This paper aims to elucidate the connectedness between major forex currencies and cryptocurrencies using the quantile cross-spectral approach recently proposed by Baruník and Kley (2015). The sample covers six forex currencies and six cryptocurrencies over the period of 1 September 2015 to 29 December 2017. Compared with the results obtained from standard correlations and detrended moving-average cross-correlation analysis (DMCA), the quantile cross-spectral approach provides richer information on the dependence structure across different quantiles and frequencies. The most interesting result is that the intra-group dependencies are positive in the lower extreme quantiles, while inter-group dependencies are negative. This result holds in both the short- and long-term perspectives. Thus, it is worth diversifying between these two currency groups.
Nowadays, blockchain has become one of the most cutting-edge technologies, which has been widely concerned and researched. However, the quantum computing attack seriously threatens the security of blockchain, and related research is still less. Targeting at this issue, in this paper, we present the definition of post-quantum blockchain (PQB) and propose a secure cryptocurrency scheme based on PQB, which can resist quantum computing attacks. First, we propose a signature scheme based on lattice problem. We use lattice basis delegation algorithm to generate secret keys with selecting a random value, and sign message by preimage sampling algorithm. In addition, we design the first-signature and last-signature in our scheme, which are defined as double-signature. It is used to reduce the correlation between the message and the signature. Second, by combining the proposed signature scheme with blockchain, we construct the PQB and propose this cryptocurrency scheme. Its security can be reduced to the lattice short integer solution (SIS) problem. At last, through our analysis, the proposed cryptocurrency scheme is able to resist the quantum computing attack and its signature satisfies correctness and one-more unforgeability under the lattice SIS assumption. Furthermore, compared with previous signature schemes, the sizes of signature and secret keys are relatively shorter than that of others, which can decrease the computational complexity. These make our cryptocurrency scheme more secure and efficient.
Jethin Abraham, Daniel Higdon, John B. Nelson, Juan G. Ibarra
In this paper, we present a method for predicting changes in Bitcoin and Ethereum prices utilizing Twitter data and Google Trends data. Bitcoin and Ethereum, the two largest cryptocurrencies in terms of market capitalization represent over \$160 billion dollars in combined value. However, both Bitcoin and Ethereum have experienced significant price swings on both daily and long term valuations. Twitter is increasingly used as a news source influencing purchase decisions by informing users of the currency and its increasing popularity. As a result, quickly understanding the impact of tweets on price direction can provide a purchasing and selling advantage to a cryptocurrency user or a trader. By analyzing tweets, we found that tweet volume, rather than tweet sentiment (which is invariably overall positive regardless of price direction), is a predictor of price direction. By utilizing a linear model that takes as input tweets and Google Trends data, we were able to accurately predict the direction of price changes. By utilizing this model, a person is able to make better informed purchase and selling decisions related to Bitcoin and Ethereum.
We use the GARCH-MIDAS model to extract the long- and short-term volatility components of cryptocurrencies. As potential drivers of Bitcoin volatility, we consider measures of volatility and risk in the US stock market as well as a measure of global economic activity. We find that S&P 500 realized volatility has a negative and highly significant effect on long-term Bitcoin volatility. The finding is atypical for volatility co-movements across financial markets. Moreover, we find that the S&P 500 volatility risk premium has a significantly positive effect on long-term Bitcoin volatility. Finally, we find a strong positive association between the Baltic dry index and long-term Bitcoin volatility. This result shows that Bitcoin volatility is closely linked to global economic activity. Overall, our findings can be used to construct improved forecasts of long-term Bitcoin volatility.
Abstract We document numerous occurrences of pump-and-dump schemes (P&Ds) targeting cryptocurrencies, which tend to trigger short-term episodes that feature dramatic increases in prices, volume, and volatility, followed by quick reversals. The evidence we document, including price run-ups before P&Ds start, suggests wealth transfers from outsiders to insiders. Our findings based on wallet-level data are consistent with the reasoning that gambling preferences, overconfidence, and naïve reinforcement learning help explain P&D participation. Finally, exploiting two natural experiments in which exchanges altered P&D policies, we find evidence consistent with the idea that P&Ds contribute to reduced cryptocurrency liquidity and lower prices.
Laura Alessandretti, Abeer ElBahrawy, Luca Maria Aiello, Andrea Baronchelli
Machine learning and AI-assisted trading have attracted growing interest for the past few years. Here, we use this approach to test the hypothesis that the inefficiency of the cryptocurrency market can be exploited to generate abnormal profits. We analyse daily data for $1,681$ cryptocurrencies for the period between Nov. 2015 and Apr. 2018. We show that simple trading strategies assisted by state-of-the-art machine learning algorithms outperform standard benchmarks. Our results show that nontrivial, but ultimately simple, algorithmic mechanisms can help anticipate the short-term evolution of the cryptocurrency market.
Abstract We establish that cryptocurrency returns are driven and can be predicted by factors that are specific to cryptocurrency markets. Cryptocurrency returns are exposed to cryptocurrency network factors but not cryptocurrency production factors. We construct the network factors to capture the user adoption of cryptocurrencies and the production factors to proxy for the costs of cryptocurrency production. Moreover, there is a strong time-series momentum effect, and proxies for investor attention strongly forecast future cryptocurrency returns.
Blockchain-based distributed computing platforms enable the trusted execution of computation - defined in the form of smart contracts - without trusted agents. Smart contracts are envisioned to have a variety of applications, ranging from financial to IoT asset tracking. Unfortunately, the development of smart contracts has proven to be extremely error prone. In practice, contracts are riddled with security vulnerabilities comprising a critical issue since bugs are by design non-fixable and contracts may handle financial assets of significant value. To facilitate the development of secure smart contracts, we have created the FSolidM framework, which allows developers to define contracts as finite state machines (FSMs) with rigorous and clear semantics. FSolidM provides an easy-to-use graphical editor for specifying FSMs, a code generator for creating Ethereum smart contracts, and a set of plugins that developers may add to their FSMs to enhance security and functionality.
Bikramaditya Singhal, Gautam Dhameja, Priyansu Sekhar Panda
The era of Blockchain applications has just begun. Ethereum is here to be the defacto Blockchain platform for building decentralized applications. We already learned in the previous chapters that public Blockchain use cases are not just limited to cryptocurrencies, and the possibilities are only limited by your imagination! Ethereum has already made inroads in many business sectors and works best not only for public Blockchain use cases, but also for the private ones. Ethereum has already set a benchmark for Blockchain platforms and must be studied well to be able to envision how usable decentralized applications can be built with or without using Ethereum. Today, it is possible to build Blockchain applications with minimal knowledge of cryptography, game theory, mathematics or complex coding, and computer science fundamentals, thanks to Ethereum.
According to Forbes, “Ethereum is the first generic blockchain platform that allows users to easily create and deploy their decentralized and trustless applications. It has created incredible opportunities in the fintech space.” This chapter will introduce you to the entire ecosystem of Ethereum. Later chapters will discuss its specific components in more detail.
Alex Biryukov, Dmitry Khovratovich, Sergei Tikhomirov
Identity is a fundamental concept for the financial industry. In order to comply with regulation, financial institutions must verify the identity of their customers. Identities are currently handled in a centralized way, which diminishes users' control over their personal information and threats their privacy. Blockchain systems, especially those with support for smart contracts (e.g., Ethereum), are expected to serve as a basis of more decentralized systems for digital identity management. We propose a design of a privacy-preserving KYC scheme on top of Ethereum. It would let providers of financial services leverage the potential of blockchain technology to increase effciency of customer onboarding while complying with regulation and protecting users' privacy.
This chapter walks you through the setup and installation of tools required to run the Ethereum blockchain. We cover hardware requirements, operating system requirements, and software requirements. After covering the installation of the software, we provide the basic commands required to interact with the Ethereum network.