism of the most widely used Ethereum client.
Ethereum is a kind of blockchain platform where developers may develop and run programs called smart contracts. It inherently relies on gas consumption within a specified allowance to constrain code execution, making every instruction along an execution path to be a location for raising an exception. In this paper, we present GasFuzzer, the first work in exploring the effects of gas allowance manipulation to expose gas-oriented exception security vulnerabilities. GasFuzzer consists of two phases. The first phase introduces a gas-greedy strategy to favor transactions having higher gas consumption for mutation to obtain test transactions with different gas consumptions. The second phase introduces a novel notion of fractional gas consumption coverage and a novel gas-leveling strategy. It applies them to mutate the gas allowances of some of these transactions resulting in the highest gas consumptions produced in the first phase followed by applying these allowance-mutated transactions together with those which remained non-mutated to fuzz test the smart contract. We report an evaluation of GasFuzzer via an experiment on 3170 real-world smart contracts deployed on the public Ethereum Blockchain between October 2017 and July 2019. The findings show that GasFuzzer with gas-greedy strategy can detect more Exceptions Disorder kind of security vulnerabilities (7 more cases) than the previous state-of-the-art black-box fuzzer, and GasFuzzer with gas-leveling strategy and gas coverage criterion can detect 6 additional cases of Exceptions Disorder security vulnerabilities, which is significant.
Ethereum is a decentralized blockchain, known as being the second most popular public blockchain after Bitcoin. Since Ethereum is decentralised the canonical state is determined by the Ethereum network participants via a consensus mechanism without a centralized coordinator. The network participants are required to evaluate every transaction starting from the genesis block, which requires a large amount of network, computing, and storage resources. This is impractical for many devices with either limited computing resources or intermittent network connectivity. To overcome this drawback Ethereum defines a light client protocol where the light client fetches the blockchain state from a node operating as a light protocol server. Light clients are unable to maintain blockchain state internally, and as a consequence can only perform partial validation on blocks. Thus they rely on the light server for full block validation and to provide the updated blockchain state. Light clients connect to multiple light servers to mitigate the risk of relying on a single potentially dishonest server. Ethereum light clients are known to suffer from a probabilistic security model, but they are widely assumed to be secure under normal operating conditions. In fact, the implicit security assumptions of light clients have not been formally characterised in the literature. We present and analyse the probabilistic security guarantees under three different adversarial scenarios. The results show that for any adversary that is able to manipulate the network, the security assurances provided by the light protocol are severely impacted, and in some cases entirely lost. These results clearly demonstrate that the assumption of normal operating conditions is insufficient to justify the security assumptions of light clients. Our work also provides insight to the security of light clients under different security parameters, allowing light client implementers to more accurately understand the potential security trade-offs.
The idea of a shared economy becomes one of the companies as an enterprise type. Especially with the advanced development of digital smart devices and the internet, several forms of the mutual economy have been advanced in accord with the need for sharing of separate income. Shareable commodity and digital content are also seeking to utilize. When digital content is used as a sharing economy, various possible threats may arise in the course of transactions, the potential for theft, alteration, and hacking of contents. This paper presents a comprehensive overview of the security and privacy of Blockchain. Blockchain promise transparent, tamper-proof and secure systems that can enable novel solutions, especially when combined with smart contracts. In this research, we proposed a content protection and transaction method using Blockchain Ethereum Technology. The encryption algorithm is incorporated in proposed system to make transparent transactions and it is also implemented on content itself to prevent from smart forgery and hacking. The experimental results signify that the proposed method has strong potential to enhance transactions transparency by minimizing the security threats in digital content transactions.
The Blockchain technology and, in particular blockchain-based cryptocurrencies, offer us information that has never been seen before in the financial world. In contrast to fiat currencies, all transactions of crypto-currencies and crypto-tokens are permanently recorded on distributed ledgers and are publicly available. This allows us to construct a transaction graph and to assess not only its organization but to glean relationships between transaction graph properties and crypto price dynamics. The goal of this paper is to facilitate our understanding on horizons and limitations of what can be learned on crypto-tokens from local topology and geometry of the Ethereum transaction network whose even global network properties remain scarcely explored. By introducing novel tools based on Topological Data Analysis and Functional Data Depth into Blockchain Data Analytics, we show that Ethereum network (one of the most popular blockchains for creating new crypto-tokens) can provide critical insights on price changes of crypto-tokens that are otherwise largely inaccessible with conventional data sources and traditional analytic methods.
Praveen M. Dhulavvagol, Vijayakumar H Bhajantri, Shashikumar G. Totad
Blockchain technology is evolving and revolutionizing the IT industry with better security, efficiency, and resilience. Blockchain technology is being used in many applications majorly in cryptocurrencies and bitcoin applications. Verified transactions which make a block and group of such transactions or blocks are immutable making the blockchain more secured and reliable. Blockchain achieves decentralization of power, trust, and secured of being hacked, which solves major problems or issues with the current systems. Ethereum, the most widely used blockchain platform because of its unlimited block size. Many complex problems with smart contracts can be implemented with Ethereum and the eradication of third party organizations interfering in transactions helps solving the issues of financial crisis and it is easy to implement compared to other blockchain technologies. There are certain limitations/issues in processing large number of transactions due to lack of speed in processing the transactions. Ethereum Blockchain code will be executed by different clients with varying speed and the performance level will be different. The goal of this paper is to understand Ethereum transactions and perform the comparative analysis of Geth and Parity ethereum clients on the private blockchain. In this paper, a private blockchain network is setup where the nodes will share the data among peer nodes or blocks within the network. Using this network setup a democracy voting application is developed which makes use of the blockchain to store and process the data, smart contracts are deployed to execute the transactions. Performance analysis of the two most popular ethereum clients Geth and Parity is carried out considering time, consistency and scalability parameters. Results interpret that the overall transactions are 91% on average faster in parity client as compared to Geth client.
Kentaroh Toyoda, Koji Machi, Yutaka Ohtake, Allan N. Zhang
Private Ethereum blockchain-based systems are demanded in many industry sectors. However, the throughput performance of these systems does not meet their expectations. Many researchers have analyzed the performance of private blockchains, but their studies have failed to analyze root causes. In this paper, we perform a deep function-level bottleneck analysis for the private Ethereum blockchain. As the Ethereum client application is developed with golang, we leverage pprof, which is a resource-profiling tool for golang, and custom golang functions to measure the time taken by functions. To easily configure parameters and conduct our test, we code a shell script that automates the building process of a private Ethereum blockchain with docker containers. We conducted a series of experiments and identified the bottleneck function that is called every time a transaction arrives at an Ethereum node. In addition, we also found that the multi-threading is not well utilized, meaning that there is much room for improvement.
On February 28, 2012, an 18-year-old high school student wrote “If BitcoinBitcoin is to achieve mainstream success, it cannot stop at the limited crowds of InternetInternet geeks, libertarians, and privacy advocates that it is hitting now, and it must find some way to attract the mainstream public” (Buterin in Bitcoin adoption opportunity: teenagers, 2012).
Bitcoin users can offer fees to the miners who record transactions on the blockchain. We document the blockchain rarely runs at capacity, even though there appears to be excess demand and higher fee orders are not always prioritized. We show this is inconsistent with competitive mining, but is consistent with miners exercising market power. If users believe that only high fee transactions will be executed expeditiously then we show how strategic capacity management can be used to increase fee revenue. Using a novel data set, we present evidence consistent with strategic capacity management. We show that mining pools facilitate collusion, and estimate that they have extracted least 300 million USD a year in excess fees by making processing capacity artificially scarce.
This paper evinces the ability of gold to avoid risks during periods with great fluctuations in the Bitcoin market. We apply bootstrap full- and subsample rolling-window Granger causality tests to explore the causal relationship between Bitcoin price (BCP) and gold price (GP). The empirical results show that an increase in BCP can cause GP to decrease, indicating that the prosperity of the Bitcoin market undermines the hedging ability of gold. However, a decrease in BCP causes GP to increase, and it also emphasizes that the ability of gold to avoid risks persists. Hence, the status of gold will not be completely threatened by Bitcoin, and they are complementary to each other instead of in competition. In turn, both positive and negative influences of GP on BCP suggest that fluctuations in BCP can be predicted through the gold market. In situations of severe global uncertainty and complicated investment environments, investors can benefit from complementary markets to optimize their asset allocation. Additionally, countries can grasp the trends in Bitcoin and gold prices to prevent large fluctuations in both markets and to reduce the uncertainty of the financial system.
Bitcoin is considered to be most valuable and expensive currency in the world. Besides being first decentralized digital currency, its value has also experienced a steep increase, from around 1 dollar in 2010 to around 18000 in 2017. In recent years, it has attracted considerable attention in a diverse set of fields, including economics, finance and computer science. In economics, the primary focus has always been on studying how it affects the market, determining reasons behinds its price fluctuations, and predicting its future prices. In computer science, the focus is on its vulnerabilities, scalability, and other techno-cryptoeconomic issues. Firstly, we are going to collect the historical data of Bitcoin prices over the years 2013 to 2019 and do prediction for the year 2020. We have aimed to justify the usefulness of traditional Autoregressive Integrative Moving Average (ARIMA) model for predicting bitcoin prices. We have predicted the closing price of bitcoin for first seven days of January 2020. Further, we have created web services using ASP.NET to make the predictions on bitcoin price online and lastly, we have plotted the results in a responsive chart using Highcharts.
Κωνσταντίνος Γκίλλας, Elie Bouri, Rangan Gupta, David Roubaud
We extend existing studies by considering the higher-order moments relationships among crude oil, gold, and Bitcoin markets. Using high-frequency data from December 2, 2014 to June 10, 2018, we analyze spillovers in jumps and realized second, third, and fourth moments among crude oil, gold, and Bitcoin markets via Granger causality and generalized impulse response analyses. Results suggest evidence of predictability and emphasize, among others, the need of jointly modeling linkages across those three markets with higher-order moments; otherwise, inaccurate risk assessment and investment inferences may arise. The responses of realized volatility shocks are generally positive. Further analyses indicate evidence of a weaker relationship between gold and crude oil and Bitcoin and crude oil compared to the relationship between Bitcoin and gold. Practical implications are also discussed.
Shaen Corbet, Charles Larkin, Brian M. Lucey, Andrew Meegan · 5 authors
This paper examines the relationship between news coverage and Bitcoin returns. Previous studies have provided evidence to suggest that macroeconomic news affects stock returns, commodity prices and interest rates. We construct a sentiment index based on news stories that follow the announcements of four macroeconomic indicators: GDP, unemployment, Consumer Price Index (CPI) and durable goods. By controlling for a number of potential biases we determine as to whether each of the series' have a significant impact on Bitcoin returns. While an increase in positive news surrounding unemployment rates and durable goods would typically result in a corresponding increase in equity returns, we observe the opposite to be true in the case of Bitcoin. Increases in positive news after unemployment and durable goods announcements result in a decrease in Bitcoin returns. Conversely, an increase in the percentage of negative news surrounding these announcements is linked with an increase in Bitcoin returns. News relating to GDP and CPI are found not to have any statistically significant relationships with Bitcoin returns. Our results indicate that this developing cryptocurrency market is further maturing through interactions with macroeconomic news.