Blockchain Papers

Xudong Lin, Yiqun Meng, Hao Zhu

No abstract is available for this record.

Haris Alibašić

The rise in artificial intelligence (AI) and machine learning (ML) in cryptocurrency trading has precipitated complex ethical considerations, demanding a thorough exploration of responsible regulatory approaches. This research expands upon this need by employing a consequentialist theoretical framework, emphasizing the outcomes of AI and ML’s deployment within the sector and its effects on stakeholders. Drawing on critical case studies, such as SBF and FTX, and conducting an extensive review of relevant literature, this study explores the ethical implications of AI and ML in the context of cryptocurrency trading. It investigates the necessity for novel regulatory methods that address the unique characteristics of digital assets alongside existing legalities, such as those about fraud and insider trading. The author proposes a typology framework for AI and ML trading by comparing consequentialism to other ethical theories applicable to AI and ML use in cryptocurrency trading. By applying a consequentialist lens, this study underscores the significance of balancing AI and ML’s transformative potential with ethical considerations to ensure market integrity, investor protection, and overall well-being in cryptocurrency trading.

Khanh Quoc Nguyen, Thanh Huong Nguyen, Bao Linh

No abstract is available for this record.

Authors unavailable

This paper aims to compare the empirical performance of two approaches in detecting structural breaks and outliers due to the significant frequent price changes seen in cryptocurrencies.The two approaches are indicator saturation (IS) and Bai and Perron (BP).The cryptocurrency data employed in this study are Bitcoin and Ethereum.In comparing the performance of the two approaches, this study performed multiple empirical comparisons using various significant levels, different data frequencies, as well as the original and log series (price).The findings showed that the prices contained structural breaks and outliers and that the IS approach performed significantly better than the BP test in terms of the identified structural breaks as well as outliers across different settings.The contribution of this study is providing empirical comparisons between IS and BP approaches using cryptocurrency data.These findings are important to the potential stakeholders, in particular, for quality control in industries, for setting price targets, and for confirming trading signals to reduce potential losses.

Artor Nuhiu, Florin Aliu, Jakub Horák, Bedri Peci

Despite widespread skepticism linked to cryptocurrencies, they are constantly gaining the interest of scholars, investors, media, and regulators. Recognizing the importance that portfolio risk maintains for crypto participants, this study attempts to shed light on this issue. We investigate the risk-return tradeoffs of the most tradable cryptocurrencies based on portfolio diversification techniques. Three different crypto portfolios containing a diverse number of cryptocurrencies were created to analyze the diversification risk from a historical perspective. Data concerning daily prices and their trade volume was collected from the Coin Market Cap database and covered the period from 1 January 2016 to 31 December 2022. The results regarding the risk-reward tradeoff stand in line with the portfolio theory, where higher expected returns offset higher risk. On average, the portfolio composed of 10 cryptocurrencies offers better optimization than the one with five, as it generates the same returns with lower risk. The year 2018 reflects the maximum diversification benefits in the three portfolios, corresponding to the period when cryptocurrencies gained massive popularity. From the managerial perspective, results inform crypto and institutional investors of the possible diversification benefits of the 15 most traded cryptocurrencies.

Hanol Lee, Dainn Wie

Disruption and shutdown of exchanges frequently happen in the cryptocurrency market, though its potential impacts are relatively under-investigated due to several empirical challenges. This study employs 20-h of service interruption on October 15th at Upbit , the dominant cryptocurrency exchange in Korea, as an exogenous shock to examine the effect of unexpected service interruption at the exchange on cryptocurrency market. Event study estimation using price data from Binance, the largest cryptocurrency exchange globally, shows the sharp and negative reactions to cryptocurrencies mostly traded at Upbit . Major currencies such as Bitcoin and Ethereum also presented limited reactions, implying that service interruption could be interpreted as vulnerability of overall cryptocurrencies.

Ke-Hsin Chou, Min-Yuh Day, Chien‐Liang Chiu

No abstract is available for this record.

Marcin Wątorek, Maria Skupień, Jarosław Kwapień, Stanisław Drożdż

This paper investigates the temporal patterns of activity in the cryptocurrency market with a focus on Bitcoin, Ethereum, Dogecoin, and WINkLink from January 2020 to December 2022. Market activity measures - logarithmic returns, volume, and transaction number, sampled every 10 seconds, were divided into intraday and intraweek periods and then further decomposed into recurring and noise components via correlation matrix formalism. The key findings include the distinctive market behavior from traditional stock markets due to the nonexistence of trade opening and closing. This was manifest in three enhanced-activity phases aligning with Asian, European, and U.S. trading sessions. An intriguing pattern of activity surge in 15-minute intervals, particularly at full hours, was also noticed, implying the potential role of algorithmic trading. Most notably, recurring bursts of activity in bitcoin and ether were identified to coincide with the release times of significant U.S. macroeconomic reports such as Nonfarm payrolls, Consumer Price Index data, and Federal Reserve statements. The most correlated daily patterns of activity occurred in 2022, possibly reflecting the documented correlations with U.S. stock indices in the same period. Factors that are external to the inner market dynamics are found to be responsible for the repeatable components of the market dynamics, while the internal factors appear to be substantially random, which manifests itself in a good agreement between the empirical eigenvalue distributions in their bulk and the random matrix theory predictions expressed by the Marchenko-Pastur distribution. The findings reported support the growing integration of cryptocurrencies into the global financial markets.

Zhunzhun Liu, Ruidong Zhang

In recent years cryptocurrency trading has been very active and received attention from worldwide investors. Given its short history and lack of effective regulatory frameworks, cryptocurrency trading is still like the wild west. This study selected the top 21 dominant cryptocurrencies to analyze the trading behavior associated with them over the period from 2016 to May 2023. We adopted the cross-sectional absolute deviations (CSAD) model to investigate investors’ trading behavior. Two calculation methods are used: the capitalization-weighted method and the equally weighted method. Our analysis results have indicated that no matter which calculation method is used, the cryptocurrency marketplace has shown strong and significant herd behavior from 2016 to March 12, 2020. However, significant herd behavior couldn’t be detected after March 12, 2020. This is a very interesting and yet important finding from our study. The implication is that March 12, 2020 appeared to be a turning point where important market conditions might have changed, such as the dominant cryptocurrency trading force has transformed from individuals to institutional investors.

Utku Altunöz

This study aims to model the volatility features of Bitcoin, Ethereum, and Ripple, which are the cryptocurrencies with the greatest volumes that have come to the agenda since the global crisis, and to determine the presence and dates of price bubbles.After running the ADF and Ng-Perron unit root tests, the EGARCH model was analyzed as the best for Bitcoin and TGARCH for the Ethereum and Ripple. According to the obtained results, negative coefficients for Bitcoin imply that negative shocks will increase volatility more than positive shocks. This means that a leverage effect is present. No leverage effect was reached for Ethereum or Ripple, and positive shocks are understood to increase volatility for them compared to negative shocks. In addition, continuous speculative bubble pricing occurred for all three cryptocurrencies, with much higher bubble prices being understood to have occurred with Ethereum and Bitcoin compared to Ripple.

Bayu Adi Nugroho

Purpose — This study aims to examine Islamic cryptocurrencies and their dependency on foreign exchange markets in vine copula architecture (CD-Vine) and provide a framework for detecting complex dependence structures, risk management implications, and hedging effectiveness. Design/Methodology/Approach — This study used gold-backed cryptocurrencies and three fiat currencies. The vine copula approach was preferred because it applies several distributions and estimates complex dependencies. Hedging effectiveness was measured by constructing simulation-based portfolios optimised with DCC-t-Copula. Benford’s law and realized variance were used to determine the stability of Islamic cryptocurrencies. Findings — According to C-Vine and D-Vine copula models, paper money has a weak tail dependence with gold-backed cryptocurrencies. Only OneGram coin, whose volatility matched the risk of Bitcoin, showed zero irregularities in volume trading. The findings were robust to different estimations based on Minimum Spanning Tree and Dendrogram. Originality/Value — This is the first study to examine Islamic cryptocurrencies’ stability and the significance of hedging effectiveness on gold-backed cryptocurrencies under a copula-based approach. Research Limitations — The study did not apply time-varying vine copula. Practical Implications — The risk management perspective shows insignificant hedge effectiveness in the portfolio of fiat and gold-backed cryptocurrencies.

Kai Meng, Khalid Khan

No abstract is available for this record.

Benjamin Hubbard

Purpose The purpose of this paper is to examine potential financial accounting treatments for cryptocurrencies, including the current guidance, and compare the benefits and shortcomings of each method. It proposes the introduction and use of an intangible asset revaluation model. The study aims to inform both standard setters and financial statement preparers of the most appropriate accounting treatment of this digital asset. Design/methodology/approach This paper uses an exploratory analysis and conceptualizes each technical treatment option. For each potential treatment, this paper describes the technical accounting guidance and financial statement implications. The study also uses an illustration to compare the outcomes of each treatment option. Findings This paper provides insights into the most appropriate financial accounting treatment of cryptocurrencies. Findings indicate the best option is an intangible asset revaluation model that allows firms to elect a fair value option and record fluctuations in market value to other comprehensive income. This model would improve the accuracy of asset numbers while maintaining the relevance of income amounts by preventing large gains or losses from fair value fluctuations from flowing through the income statement. Research limitations/implications The number of firms that hold cryptocurrencies on their balance sheet remains small, thus the research is limited to anecdotal and expository analysis. Practical implications The study includes implications for accounting standard setters as they continue to deliberate the appropriate financial accounting treatment for cryptocurrencies. The study can inform the standard setting process and impact future authoritative guidance. Social implications The use of cryptocurrency is extremely popular among individual investors and consumers. Updating accounting guidance on crypto can help support a robust crypto market through a useful, informative approach to measurement and reporting. This can also aid in improving the economic prosperity of crypto investors. Originality/value This paper fulfills an identified need to examine and understand appropriate accounting guidance for cryptocurrencies. Current guidance has been deemed ineffective and there is debate regarding the proper treatment moving forward. This paper contextualizes this debate and provides suggested solutions.

houssam boughabi, Yassine El Qalli

Abstract Volatility of Bitcoin has a long memory, we modeled such a character using FIGARCH processes, afteward we went on for pricing Futures and Options, the price of Futures depends on many factors in the market, we have proposed a model for futures contracts which links their price to spot price and volatility, after calibrating our model the result was consistent with market values, the pandemic of Covid which started earlier in 2020 after hitting the district of Wuhan just before; had not really an effect on derivatives markets until july 2021 when the market started a downward trend. We price Options using a sample of volatilities that we consider determinstic for a matter of calculous. We finally compare our model for Futures to the same model but with a constant volatility. JEL Classification. G13

Wafa Abdelmalek

Purpose This study investigates the diversification benefits of multiple cryptocurrencies and their usefulness as investment assets, individually or combined, in enhancing the performance of a well-diversified portfolio of traditional assets before and during the pandemic COVID-19. Design/methodology/approach This paper uses two optimization techniques, namely the mean-variance and the maximum Sharpe ratio. The naïve diversification rules are used for comparison. Besides, the Sharpe and the Sortino ratios are used as performance measures. Findings The results show that cryptocurrencies diversification benefits occur more during the COVID-19 pandemic rather than before it, with the maximum Sharpe ratio portfolio presenting its highest performance. Furthermore, the results suggest that, during COVID-19, the diversification benefits are slightly better when using a combination of cryptocurrencies to an already well-diversified portfolio of traditional assets rather than individual ones. This serves to improve the performance of the maximum Sharpe ratio portfolio, and to some extent, the naïve portfolio. Yet, cryptocurrencies, whether added individually or combined to a well-diversified portfolio of traditional assets, don't fit in the minimum variance portfolio. Besides, the efficient frontier during COVID-19 pandemic dominates the one before COVID-19 pandemic, giving the investor a better risk-return trade-off. Originality/value To the best of the author's knowledge, this is the first study that examines the diversification benefits of multiple cryptocurrencies both as individual investments and as additional asset classes, before and during COVID-19 pandemic. The paper covers all analyses performed separately in previous studies, which brings new evidence regarding the potential for cryptocurrencies in portfolio diversification under different portfolio strategies.

Yongqiang Meng, John W. Goodell, Dehua Shen

No abstract is available for this record.

Anamika Kumar Kulbhaskar, Sowmya Subramaniam

No abstract is available for this record.

Nitin N. Sakhare, Imambi S. Shaik

No abstract is available for this record.

Muhammad Abubakr Naeem, Mohammad Rahim Shahzad, Sitara Karim, Rima Assaf

No abstract is available for this record.

Tezer Yelkenci, Birce Dobrucalı, Gülin Vardar, Berna Aydoğan

Purpose This study aims to empirically investigate the linkages between digital trails of social signals (content and profile features of bitcoin-related tweets) and bitcoin price return using a VAR-BEKK-GARCH model. Design/methodology/approach Bitcoin-related tweets were collected every hour for six months from September 1, 2020, to February 29, 2021. The analysis involved two steps: first, examining tweet content, profiles, sentiment and emotions; and second, investigating the relationship between social signal volatility and hourly bitcoin price return. Findings Results indicate that bitcoin price changes can impact the sentiment expressed in tweets about bitcoin, and vice versa. While sadness exhibits a bidirectional volatility spillover with bitcoin, fear and anger display a one-period lag. Quartile analyses reveal that only fear in the second quartile shows a bidirectional spillover effect with bitcoin, while all other emotions except sadness demonstrate a unidirectional spillover effect in all remaining quartiles. Originality/value The study uses a novel two-step approach to analyze volatility spillovers between social signals and bitcoin price returns. Findings can guide investors and portfolio managers in making better allocation decisions and assist policymakers and regulators in reducing the adverse effects of bitcoin’s volatility on financial system stability.

Siddharth M. Bhambhwani, Stefanos Delikouras, George M. Korniotis

No abstract is available for this record.

You Liang, A. Thavaneswaran, Alex Paseka, Sulalitha Bowala · 5 authors

A profitable data-driven algorithmic trading algorithm will benefit from a dynamic system that can produce accurate hedge ratio estimates and short-term innovation volatility forecasts. Commonly used pairs and multiple trading strategies are constructed using the Kalman Filter (KF) and exploiting mean reversion in co-integrated nonstationary stock prices. However, KFs are sensitive to model errors. Misspecified modelling produces unstable solutions for dynamic systems. Fading-Memory Filter (FMF) uses a discounting weight to past observations. Compared to a standard KF, FMF addresses more recent observations and is more resilient (less sensitive) to modelling errors. However, the FMF algorithm does not provide slope parameter covariance matrix updates and innovation volatility forecasts. This paper proposes a novel resilient FMF algorithm for pairs trading and multiple trading by defining an appropriate data-driven innovation volatility forecasting model. The FMF-based strategies are implemented through some experiments on the hourly prices (high-frequency data) of Bitcoin, Ethereum and Litecoin. It is shown that the proposed FMF trading strategies outperform the existing KF trading strategies and they are more profitable in the bear market over time, especially for continuous falling of prices and the short-lived and sharp rally recovery where prices are not stationary.

Japjeet Singh, Ruppa K. Thulasiram, A. Thavaneswaran, Alex Paseka

There have been several studies in the literature discussing the profitability with various trading strategies. Two common strategies are pairs trading and momentum strategies. The momentum strategy aims to exploit the phenomenon of momentum, where securities that have performed well in the past are likely to continue performing well in the future. The concept behind a pairs trading of stocks is similar to the statistical idea of cointegration. The goal of pairs trading is to profit from the relative price movements of the two assets, rather than from the absolute price movements of either asset. This strategy is generally implemented using algorithmic trading techniques, and it is often used by traders and investors to take advantage of mispricing in the market. In this study we first compare these two strategies and implement them to study for their profitability. We considered two major cryptocurrencies (Bitcoin and Ethereum) for these two trading strategies and show that with daily price data, dual momentum strategy generates significantly better results than the pairs trading strategy.

Mohammad Abdullah, David Adeabah, Emmanuel Joel Aikins Abakah, Chi‐Chuan Lee

No abstract is available for this record.