Blockchain Papers

Ghulame Rubbaniy

No abstract is available for this record.

Patrick Augustin, Alexey Rubtsov, Donghwa Shin

No abstract is available for this record.

Rebecca Abraham

Bitcoin is the currency of the blockchain, which promises cost reductions for businesses. This paper develops models to value bitcoin, bitcoin futures, and bitcoin options. It provides the theoretical basis for bitcoin pricing. Optimal bitcoin prices are derived at the intersection of an aberrancy utility function, a hyperbolic cosine utility function, and a Bessel utility function with price distributions. Rational investors value bitcoin on the basis of blockchain applications, while irrational investors' value bitcoin based on personal recommendations.

Nenad Tomić

The objective of the study is to determine whether the Bitcoin forks have produced significant effects on the cryptocurrency market. The event study methodology is used in this paper in order to determine the statistical significance of the abnormal return of leading cryptocurrencies after three Bitcoin forks. The forks were viewed as three isolated events, with the estimations windows and the event windows constructed separately for each of them. There were statistically significant negative effects related to the creation of Bitcoin Gold and Bitcoin SV. Contrary to expectations, there was no statistically important effect throught out the most famous Bitcoin forking and emergence of Bitcoin Cash. Although cryptocurrencies are a current topic, the literature lacks quantitative research dealing with price changes. Without quantitative analysis, it is difficult to conclude whether the return change is a consequence of a statistically significant event The analysis would therefore provide the tool to determine the statistical significance of their impact on the market. A small number of observed cryptocurrencies is the main limitation of this research. Future researches could cover a wider scope of the market and include other famous cases of forking, for example, the Ethereum forks.

Thomas Dimpfl, Kai Mäckle

No abstract is available for this record.

D. Susana, S. Sreejith, J. K. Kavisanmathi

No abstract is available for this record.

Muhammad Abubakr Naeem, Kashif Saleem, Sheraz Ahmed, Naeem Muhammad · 5 authors

We explore extreme return-volumes dependence among different cryptocurrencies such as Bitcoin, Ethereum, Ripple, and Litecoin by using the Copula approach. We use Student-t, Frank, Clayton, Survival Clayton, Gumbel, and SJC copulas. We filter out margins by using the EGARCH model for return series and GARCH model for volume series. Evidence of significant symmetric dependence between return-volume is not found due to insignificance of student-t and Frank copula parameters. In a return-volume relationship, coefficients of lower tail dependence are significant for Bitcoin, Ripple, and Litecoin which means that low returns are followed by low volumes. Lower tail dependence for the return-volume relationship is stronger than the upper tail dependence for Bitcoin, Ripple, and Litecoin. Moreover, for negative return-volume, left tail dependence coefficients are significant for Ripple and Litecoin, which means that high returns are followed by low volumes for Ripple and Litecoin. Our investigation shows that investors (buyer or seller) are very careful in extreme market conditions for both Ripple and Litecoin. Extreme upper tail and lower tail dependence coefficients are insignificant for Ethereum.

Audil Rashid Khaki, Somar Al-Mohamad, Walid Bakry, Nasser Elkanj

No abstract is available for this record.

Xinwen Ni, Taojun Xie, Wolfgang Karl Härdle, Xiaorui Zuo

Abstract Cryptocurrency markets are highly sensitive to regulatory changes, often experiencing sharp price fluctuations in response to new policies and government interventions. Despite this, existing market indices fail to adequately capture the risks associated with regulatory uncertainty. In this paper, we introduce the Cryptocurrency Regulatory Risk Index (CRRIX), a machine learning-based index designed to quantify the impact of regulatory developments on cryptocurrency markets. Our methodology employs Latent Dirichlet Allocation (LDA) to classify policy-related news articles from major cryptocurrency news platforms, providing an objective measure of regulatory risk. We find that the CRRIX exhibits strong synchronicity with VCRIX, a cryptocurrency volatility index, suggesting that regulatory uncertainty plays a significant role in driving market fluctuations. Our results indicate that regulatory risk is a leading factor in market volatility, with major policy shifts triggering significant market movements. The proposed regulatory risk index provides a novel approach to quantifying policy uncertainty in the cryptocurrency sector, offering valuable insights for market participants navigating this rapidly changing environment.

Roman Matkovskyy, Akanksha Jalan

No abstract is available for this record.

Yukun Liu, Jinfei Sheng, W. Wang

No abstract is available for this record.

Nektarios Aslanidis, Aurelio F. Bariviera, Alejandro Pérez-Laborda

This paper studies the dynamic market linkages among cryptocurrencies during August 2015 - July 2020 and finds a substantial increase in market linkages for both returns and volatilities. We use different methodologies to check the different aspects of market linkages. Financial and regulatory implications are discussed.

Cyn‐Young Park, Shu Tian, Bo Zhao

No abstract is available for this record.

Yoontae Jeon, Laleh Samarbakhsh, Kenji Hewitt

No abstract is available for this record.

Daniele Bianchi, Massimo Guidolin, Manuela Pedio

In this paper we take an empirical asset pricing perspective and investigate the dominant view (possibly, an instinctive reflection of the media hype surrounding the surge of Bitcoin valuations) that cryptocurrencies represent a new asset class, spanning risks and payoffs sufficiently different from the traditional ones. Methodologically, we rely on a flexible dynamic econometric model that allows not only time-varying coeficients, but also allow that the entire forecasting model be changing over time. We estimate such model by looking at the time variation in the exposures of major cryptocurrencies to stock market risk factors (namely, the six Fama French factors), to precious metal commodity returns, and to cryptocurrency-specific risk-factors (namely, crypto-momentum, a sentiment index based on Google searches, and supply factors, i.e., electricity and computer power). The main empirical results suggest that cryptocurrencies are not systematically exposed to stock market factors, precious metal commodities or supply factors with the exception of some occasional spikes of the coefficients during our sample. On the contrary, crypto assets are characterized by a time-varying but significant exposure to a sentiment index and to crypto-momentum. Despite the lack of predictability compared to traditional asset classes, cryptocurrencies display considerable diversification power in a portfolio perspective and as such they can lead to a moderate improvement in the realized Sharpe ratios and certainty equivalent returns within the context of a typical portfolio problem.

Devesh Chandra, Pranav Tyagi, Radhe Shyam Gupta, Aayush Mohan Saxena · 5 authors

  The application of machine learning algorithms in predicting cryptocurrency prices has gained significant attention in recent years. Researchers have explored various approaches such as recurrent neural networks, deep learning neural networks, Bayesian regression, k-nearest neighbor, support vector machine, and other algorithms to forecast the prices of cryptocurrencies like Bitcoin, Ethereum, Dogecoin and Litecoin. This paper will draw on established literature on price prediction using machine learning, including studies on NFT sales predictability, NFT sale price fluctuations prediction, gold price prediction, and silver price forecasting. The research paper has focused on utilizing high-dimensional features, time-series analysis, as well as the comparison of different statistical models and machine learning algorithms. Additionally, the prediction models have incorporated factors such as market liquidity, exchange market dynamics. While the literature acknowledges the potential of machine learning in cryptocurrency price prediction, gold, silver and NFT’s there is a recognized gap in the application of these techniques across a broader range of cryptocurrencies. The proposed methodology will integrate various machine learning models and statistical methods to predict the prices of cryptocurrencies, gold, silver, and NFTs, taking into account factors such as market trends, trade networks and visual features. Furthermore, the studies emphasize the importance of feature engineering, sample dimension engineering, and the use of various machine learning techniques to enhance the accuracy and stability of cryptocurrency price predictions. As the cryptocurrency market continues to expand, there is a need for further research to develop robust machine learning models that can effectively forecast the prices of diverse cryptocurrencies, contributing to the advancement of this field.

Bingbing Dong, Lei Jiang, Jinyu Liu, Yifeng Zhu

We examine how liquidity affects cryptocurrency market efficiency and study commonalities in anomaly performance in cryptocurrency market. Based on the unique features of cryptocurrencies, we build a model with anonymous traders valuing cryptocurrencies as payments for goods and investment assets, and find that decreases in funding liquidity translate into lower asset liquidity in the cryptocurrency market. Empirically, we observe that many widely recognized stock market anomalies also exist in the cryptocurrency market, though some have opposite long/short legs. We also find supportive evidence that a decrease in cryptocurrency liquidity enhances anomaly returns while preventing the cryptocurrency market from achieving efficiency.

Daniel Felix Ahelegbey, Paolo Giudici, Fatemeh Mojtahedi

No abstract is available for this record.

Johannes Jakubik, Abdolreza Nazemi, Andreas Geyer-Schulz, Frank J. Fabozzi

In this paper, we investigate how a deep learning machine learning model can be applied to improve Bitcoin price forecasting and trading by incorporating unstructured information from financial news. The two-stage model we propose that includes financial news significantly outperforms machine learning models without financial news. In the first stage, we leverage long short-term memory (LSTM) networks to extract structured information from financial news. In the second stage, we apply machine learning models with structured input from financial news to the prediction of Bitcoin prices. In addition to the superior performance relative to machine learning models without input from financial news, we find that the out-of-time rate of return attained with the proposed forecasting system is substantially higher than for a buy-and-hold strategy. Our study highlights how combining deep learning and financial news offers investors and traders support for the monetization of unstructured data in finance.

Alexander Thoma

This paper investigates the risk and return properties of a trading strategy for the cryptocurrency market. The main predictive power for portfolio formation comes from a simple prospect theory model that only uses price information readily available. The dataset consists of a large body of cryptocurrencies from 2014 to 2020. I find a strong outperformance over the market, even after controlling for known predictors. Factor regressions with a cryptocurrency three-factor model further reveal significant alphas. Robustness test emphasize the legitimacy of the strategy. On average, cryptocurrencies with a high (low) prospect theory value earn low (high) subsequent returns. Interestingly, traders in the cryptocurrency market seem to assess the attractiveness of cryptocurrency in a way described by prospect theory. Mechanical tests of the model show that probability weighting is a main driver behind this assessment. Cryptocurrencies with a high prospect theory value tend to be highly positively skewed. This skewness could be the reason why the cryptocurrency seems attractive to traders, similar to lottery-like gambles.

Daniele Bianchi, Luca Rossini, Matteo Iacopini

We study the cross-sectional interdependence between returns on cryptocurrency pairs and deviations of Tether USD from its parity to the U.S. dollar. Methodologically, we propose a large-scale Bayesian Vector Autoregressive (BVAR) model which features a global-local shrinkage prior for cross-pairs return correlations. Empirically, we show that deviations from the USDT/USD parity significantly and positively correlate with future returns on cryptocurrency pairs, conditional on both aggregate and asset-specific trading activity. A simple long-only rotational investment strategy which exploits the exposure to the lagged USDT/USD deviations outperforms out-of-sample passive benchmark investments in Bitcoin and a value-weighted market index.

Chamil W. Senarathne, Wei Jianguo

This paper sets out to explore whether the investor herding in the cryptocurrency market induces correlations in cryptocurrency returns using the methodology of Chang et al. (2000) and Galariotis et al. (2015) from a daily data sampling period of 3/30/2015 to 5/24/2019. The initial regression results show that the cross-sectional absolute deviation of return can only be explained by GSCI oil and gold index return, but no relationship exists between cross-sectional absolute deviation of return and other regression variables, such as return on CCi30, US equity risk premium and US/Euro exchange rate return. The herding regression results under normal market condition show that a strong tendency exists to herd on non-fundamental information that explains cross-sectional absolute deviation of returns. As such, cryptocurrency returns cannot be predicted on the basis of fundamental economic information (e.g., major macroeconomic announcements). Herding on non-fundamental information is found to be more pronounced during an upward-trending period of the market and other than upward-trending period. No signs of herding on fundamental information could be observed under other market conditions. Although the theory suggests that herding on non-fundamental information results in more efficient outcomes, the above findings do not encourage the diversification of traditional assets with cryptocurrency on the basis of low correlation. Since cryptocurrency lacks intrinsic value, the exchange is shown to provide a pseudo-efficient trading platform for speculative investors. Implications for future research are discussed.

Jinan Liu, Sajjadur Rahman, Apostolos Serletis

Abstract In this paper, we use a bivariate structural VAR to investigate risk spillovers from the cryptocurrency market to standard financial markets. We investigate the effects of cryptocurrency shocks on key financial markets, including the stock, bond, gold and foreign exchange markets. The results show that cryptocurrency shocks do not have statistically significant effects on standard financial markets except for the bond market. This is consistent with most of the existing literature that argues that cryptocurrencies are mostly a new and different asset class, not related to standard factors.

Lennart Ante, Ingo Fiedler

No abstract is available for this record.