Blockchain Papers

Evrim Akdoğu, Şerif Aziz Şimşir

No abstract is available for this record.

Mustafa Kevser

The aim of this research is to investigate the causality between Global Economic Political Uncertainty (GEPU) and Geopolitical Risk (GPRT) and Bitcoin Energy Consumption (BTCE). In order to test the stationarity of the variables, the Lee-Strazich unit root test, which takes into account the structural breaks, was used, and the causality relationship between the variables was analyzed with the Hatemi-J (2012) causality test. Monthly data between May 2011 and February 2022 were used in the research. According to the results obtained from the research, geopolitical risk and global economic policy uncertainity are effective on bitcoin energy consumption. In addition, it has been determined that the negative effects of geopolitical risk and global uncertainties are more dominant. The results show that the demand for bitcoin, which is considered an alternative financial asset class, and accordingly bitcoin energy consumption, increases in case of global risks and economic uncertainties.

Ümit Cali, Murat Kuzlu, D. Jonathan Sebastian-Cardenas, Onur Elma · 6 authors

Decarbonization of energy systems has been a recent trend during the last two decades where large-scale renewable energy sources (RES) are integrated into the modern power systems. Various countries have developed new energy policy instruments, such as Renewable Energy Certificates (RECs), to promote the growth of RES. RECs are tradable, non-tangible assets, which have a monetary value. Tracking and certification of the origin of an energy resource regardless of its type (e.g., a conventional power plant or RES) are a critical operation. In addition to the certification of origin, trading transactions must be performed using a secure method. Energy industry participants need to secure the data and applications related to RECs. Digitalization technologies such as artificial intelligence (AI) and distributed ledger technology (DLT) are among the most popular and promising options. DLT is a perfect framework that can support such REC functionalities. This paper addresses the cybersecurity aspects in REC trading using blockchain and distributed ledger technology, considering detailed cybersecurity perspectives and aspects of adopting technology from an organizational perspective.

Syed Ali Raza, Komal Akram Khan, Khaled Guesmi, Ramzi Benkraiem

No abstract is available for this record.

Yosra Ghabri, Luu Duc Toan Huynh, Muhammad Ali Nasir

Abstract In the context of the COVID‐19's outbreak and its implications for the financial sector, this study analyses the aspect of hedging and safe‐haven under the pandemic. Drawing on the daily data from 02 August 2019 to 17 April 2020, our key findings suggest that the contagious effects in financial assets' returns significantly increased under COVID‐19, indicating exacerbated market risk. The connectedness spiked in the middle of March, consistent with lockdown timings in major economies. The effect became severe with the WHO's declaration of a pandemic, confirming negative news effects. The return connectedness suggests that COVID‐19 has been a catalyst of contagious effects on the financial markets. The crude oil and the government bonds are however not as much affected by the spillovers as their endogenous innovation. In terms of spillovers, we do find the safe‐haven function of Gold and Bitcoin. Comparatively, the safe‐haven effectiveness of Bitcoin is unstable over the pandemic. Whereas, GOLD is the most promising hedge and safe‐haven asset, as it remains robust during the current crisis of COVID‐19 and thus exhibits superiority over Bitcoin and Tether. Our findings are useful for investors, portfolio managers and policymakers interested in spillovers and safe havens during the current pandemic.

Ranjan Aneja, Robert Dygas

Literature review regarding digital currencies and cryptocurrencies in the New Global Financial System - 1

Jihed Ben Nouir, Hayet Ben Haj Hamida

No abstract is available for this record.

Khreshna Syuhada, Arief Rachman Hakim, Djoko Suprijanto, Intan Muchtadi-Alamsyah · 5 authors

No abstract is available for this record.

Marko Stankovic, Nebojša Bačanin, Miodrag Živković, Luka Jovanović · 6 authors

Cryptocurrencies have established a firm position in the economic world in the past decade, with thousands of distinctive currencies available for electronic payments. The majority of cryptocurrencies, however, experience extremely volatile price perturbations, drastically affecting investors and traders. To address this problem, this paper proposes long short-term memory approach tuned by salp swarm metaheuristics. This hybrid model has been validated on a benchmark financial dataset, and the outcomes have been compared to other cutting-edge methods. The results suggest that the proposed method outperformed the competitors, showing significant potential in time-series prediction tasks.

David Iheke Okorie, Boqiang Lin

No abstract is available for this record.

Dora Almeida, Andreia Dionísio, Isabel Vieira, Paulo Ferreira

Cryptocurrency investments are often perceived as uncertain and risky. In this study, we assessed if this is indeed the case, using a sample of seven cryptocurrencies and considered a period that encompassed the first real global shock in the life of these relatively new financial assets, the COVID-19 pandemic. Uncertainty was evaluated using Shannon’s symbolic entropy. To measure risk, we use value-at-risk and conditional value-at-risk. The results indicate that, except for Tether, the analyzed cryptocurrencies’ returns exhibited similar patterns of uncertainty and risk. Levels of uncertainty were close to the maximum values, but high uncertainty is not always associated with high risk. During the pandemic crisis, uncertainty increased while risk decreased, suggesting that the considered assets may have safe haven properties.

Sarfaraz Hashemkhani Zolfani, Hassan Mehtari Taheri, Mahmoud Gharehgozlou, Alireza Farahani

No abstract is available for this record.

Geumil Bae, Jang Ho Kim

The cryptocurrency market is understood as being more volatile than traditional asset classes. Therefore, modeling the volatility of cryptocurrencies is important for making investment decisions. However, large swings in the market might be normal for cryptocurrencies due to their inherent volatility. Deviations, along with correlations of asset returns, must be considered for measuring the degree of market anomaly. This paper demonstrates the use of robust Mahalanobis distances based on shrinkage estimators and minimum covariance determinant for observing anomaly scores of cryptocurrencies. Our analysis shows that anomaly scores are a critical complement to volatility measures for understanding the cryptocurrency market. The use of anomaly scores is further demonstrated through portfolio optimization and scenario analysis.

Yingying Huang, Kun Duan, Andrew Urquhart

No abstract is available for this record.

Xingzhi Qiao, Huiming Zhu, Yiding Tang, Cheng Peng

No abstract is available for this record.

Chanapon Phasook, Jantima Polpinij, Bancha Luaphol

This work aims to present a comparative study method of closed-price prediction for cryptocurrencies namely machine learning-based and deep learning-based methods. Three data normalization techniques in the data pre-processing stage were also compared. They are log scaling, min-max, and z-score normalization. In the machine learning-based method, support vector regression (SVR) was used to develop the predictive model, whereas long-short term memory (LSTM) was used in the deep learning-based method to develop the predictive model. In addition, three datasets are used in this study namely Bitcoin (BTC), Ethereum (ETH), and Litecoin (LTC). The results of evaluating the predictive models using RMSE and MAPE revealed that SRV with RBF kernel produced slightly better results than LSTM. Compared to other data normalization methods, log scaling normalization produced outcomes that are more satisfactory.

Haruna Umar Yahaya, John Sunday Oyinloye, Samuel Olorunfemi Adams

The future of e-money is crypocurrencies, it is the decentralize digital and virtual currency that is secured by cryptography. It has become increasingly popular in recent years attracting the attention of the individual, investor, media, academia and governments worldwide. This study aims to model and forecast the volatilities and returns of three top cryptocurrencies, namely; Bitcoin, Ethereum and Binance Coin. The data utilized in the study was extracted from the higher market capitalization at 31st December, 2021 and the data for the period starting from 9th November, 2017 to 31st December 2021. The Generalised Autoregressive conditional heteroscedasticity (GARCH) type models with several distributions were fitted to the three cryptocurrencies dataset with their performances assessed using some model criteria. The result shows that the mean of all the returns are positive indicating the fact that the price of this three crptocurrencies increase throughout the period of study. The ARCH-LM test shows that there is no ARCH effect in volatility of Bitcoin and Ethereum but present in Binance Coin. The GARCH model was fitted on Binance Coin, the AIC and log L shows that the CGARCH is the best model for Binance Coin. Automatic forecasting was perform based on the selected ARIMA (2,0,1), ARIMA (0,1,2) and the random walk model which has the lowest AIC for ETH-USD, BNB-USD and BTC-USD respectively. This finding could aid investors in determining a cryptocurrency's unique risk-reward characteristics. The study contributes to a better deployment of investor’s resources and prediction of the future prices the three cryptocurrencies.

Stefan Hubrich

Investors looking to integrate digital assets into a traditional, diversified multi-asset portfolio need to formulate appropriate risk and return assumptions for them. Using the case of bitcoin, we argue that due to the short duration of available returns and the extreme volatility of the asset, historical returns are an unreliable basis for directly formulating forward return expectations. We also show that bitcoin’s return characteristics require an emphasis on such portfolio construction considerations as rebalancing frequency that are often peripheral in traditional asset allocation studies. We then demonstrate an allocation approach that addresses these concerns. The main idea is to extract required return thresholds for a small bitcoin investment (1% or 5%) that need to be underwritten by the investor, rather than relying on explicit return expectations as the input. We show that these return thresholds are surprisingly low, illustrating that the broader multi-asset portfolio perspective is critical when making investment decisions regarding high-volatility assets like bitcoin.

Changqing Luo, Lurun Pan, Binwei Chen, Huiru Xu

In recent years, digital currencies have flourished on a considerable scale, and the markets of digital currencies have generated a nonnegligible impact on the whole financial system. Under this background, the accurate prediction of cryptocurrency prices could be a prerequisite for managing the risk of both cryptocurrency markets and financial systems. Considering the multiscale attributes of cryptocurrency price, we match the different machine learning algorithms to corresponding multiscale components and construct the ensemble prediction models based on machine learning and multiscale analysis. The Bitcoin price series, respectively, from 2017/11/24 to 2020/4/21 and 2020/4/22 to 2020/11/27, is selected as the training and prediction datasets. The empirical results show that the ensemble models can achieve a prediction accuracy of 95.12%, with better performance than the benchmark models, and the proposed models are robust in upward and downward market conditions. Meanwhile, the different algorithms are applicable for components with varying time scales.

Héla Mzoughi, Ramzi Benkraiem, Khaled Guesmi

No abstract is available for this record.

Azeez A. Oyedele, Anuoluwapo Ajayi, Lukumon O. Oyedele, Sururah A. Bello · 5 authors

The emergence of cryptocurrencies has drawn significant investment capital in recent years with an exponential increase in market capitalization and trade volume. However, the cryptocurrency market is highly volatile and burdened with substantial heterogeneous datasets characterized by complex interactions between predictors, which may be difficult for conventional techniques to achieve optimal results. In addition, volatility significantly impacts investment decisions; thus, investors are confronted with how to determine the price and assess their financial investment risks reasonably. This study investigates the performance evaluation of a genetic algorithm tuned Deep Learning (DL) and boosted tree-based techniques to predict several cryptocurrencies' closing prices. The DL models include Convolutional Neural Networks (CNN), Deep Forward Neural Networks, and Gated Recurrent Units. The study assesses the performance of the DL models with boosted tree-based models on six cryptocurrency datasets from multiple data sources using relevant performance metrics. The results reveal that the CNN model has the least mean average percentage error of 0.08 and produces a consistent and highest explained variance score of 0.96 (on average) compared to other models. Hence, CNN is more reliable with limited training data and easily generalizable for predicting several cryptocurrencies' daily closing prices. Also, the results will help practitioners obtain a better understanding of crypto market challenges and offer practical strategies to lower risks.

Xingyi Li, Kai Gan, Qi Zhou

No abstract is available for this record.

Jingjing Li, Xinge Rao, Xianyi Li, Sihai Guan

In recent years, the bitcoin market has developed rapidly and has been recognized as a new type of gold by many investors. It may replace gold as a hedge against inflation and become a new investment asset for financial management. The investment relationship with gold has increasingly important research value and practical significance. This paper modeled daily price flow data from 11 September 2016 to 10 September 2021 to help market traders determine whether they need to buy, hold, or sell assets in their portfolios daily. The model predicts price fluctuations through linear regression prediction of machine learning, K-Nearest Neighbor (KNN) algorithm. In the linear regression prediction, the goodness of fit of gold is 89.44%, and the goodness of fit of Bitcoin is 98.43%. In the test set prediction of KNN algorithm, the goodness of fit of gold is 97.25%, and the goodness of fit of Bitcoin is 95.06%. Based on this, the optimal investment strategy and the initial investment value are obtained. Empirical analysis shows that bitcoin price volatility and gold price volatility have a strong substitution effect; gold and currency used will be a suitable combination of hedging, which will bring momentum for the development of the market economy and become an important force in the sustainable development of a high-quality-driven economy.

Martin Nedved, Ladislav Krištoufek

No abstract is available for this record.