Blockchain Papers

Alex de Vries

No abstract is available for this record.

Prithviraj Lakkakula, David W. Bullock, William W. Wilson

Abstract Asymmetric information is prevalent in the grain and oilseed markets. This paper demonstrates the benefits of blockchain technology to mitigate asymmetric information about the soybean's protein quality between sellers and buyers. We use decision trees to model information asymmetry under both conventional and blockchain scenarios. The results suggest that asymmetric information can be mitigated with a blockchain, resulting in substantial premiums (40–60 cents per bushel of soybeans). These results could have significant implications for the grain and oilseed industry in order to decrease transaction costs, to improve market efficiency, and to prioritize strategies for the procurement of soybeans. JEL CLASSIFICATION O33 Q13; Q17

Nassim Dehouche

A reputation of high volatility accompanies the emergence of Bitcoin as a financial asset. This paper intends to nuance this reputation and clarify our understanding of Bitcoin's volatility. Using daily, weekly, and monthly closing prices and log-returns data going from September 2014 to January 2021, we find that Bitcoin is a prime example of an asset for which the two conceptions of volatility diverge. We show that, historically, Bitcoin allies both high volatility (high Standard Deviation) and high predictability (low Approximate Entropy), relative to Gold and S&P 500. Moreover, using tools from Extreme Value Theory, we analyze the convergence of moments, and the mean excess functions of both the closing prices and the log-returns of the three assets. We find that the closing price of Bitcoin is consistent with a generalized Pareto distribution, when the closing prices of the two other assets (Gold and S&P 500) present thin-tailed distributions. However, returns for all three assets are heavy tailed and second moments (variance, standard deviation) non-convergent. In the case of Bitcoin, lower sampling frequencies (monthly vs weekly, weekly vs daily) drastically reduce the Kurtosis of log-returns and increase the convergence of empirical moments to their true value. The opposite effect is observed for Gold and S&P 500. These properties suggest that Bitcoin's volatility is essentially an intra-day and intra-week phenomenon that is strongly attenuated on a weekly time-scale, and make it an attractive store of value to investors and speculators, but its high standard deviation excludes its use a currency.

Parthajit Kayal, G. Balasubramanian

This article investigates the excess volatility in Bitcoin prices using an unbiased extreme value volatility estimator. We capture the time-varying nature of the excess volatility using bootstrap, multi-horizon, sub-sampling and rolling-window approaches. We observe that Bitcoin price changes are almost efficient. Although Bitcoin prices exhibit high volatility and show signs of excess volatility for a few periods, it is decreasing over time. After controlling for the outliers, we also notice that the Bitcoin market shows signs of increasing maturity. Overall, Bitcoin prices show a sign of increasing efficiency with decreasing volatility. Our findings have implications for investors making investment decisions and for regulators making policy choices.

Ahmed Jeribi, Achraf Ghorbel

Purpose The purpose of this paper is threefold. First, it models and forecasts the risk of the five leading cryptocurrencies, stock market indices (developed and BRICS) and gold returns. Second, it conducts different backtesting procedures forecasts. Third, it focuses on the hedging potential of cryptocurrencies and gold. Design/methodology/approach The authors used the generalized autoregressive score (GAS) models to model and forecast the risk of cryptocurrencies, stock market indices and gold returns. They conduct different backtesting procedures of the 1% and 5%-value-at-risk (VaR) forecasts. They also use the generalized orthogonal generalized autoregressive conditional heteroskedasticity (GO-GARCH) model to explore the hedging potential of cryptocurrencies by estimating the dynamic conditional correlation between cryptocurrencies and gold, on the one hand, and stock markets on the other hand. Findings When conducting different backtesting procedures of VaR, our finding suggests that Bitcoin has the highest VaR among cryptocurrencies and Gold and the BRICS indices returns have lower VaR compared to the developed countries. Finally, we provide evidence that the risks among developed stock markets can be hedged by Bitcoin and Gold. Bitcoin can be considered as the new Gold for these economies. Unlike Bitcoin, Gold can be considered as a hedge for Chinese and Indian investors. However, Gold and Bitcoin can be considered as diversifier assets for the other BRICS economies while Dash and Monero are diversifier assets for developed stock markets. Originality/value The first paper's empirical contribution lies in analyzing optimal forecast models for cryptocurrencies (other than Bitcoin) returns and risk. The second contribution consists of studying the hedging potential of five leading cryptocurrencies. To the best of our knowledge, no previous studies have investigated the role of cryptocurrencies for BRICS investors.

Thanasis Stengos

The emergence of Bitcoin and other cryptocurrencies has led to an explosion of trading and speculation in once nontraditional markets [...]

Vaibhav Aggarwal

Purpose Bitcoin and Ethereum, although the most prominent cryptocurrencies, carry a high ticker price. Many investors carry an inherent bias against high price ticker securities and prefer only low prices securities. This paper aims to help market players generate adequate risk-adjusted returns by investing in only lower-priced cryptocurrencies. Design/methodology/approach The pairwise bivariate BEKK-GARCH (1,1) model is deployed to capture the short- and long-term volatility linkages between Litecoin, Stellar and Ripple from August 2015 to June 2020. Findings Litecoin is the most influential volatility sender in the basket of these three cryptocurrencies. The portfolio weights indicate that investors can create an optimized two asset portfolio with the lowest exposure to Stellar with Litecoin and Ripple. Market players with a long position in Ripple can have the cheapest hedge by shorting Stellar. Originality/value This study adds to the scant literature on the association between emerging cryptocurrencies and finding optimum portfolio weight and hedge ratios.

Shaista Karim Sadrudin Jaffer

Cryptocurrencies have several features that set them aside from traditional currencies. In terms of market capitalization, the top five cryptocurrencies are considered for the analysis to strengthen the research's validation. The most successful crypto asset, being Bitcoin, possesses several characteristics that pose advantages and disadvantages in the financial markets. Digital convenience ensures the safety and ease of use for Bitcoin users, while decentralization also poses a primary benefit. Extreme volatility and the impact of negative externalities on the value of Bitcoin contribute to the assessment of Bitcoin trends in the market. The recent outbreak of the coronavirus (COVID-19) has shown evidence of influencing Bitcoin prices as the virus is spread across continents, leaving the global financial environment in turmoil. The classification of Bitcoin as a hedge is dependent on various factors, including global economic uncertainty. The extent to which the coronavirus impacts cryptocurrencies' hedging capabilities, especially that of Bitcoin’s, is of particular interest during the 2020 pandemic. Analyzing the literature on the influence of crisis on Bitcoin movement will explain why COVID-19 has had such a significant impact on the global financial markets, especially that of cryptocurrencies. The performance of Bitcoin, Ethereum, XRP, Tether, and Bitcoin Cash is compared to that of seven factors including commodities and indices: gold, USD, S&P 500 index, SSE index, world and emerging markets MSCI indices, and Economic Uncertainty, to better understand the hedging capabilities throughout the time of the crisis. This is done using four different multivariate GARCH specifications that account for the nature of the interaction between the cryptocurrencies and the financial variables. Although previous research finds that Bitcoin should act as a hedge during times of economic turmoil, the performance observed during COVID-19 suggests otherwise.

Büşra YAĞLI, Cem KARTAL

Bitcoin, the most important of all cryptocurrencies, is a currency that is not included in the central monetary system and has a digital format. With the rapid increase in the value of Bitcoin in recent years, it has attracted the attention of investors. Bitcoin, as an alternative to traditional investment tools, has sparked a lot of controversies. In this study, the Bitcoin price relationship between stock market indices and the BRICS countries belonging to Turkey is intended to be detected. In this direction of Bitcoin 01.01.2013-31.12.2019 period to test the relationship between Turkey and the BRICS countries, stock index monthly data are used. After determining the basic statistical properties of the series, cointegration and causality test was performed to determine the financial relationship. ADF (Augmented Dickey-Fuller) unit root tests and stationarity analysis are applied, and then the existence of long-term relationships between stock exchanges is explained with the Johansen cointegration test. The Vector Error Correction Model (VECM) was used to analyze whether the long-term relationship is in equilibrium. Also, short-term relationships were determined by Granger causality analysis. The analysis performed between variables were identified as a result of a long-term relationship, Russia (MOEX) and Turkey (BIST100) was found to be the cause of the stock market index of Bitcoin. It has been determined that Bitcoin is the cause of China (SHANGAI) exchange. In these exchanges, it has been observed that the change in Bitcoin prices in the short term affects investment decisions.

Walid M.A. Ahmed

No abstract is available for this record.

Muhammad Umar, Chi‐Wei Su, Syed Kumail Abbas Rizvi, Xuefeng Shao

No abstract is available for this record.

Muhammad Abubakr Naeem, Saba Sehrish, Mabel D. Costa

Purpose This study aims to estimate the time–frequency connectedness among global financial markets. It draws a comparison between the full sample and the sample during the COVID-19 pandemic. Design/methodology/approach The study uses the connectedness framework of Diebold and Yilmaz (2012) and Barunik and Krehlik (2018), both of which consider time and frequency connectedness and show that spillover is specific to not only the time domain but also the frequency (short- and long-run) domain. The analysis also includes pairwise connectedness by making use of network analysis. Daily data on the MSCI World Index, Barclays Bloomberg Global Treasury Index, Oil future, Gold future, Dow Jones World Islamic Index and Bitcoin have been used over the period from May 01, 2013 to July 31, 2020. Findings This study finds that cryptocurrency, bond and gold are hedges against both conventional stocks and Islamic stocks on average; however, these are not “safe havens” during an economic crisis, i.e. COVID-19. External shocks, such as COVID-19, strengthen the return connectedness among all six financial markets. Research limitations/implications For investors, the study provides important insights that during external shocks such as COVID-19, there is a spillover effect, and investors are unable to hedge risk between conventional stocks and Islamic stocks. These so-called safe haven investment alternatives suffer from the similar negative impact of systemic financial risk. However, during an external shock such as COVID-19, cryptocurrencies, bonds and gold can be used to hedge risk against conventional stocks, Islamic stocks and oil. Moreover, the findings imply that by engaging in momentum trading, active investors can gain short-run benefits before the market processes any new information. Originality/value The study contributes to the emergent literature investigating the connectedness among financial markets during the COVID-19 pandemic. It provides evidence that the return connectedness among six global financial markets, namely, conventional stocks, Islamic stocks, bond, oil, gold and cryptocurrency, is extremely strong. From a methodological standpoint, this study finds that COVID-19 pandemic shock has a significant short-run impact on the connectedness among financial markets.

Serkan Aras

No abstract is available for this record.

Mayukh Samaddar, Rishiraj Saha Roy, Sayantani De, Raja Karmakar

Machine learning is growing rapidly and has made many theoretical breakthroughs which find its application in many fields. Bitcoin is a very secure, decentralized, peer to peer currency with no third-party involvement. The price prediction of Bitcoin in the following years is a difficult task. The objective is to take a dig in the prediction of the future prices, dealing with real world data. A comparative study of the results produced by different machine learning models, along with graphs for epoch vs price, error and accuracy for each model using both linear and non-linear functions is done. We are using both neural network algorithms, such as artificial neural network (ANN), recurrent neural network (RNN) and convolutional neural network (CNN), as well as some famous supervised learning algorithms such as Random Forest (RF) and k-nearest neighbors (k-NN), to form the analysis. The time price prediction graphs and the epoch loss accuracy graphs are used for the analysis of each algorithm working on the same data and produces different results. Finally, the best suited algorithm are used for the prediction of future Bitcoin price.

Adlane Haffar, Éric Le Fur

No abstract is available for this record.

Sashikanta Khuntia, J. K. Pattanayak

Purpose This study broadly attempts to explore adaptive or dynamics patterns of calendar effects existed in the cryptocurrency market as per the adaptive market hypothesis (AMH) framework. Another agendum of this study is to investigate the quantum of extra returns which may result from the presence of calendar effects. Design/methodology/approach The present study considers both parametric and non-parametric approaches to verify calendar effects empirically. Specifically, this study has implemented Generalised Autoregressive Conditional Heteroscedasticity (1, 1) and Kruskal–Wallis tests in the rolling window approach to reveal adaptive patterns of calendar effects. Additionally, the present study has used the implied trading strategy to evaluate the volume of excess returns resulted from calendar effects than buy-and-hold (BH) strategy. Findings The overall results of the current study exhibit that calendar effect in the cryptocurrency market is dynamic rather than static which indicates the calendar effect is a time-varying phenomenon. Moreover, this study also confirmed that ITS is not suitable to obtain extra returns despite the existence of calendar effects. Research limitations/implications The present study has covered some broad aspects of calendar anomalies in the cryptocurrency market, keeping aside certain other limitations which need to be addressed in the following dimensions. Future studies may aim at addressing issues like, Turn-of-the-Year effect, Halloween effect, weather effect, and Month-of-the-Year effects, and try to explore the reasons of presence of dynamic patterns of calendar effects. Practical implications The significant implication of this study is that it alerts investors about market return predictability due to calendar patterns or effects in different periods. It also suggests the period in which the ITS can perform better than the BH strategy. Originality/value It is the first study in the cryptocurrency literature which has adopted the AMH framework to verify adaptive calendar effects or anomalies. Furthermore, this study, instead of a mere examination of the presence of calendar effects, has evaluated the potential of calendar effects to produce extra returns through trading strategies.

David Vidal-Tomás

No abstract is available for this record.

Pavel Ciaian, d’Artis Kancs, Miroslava Rajčániová

This paper studies to what extent the cost of operating a proof-of-work\nblockchain is intrinsically linked to the cost of preventing attacks, and to\nwhat extent the underlying digital ledger security budgets are correlated with\nthe cryptocurrency market outcomes. We theoretically derive an equilibrium\nrelationship between the cryptocurrency price, mining rewards and mining costs,\nand blockchain security outcomes. Using daily crypto market data for 2014-2021\nand employing the autoregressive distributed lag approach - that allows\ntreating all the relevant moments of the blockchain series as potentially\nendogenous - we provide empirical evidence of cryptocurrency price and mining\nrewards indeed being intrinsically linked to blockchain security outcomes.\n

Larisa Yarovaya, Ahmed H. Elsayed, Shawkat Hammoudeh

No abstract is available for this record.

Tak Kuen Siu

This paper aims to study the impacts of long memory in conditional volatility and conditional non-normality on market risks in Bitcoin and some other cryptocurrencies using an Autoregressive Fractionally Integrated GARCH model with non-normal innovations. Two tail-based risk metrics, namely Value at Risk (VaR) and Expected Shortfall (ES), are adopted to study the tail behaviour of market risks in Bitcoin and some other cryptocurrencies. Empirical investigations for the tail behaviour based on real exchange rate data of cryptocurrencies are conducted. An extreme-value-theory-based approach is used to study potential improvements in the estimation for the risk metrics under GARCH-type models. The possibility of explosive regimes in cryptocurrencies’ volatilities is examined using Markov-switching GARCH models.

Panos Fousekis, Vasilis Grigoriadis

Purpose This paper aims to identify and quantify directional predictability between returns and volume in major cryptocurrencies markets. Design/methodology/approach The empirical analysis relies on the cross-quantilogram approach that allows one to assess the temporal (lag-lead) association between two stationary time series at different parts of their joint distribution. The data are daily prices and trading volumes from four markets (Bitcoin, Ethereum, Ripple and Litecoin). Findings Extreme returns either positive or negative tend to lead high volume levels. Low levels of trading activity have in general no information content about future returns; high levels, however, tend to precede extreme positive returns. Originality/value This is the first work that uses the cross-quantilogram approach to assess the temporal association between returns and volume in cryptocurrencies markets. The findings provide new insights about the informational efficiency of these markets and the traders’ strategies.

Barbara Będowska-Sójka, Agata Kliber

No abstract is available for this record.

Mohamed Arbi Madani, Zied Ftiti, Waël Louhichi, Hachmi Ben Ameur

We investigate intraday hedging and the safe haven role of Bitcoin for stocks, currencies, and oil. The hedge concept depends on non-correlation or negative interaction, on average, while the safe haven concept depends on non-correlation or negative correlation in times of market turmoil. We look at Bitcoin’s ability to be a hedge or safe haven asset with standard financial assets by considering a short investment horizon using high frequency data. Accordingly, we propose a new measure, the q-detrending moving average cross-correlation coefficient, to characterise intraday market interdependence between Bitcoin and these assets during medium and extreme movements. During medium fluctuations, Bitcoin is a weak hedge against currencies, oil, and stocks. During high fluctuations, we find a negative relationship between Bitcoin and oil, meaning Bitcoin can serve as a safe haven against extreme down movements in this market. However, Bitcoin is a weak safe haven asset for the other two markets.

Ngô Thái Hưng

Purpose This study aims to analyze the dynamic relationship between the Bitcoin market and the conventional asset classes in India Design/methodology/approach This paper aims to cast light on the dynamic linkages between Bitcoin prices and other conventional asset classes in India by using the wavelet transform frameworks, which can allow us to analyze components of time series without losing the information. To do that, the techniques used with the data set include wavelet-based covariance, correlation, coherence spectrum, continuous power spectrum and Granger causality test. Findings The findings of the study suggest that interrelationships between Bitcoin and the key financial asset returns are statistically significant at low, medium and high frequencies. This study also finds the existence of the unidirectional connectedness between Bitcoin the other assets in India. Practical implications The outcome of the analysis calls for substantial policy implications for investors, portfolio management in India. This research on the existence of the interconnectedness between Bitcoin and other conventional asset classes in a specific country context, India can, therefore, make a significant contribution to the contemporary debate about the speculative nature of the cryptocurrencies. It casts light on whether Bitcoin provides any diversification and risk management benefits for Indian, as well as global investors. Originality/value To the best of the author’s knowledge, this is the first paper investigating the interrelatedness between Bitcoin and key conventional asset classes in India. This research makes methodological advancements by using the wavelet coherence transform. The findings provide empirical bases from which to deal with issues regarding hedging purposes and optimal portfolio allocation for an increasing number of investors in the Indian context. Therefore, the main contribution of this study to related literature in this field is significant.