Blockchain Papers

Fan Zhou

No abstract is available for this record.

Tieming Li

No abstract is available for this record.

Sihan He

Nakamoto’s Bitcoin is the first decentralized digital cash system that utilizes a blockchain to manage transactions in its peer-to-peer network. The newer generation of blockchain systems, including Ethereum, extend their capabilities to support deployment of smart contracts within their peer-to-peer networks. However, smart contracts cannot acquire data from sources outside the blockchain since the blockchain network is isolated from the outside world. To obtain data from external sources, smart contracts must rely on Oracles, which are agents that bring data from the outside world to a blockchain network. However, guaranteeing that the oracle’s off-chain nodes are trustworthy remains a challenge. A centralized oracle that relies on a single off-chain node creates a single point of failure. Therefore, a decentralized mechanism is necessary. One possible design for a decentralized oracle is to use a game mechanism that utilizes the Schelling-point theory to identify the correct data point among various data points reported by the oracle’s off-chain nodes. In this paper, we introduce Spartan Price Oracle (SPO), a decentralized oracle designed to provide accurate price data. SPO utilizes the Schelling-point theory in its game mechanism to ensure the accuracy of the price data it provides. The mechanism design of SPO is based on SchellingCoin but with two significant improvements. Firstly, SPO uses Kernel Density Estimation to estimate the probability density function of data points that are reported by multiple off-chain nodes. This enables SPO to identify the accurate data by determining the mode of the probability density function. Secondly, SPO utilizes a redistributive economic incentive model that incorporates an appeal mechanism to increases the maximum reward for its off-chain nodes. This model has been proven to raises the budget required for compromising off-chain nodes and helps in preventing potential attacks on the oracle.

Ruting Wang, Valerio Potì, Wolfgang Karl Härdle

No abstract is available for this record.

Justice Kyei-Mensah

No abstract is available for this record.

Andrey Rezaev, Наталья Дамировна Трегубова

This paper aims to analyze Bitcoin as an identifiable system of human-machine interdependence. The authors start with a brief historical outline of the Bitcoin project and discuss questions that Bitcoin poses to social sciences, such as whether Bitcoin is money, how the Bitcoin project relates to economic theory, what determines the value of a Bitcoin, and what are the conditions for trust in Bitcoin? Finally, what happens when the Bitcoin project becomes a reality? In what follows, the authors correlate the existence of Bitcoin with the spread of artificial intelligence (AI) technologies as active intermediaries and participants in human interactions. After observing the similarities and differences between AI and the Bitcoin project, the idea of whether Bitcoin can act as “artificial money” for AI is discussed, and the reality of human-machine interdependence is exemplified. In conclusion, the authors define Bitcoin as a particular system of human-machine interdependence initially conceived as an alternative to money; however, in reality, it supplements the existing economic order.

Siyu Yang, Kun Liu

This article quantifies the correlation between Bitcoin and NVIDIA using the DCC-GARCH model during the period of 2020-2023. We analyzed data from investing.com for this research. Bitcoin is a cryptocurrency based on blockchain technology, which involves mining by solving complex cryptographic puzzles. Mining refers to the process of verifying and recording Bitcoin transactions through computation, and acquiring newly generated Bitcoins as a contribution to network security and the distributed consensus mechanism. Therefore, it is important to understand the correlation between Bitcoin and graphics cards, especially with the expansion of the virtual currency market. Determining the correlation between Bitcoin mining and graphics cards can help miners optimize their hardware choices, investors better understand market potential, and manufacturers produce and develop graphics cards according to market demand. Due to the high computational requirements of Bitcoin mining, traditional central processing units (CPUs) are not well-suited for this task. On the other hand, graphics cards (graphics processing units, GPUs) have become the preferred hardware for Bitcoin mining due to their highly parallel computing capabilities. Consequently, we hypothesize the existence of a correlation between Bitcoin and graphics cards, which is further validated in subsequent sections.

Daniel Traian Pele, Wolfgang Karl Härdle, Ilyas Agakishiev

No abstract is available for this record.

Rhenan Gomes dos Santos Queiroz, Richard McGee, Sérgio Adriani David

No abstract is available for this record.

Yao YUE, Yuying Sun, K.D. Yang, Shouyang Wang

Since Bitcoin came into the world, modelling and analyzing the underlying characteristics of Bitcoin has attracted increasing attention. This paper uses a framework including decomposition, reconstruction and extraction method (DRE) to analyze price fluctuations based on ultra-high-frequency data from Dec.1, 2019, to Nov.30, 2021. First, the ensemble mode decomposition (EMD) is employed to decompose the Bitcoin hourly spot price into 13 intrinsic mode functions (IMF) plus a residual. Second, the IMFs are reconstructed into high-frequency components, low-frequency components and a trend based on fine-to-coarse reconstruction. Furthermore, the intraday volatility analysis based on LM test is applied on 15-minutes frequency data to detect discontinuous jump arrivals and extract jump from realized quadratic variation. Empirical results show that three components of reconstruction can be identified as short term fluctuations process caused by microstructure noise, the shocks affected by major events, and a long-term trend based on inelastic supply and rigid demand. We find that approximately 40% of jumps can be matched with the news from the public news database (Factiva), and the jump sizes are larger than that of stock markets. This finding indicates that the Bitcoin market has more irregularly noise and unforeseen shocks from unscheduled events.

Mahsa Dareh Shiri, Daniel Dupuis, Kimberly C. Gleason, Osamah M. Al‐Khazali

No abstract is available for this record.

Klaus Grobys, Syed Jawad Hussain Shahzad

ABSTRACT Recent literature explores the profitability of various cryptocurrency momentum trading strategies and proposes cryptocurrency momentum as a pricing factor (Liu et al.). How risky is this factor‐based investment strategy for crypto‐investments? We answer this question by examining the distributional characteristics (hence, riskiness) of six cryptocurrency momentum trading strategies. The empirical evidence suggests that the realised variances of cryptocurrency momentum strategies are governed by power laws. The statistical tests derived from block bootstraps indicate that the population mean and variance of the momentum factor realised variances are statistically not defined. Contrary to the belief that cryptocurrency momentum trading strategies produce generous payoffs, our results imply that, in real life, we might not be able to realise these risk premiums. We conclude that the performance metrics evaluating the profitability of cryptocurrency momentum strategies, using variance as an input, are not informative. We also find cross‐sectional dependence amongst the tail risk of momentum strategies based on different formation periods.

Guangye Cao

This dissertation consists of three essays. The first essay provides background on blockchain, cryptocurrency, and venture capital. It will explain the evolution of token distribution models, regulatory concerns, and the industry adoption of the technology. The second essay presents a model of startup financing that reflects regulatory concerns of the first essay. It develops a three-period model that compares token financing with traditional VC equity financing, where the key difference between the two is that tokens can be sold earlier than equity, which allows them to meet the liquidity needs of investors. The third essay combines token financial data and onchain transaction data from the Ethereum blockchain, to study the relationship between token liquidity, returns, and onchain market maker inventory.

Alexander I. Iliev, Malvika Panwar

No abstract is available for this record.

Yosef Bonaparte

This paper demonstrates that the Millennial generation exhibits unique personal traits that have implications for their portfolio choice and, hence, for the stock market. Specifically, Millennials display greater propensity to participate in the stock market, exhibit more confidence (as they trade more frequently), and more diversification (invest in greater number of stocks and foreign assets). At the macro level, we find that the Millennials influence the stock market to behave differently surrounding holidays, and the statistical significance of key financial anomalies is disrupted. Despite the Millennials’ proficiency in using internet, they utilize more social methods (friends/relatives) when they invest than previous generations. Collectively, we infer that the financial market is not only exposed to business cycles, but also to generation cycles.

Mateusz Skwarek

Abstract Despite recent studies focused on comparing the dynamics of market efficiency between Bitcoin and other traditional assets, there is a lack of knowledge about whether Bitcoin and emerging markets efficiency behave similarly. This paper aims to compare the market efficiency dynamics between Bitcoin and the emerging stock markets. In particular, this study indicates whether the dynamics of Bitcoin market efficiency mimic those of emerging stock markets. Thus, the paper's contribution emerges from the combination of Bitcoin and emerging markets in the field of dynamics of market efficiency. The dynamics of market efficiency are measured using the Hurst exponent in the rolling window. The study uses daily data for the MSCI Emerging Markets Index and the Bitcoin market over the period 2011–2022. Our results show that there is at most a moderate correlation between the dynamics of Bitcoin and emerging stock markets’ efficiency over the entire study period. The strongest correlations occur mainly in periods of high economic policy uncertainty in the largest Bitcoin mining countries. Therefore, the association between Bitcoin market efficiency and emerging stock markets’ efficiency may strengthen with an increase in economic policy uncertainty. These findings may be useful for investors and portfolio managers in constructing better investment strategies.

Yeguang Chi, Wenyan Hao, Qionghua Chu

No abstract is available for this record.

Chikashi Tsuji

This paper investigates the profitability of Bitcoin and US equity. More concretely, we inspect the performances of the S&P 500 index and Bitcoin by comparing their returns and volatilities. As a result, we obtain the following significant findings. First, our regression analysis clarifies that for the period after the sudden appearance of COVID-19, there was a weak nexus between the S&P 500 index and Bitcoin returns. In addition, our return and return spread analysis evidences that for this period, on average, Bitcoin returns were much higher than the S&P 500 index returns. Moreover, our volatility and volatility spread analysis reveals that for this period, on average, the volatilities of Bitcoin returns were much higher than those of the S&P 500 index returns.

Haoyuan Ma

Price forecasting is pretty crucial in the asset management and allocation and quantitative trading industries. With the development of the global economic situation, decentralized finance has gradually entered people's field of vision, and cryptocurrency and cryptocurrency finance have become the r

Aderonke Tosin-Amos

Virtual assets and currency sector are becoming increasingly intertwined.According to new IMF research, the correlation of crypto assets with traditional holdings like equities has increased dramatically as usage has grown, limiting their risk perception investment opportunities, and raising the danger of spillover across financial markets.Theoretical and empirical findings concerning cryptocurrencies and stock market behaviour have been misleading thereby putting policy makers at a crossroads.This paper therefore examines the response of stock market to investment in cryptocurrencies in the US stock market.Monthly data covering the period between February 2016 to February 2022 was used.The answer was achieved using novel dynamic autoregressive-distributed lag (ARDL) simulation techniques along with the Breitung and Candelon causality test.Findings revealed that cryptocurrencies impacted positively on the US stock market.Secondly, investment in Bitcoin and Ethereum is a good predictor of stock market while no evidence of causality between investment in ripple and stock market indices in the US stock market.Thirdly, a long-run relationship exists between investment in cryptocurrencies and behaviour of stock market indices in the United State, and that investment in cryptocurrencies has a significant long-run increasing effect on stock prices in United State.

Jędrzej Rudkiewicz, Marcin Hernes

The purpose of the research is to study the cryptocurrency data listed on Binance, and design a profitable strategy based on the findings. The data covers over 150 selected cryptocurrencies. The study aims to detect anomalies in the volume and number of transactions and apply an investment strategy based on deviations and sudden price fluctuations. An autoencoder and LSTM-based neural network have been used. Based on the results of the present research, it can be concluded that the model successfully identified anomalies in the data regarding the volume and number of transactions carried out. I it was also observed that price volatility in the period close to the detected anomaly was significantly higher than average volatility for the sample.

Srđan Radulović

Bitcoin was presented at the end of 2008 but the question still remains whether it is a form of money or something entirely different. Bitcoin was not designed with the aim to create money in a strict sense but primarily with the intention to make the transfer of value as effective as possible. Yet, Bitcoin has a capacity to take on the role of money, and that capacity was recognized in court cases. In this regard, the paper presents the results of the primarily empirical but also theoretical research conducted previously on the volatile but still very deflationary nature of Bitcoin and its effect on monetary obligations. The idea that cryptocurrencies can be also used as a hedging instrument to prevent the negative effects of domestic currency depreciation might be controversial for a number of reasons, one of which is certainly the volatile nature of bitcoin "price". We stress that periodic depreciation of its value does not mean that bitcoin is inflationary. On the contrary, bitcoin is deflationary by nature, which is evident in different in-built mechanisms and new ways of application. In this paper, the author uses different analytical method techniques to single out and describe various deflatory mechanisms, both preprogramed and factual ones. The author also applies the synthetical method and its techniques, primarily abstraction and generalization, to sum up the data confirming the main hypothesis that bitcoin is by nature deflationary despite its volatility and, therefore, it can be used as a hedging mechanism.

Kirill Shilov, Andrey Zubarev

No abstract is available for this record.

Ta-Cheng Chang, Wei-Ying Nie, Hsuan-Ling Chang, Kuang‐Chieh Yen

We examine how economic policy uncertainty (EPU) influences realized variance dependency and tail-risk synchronization across major cryptocurrencies. Using 5-min high-frequency returns to construct realized variance and signed jump variance measures, we document that global and Western EPU (the US, UK, France) significantly strengthen both variance dependency (VD) and signed jump variance dependency (SJVD) among the top 15 cryptocurrencies, whereas Asian EPUs exhibit weaker and less consistent effects. The sensitivity of SJVD is particularly pronounced, reflecting the asymmetric transmission of tail risk during uncertainty shocks. These findings remain robust after controlling for Bitcoin’s realized volatility and hold in post-COVID subsample analysis. Our results suggest that cryptocurrency markets exhibit greater systemic interconnectedness and heightened tail-risk co-movements during periods of elevated policy uncertainty, with important implications for risk management and financial stability monitoring.