Blockchain Papers

Yizhi Wang, Yu Wei, Brian M. Lucey, Yang Su

No abstract is available for this record.

Grzegorz Dudek, Piotr Fiszeder, Paweł Kobus, Witold Orzeszko

Forecasting cryptocurrency volatility can help investors make better-informed investment decisions in order to minimize risks and maximize potential profits. Accurate forecasting of cryptocurrency price fluctuations is crucial for effective portfolio management and contributes to the stability of the financial system by identifying potential threats and developing risk management strategies. The objective of this paper is to provide a comprehensive study of statistical and machine learning methods for predicting daily and weekly volatility of the following four cryptocurrencies: Bitcoin, Ethereum, Litecoin, and Monero. Several models and forecasting methods are compared in terms of their forecasting accuracy, i.e., HAR (heterogeneous autoregressive), ARFIMA (autoregressive fractionally integrated moving average), GARCH (generalized autoregressive conditional heteroscedasticity), LASSO (least absolute shrinkage and selection operator), RR (ridge regression), SVR (support vector regression), MLP (multilayer perceptron), FNM (fuzzy neighbourhood model), RF (random forest), and LSTM (long short-term memory). The realized variance calculated from intraday returns is used as the input variable for the models. In order to assess the predictive power of the models considered, the model confidence set (MCS) procedure is applied. Our experimental results demonstrate that there is no single best method for forecasting volatility of each cryptocurrency, and different models may perform better depending on the specific cryptocurrency, choice of the error metric and forecast horizon. For daily forecasts, the method that is always found in a set of best models is linear SVR, while for weekly forecasts, there are two such methods, namely FNM and RR. Furthermore, we show that simple linear models such as HAR and ridge regression, perform not worse than more complex models like LSTM and RF. The research provides a useful reference point for the development of more sophisticated models.

Raphael Auer, Giulio Cornelli, Sebastian Doerr, Jon Frost · 7 authors

No abstract is available for this record.

Agata Kliber, Barbara Będowska-Sójka

This paper examines whether cryptocurrencies are hedging instruments for green and non-green energy instruments. We differantiate between cryptocurrencies with two types of consensus mechanisms, Proof-of-work and Proof-of-stake, which reflect the demand for energy used for the coins' confirmation. We obtained dynamic conditional correlations from SV models and apply them to calculate hedge ratios. Based on the sample from January 2019 till December 2022 we find that clean energy sources are better hedges for oil than clean or dirty cryptocurrencies due to high volatility of the latter instruments. Cryptocurrencies are better hedging instruments for oil than for clean energy assets. We also find evidence that investors in clean crytocurrencies are more environmentally aware than those investing in the dirty one.

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

No abstract is available for this record.

Zewen Wu

No abstract is available for this record.

Tim Weingärtner, Fabian Fasser, Pedro Costa, Walter Farkas

Decentralized finance (DeFi) promises a revolution in financial accessibility, transparency, and automation. Yet, its very novelty exposes participants to a number of additional risks and challenges. This study aims to address the risks associated with DeFi, while also conducting a comparative analysis to those of classical/traditional finance (TradFi). After introducing DeFi and its defining characteristics, such as the use of smart contracts, blockchain technology, and decentralized governance, the paper outlines the principal risks associated with DeFi. Drawing insights from an extensive literature review of 200 recent articles, of which 50 were thoroughly analyzed, the study compares risks of DeFi and TradFi, categorizing these into systematic and unsystematic risks. Furthermore, we introduce the ‘risk wheel’, an innovative tool tailored to understand and navigate the subtleties of DeFi risks, finding potential applications in risk assessment, management, and even education. This paper’s primary objective is to provide a detailed and impartial examination of the risks associated with DeFi and their comparison to traditional finance in order to assist stakeholders in making informed decisions and mitigating possible losses.

Yujun Liu, Zhongfei Li, Ramzi Nekhili, Jahangir Sultan

No abstract is available for this record.

M’bakob Gilles Brice

This study examines the price dynamics of Bitcoin and Ethereum between 2013 and 2022 using two distinct approaches: financial market technical analysis and econometric analysis. Financial market technical analysis employs indicators such as the Relative Strength Index (RSI) and the Hull moving average, while econometric analysis involves the Hodrick-Prescott filter and an Autoregressive Distributed Lag (ARDL) model. The study shows that Bitcoin and Ethereum experienced supercycle years in 2013, 2017, and 2021. The Bitcoin cycle, which averages 3.5 years, was particularly emphasized. The impact of the Bitcoin halving is also noteworthy, especially in the formation of supercycle bubbles in 2021, which affected altcoins such as Ethereum. The implications of this extend to portfolio management advice. It is recommended to carefully evaluate portfolio diversification and adopt a proactive regulatory approach, especially during the Bitcoin halving period.

Xiangyang Zou

Based on the global economic downturn, the price of Bitcoin has recovered, and new investors are constantly pouring into the Bitcoin market. To ensure that new investors have a basic understanding of Bitcoin and avoid unnecessary losses, this article will analyze Bitcoin's Trading Mechanisms, Price Influencers, and Trading Recommendations The main research finds that when Bitcoin is used as a currency, commodity, risk asset, and digital gold, the price factors are quite different. For example, when it is used as a risk asset, its price is affected by capital flows, market sentiment, and policy regulation. The multiple nature of Bitcoin will have a greater impact on investors' judgments. Through research, it is found that Bitcoin is still the virtual currency with the largest volume, the largest transaction volume, and the brightest future development prospects. The diversity of its nature brings risks but also brings a variety advantages of to the single currency or other financial products.

Ruolei Zhang

With the volatile price of Bitcoin having received widespread media and investor attention in recent years, this paper surveys the existing literature on Bitcoin which focuses on the differences between Bitcoin and traditional currencies and the impact on the real economy. In doing so, various literature and scholarly insights are discussed to formally clarify the economic implications of Bitcoin in the absence of a centralised entity. A SWOT approach is then used to analyse its strengths, weaknesses, opportunities and risks, before parsing Bitcoin's price trends and making predictions for the future based on specific events and news. Society remains sceptical and uninformed about this cryptocurrency. The evidence suggests that bitcoin returns are significantly slightly inefficient at this stage, but there is also a great deal of uncertainty about the future and it could be on the move.

Hong Yu

In the five years after the launch of bitcoin futures, academics and investors' perceptions have shifted from the early view that they raise the risk of bitcoin to the present acceptance of their ability to serve as derivatives. This change indicates that bitcoin futures have the potential to improve. What influences the bitcoin market has received from bitcoin futures is investigated in this paper. The introduction of bitcoin futures has offered a feasible hedging strategy for bitcoin investors, enhanced the stability and information effectiveness of the bitcoin market, also eased the investment barrier for bitcoin, based on study and comparison of previous research on bitcoin futures. However, because bitcoin futures are the novel type of futures contract, the market is complicated, and investors prefer to trade unregulated futures, whereas regulated futures are traded in considerably lesser quantities due to position limitations. Exchanges that regulate bitcoin futures should consider changing their contract positions to allow more investors to engage in trading while obtaining regulatory protections in to enable the bitcoin futures market to develop more maturely in the future.

Byounghyo Lim, Sol Kim, Ingoo Han

This study analyzes the role of Bitcoin as an investment asset in a global multi-asset portfolio. For portfolio construction, we use a risk-based portfolio strategy that excludes estimates of future expected returns. We derive the optimal asset allocation ratio of Bitcoin included in the global multi-asset portfolio and compare the performance with the general portfolio. We find that the average investment weight of Bitcoin in the portfolio is 1.8%, and the portfolio containing Bitcoin shows superior investment performance compared to the portfolio without Bitcoin.

Yiran Wang

With the advent of 2022, the impact of the COVID-19 pandemic has weakened, the US labor market has recovered, and inflation has been severe, creating the conditions for the Fed to tighten its policies. At the same time, cryptocurrencies as a hot topic in recent years; ETH is one of the most popular cryptocurrencies in the market; this article aims to assess the impact of the Fed's raised interest rates on the yield and volatility of cryptocurrency Ethereum (ETH) based on data on the ETH price and the US dollar/CNY exchange rate since 2022. And further, simulate the impact on the overall cryptocurrency market. This paper constructs VAR and ARMA-GARCH models to analyze ETH returns and volatility variations. The results of these models suggest that the exchange rate rise triggered by the Fed's rate hike has had a negative impact on ETH yields and increased the volatility of its returns. Further, this article recommends that investors should adjust their portfolios according to their risk appetite in an uncertain market environment.

Yurii Kleban, Tetiana Stasiuk

The study examines the problem of modeling and forecasting the price dynamics of crypto currencies. We use machine learning techniques to forecast the price of crypto currencies. The FB Prophet time series model and the LSTM recurrent neural network were selected to implement the study. Using the example of data from Binance (the most popular exchange in Ukraine) for the period from 06.07.2020 to 01.04.2023, prices for Bitcoin, Ethereum, Ripple, and Dogecoin were modeled and forecasted. The recurrent neural network of long-term memory showed significantly better results in forecasting according to the RMSE, MAE, and MAPE criteria, compared to the Naïve model, the traditional ARIMA model, and the FB Prophet results.

Phuvadon Wuthisatian

This paper investigates the performance of cryptocurrencies and market indices. Using the dynamic conditional correlation (DCC) model, the result shows that cryptocurrencies and market indices, contrary to much of the literature, tend to move in the same direction, resulting in little or no benefits in portfolio management. Dividing into the sub-sample period, cryptocurrencies have moved even more strongly with market indices during the recent period after the COVID-19 pandemic, indicating the possibility of no hedging benefit. This paper shows that inclusion of cryptocurrency in a portfolio increases the return as well as volatility, as the risk-adjusted return does not show any sign of improvement. A portfolio comprising the FTSE 100 Index seems to receive the greatest benefit of including cryptocurrencies as the risk-adjusted performance improves.

Firuze Simay Sezgin, Caner Özdurak

This study investigates the impact of terrorist attacks on the price fluctuations of Bitcoin prices and NFT sales. Although the value proposition of cryptocurrencies, Decentralized Finance, and the whole blockchain revolution is a quicker, cheaper, and more transparent kind of finance, various terrorist organizations tend to use cryptocurrency anonymously to finance their terrorist activities around the world by bypassing the banking system of the regulated countries. The analyses reveal that returns of Bitcoin and NFT markets are positively associated with the organization and funding phases of the terrorist attacks but negatively associated with the post-terrorist attack circumstances, meaning that it generates positive abnormal returns (AR) prior to the attack but creates negative AR right after the attack. Furthermore, while the Bitcoin news impact curve (NIC) is nearly symmetric, the NFT NIC is asymmetric, with positive shocks having significantly more impact on future volatility than negative shocks of the same magnitude. Since previous studies claim that terrorist attack news is good news for Bitcoin returns, we will enrich our AR analysis results with NICs results.

Mustafa Özer, Serap Kamışlı, Fatih Temi̇zel, Melik Kamışlı

The aim of this study was to investigate the causal relations between COVID-19 economic supports and Bitcoin markets. For this purpose, we first determined the degree of the integration of variables by implementing Fourier Augmented Dickey–Fuller unit root tests. Then, we carried out both linear (Bootstrap Toda–Yamamoto) and non-linear (Fractional Frequency Flexible Fourier form Toda–Yamamoto) causality tests to consider the nonlinearities in variables, to determine if the effects of multiple structural breaks were temporary or permanent, and to evaluate the unidirectional causality running from COVID-19-related economic supports and the price, volatility, and trading volume of Bitcoin. Our study included 158 countries, and we used daily data over the period from 1 January 2020 and 10 March 2022. The findings of this study provide evidence of unidirectional causalities running from COVID-19-related economic supports to the price, volatility, and trading volume of Bitcoin in most of the countries in the sample. The application of non-linear causality tests helped us obtain more evidence about these causalities. Some of these causalities were found to be permanent, and some of them were found to be temporary. The results of the study indicate that COVID-19-related economic supports can be considered a major driver of the surge in the Bitcoin market during the pandemic.

Abdilcelil Koç, Ali Çeli̇k

Çalışmanın amacı, 03.01.2020 ile 28.02.2022 dönemi için üretim araçlarındaki gelişmenin bir başka veçhesi olan dijitalleşme ile kripto paralara yönelimin hızlanmasının geleneksel borsalara alternatif olup olmayacağını simetrik ve asimetrik nedensellik test yöntemleriyle incelemektir. Bu çerçevede simetrik nedensellik analiz sonuçlarına göre, BTC ve ETH fiyatlarından SP500, NASDAQ ve DOWJ fiyatlarına doğru bir nedensellik ilişkisi saptanmış, aynı zamanda VIX’ten BTC ve ETH’ye doğru bir nedensellik ilişkisi bulunmuştur. Asimetrik nedensellik analizi sonuçlarına göre SP500, NASDAQ, DOWJ ve Altın fiyatlarındaki negatif değişmelerden, BTC fiyatlarındaki pozitif değişmelere doğru bir nedensellik ilişkisi tespit edilmişken, NASDAQ ve DOWJ fiyatlarındaki pozitif değişmelerden ETH fiyatlarının pozitif değişmelerine doğru bir nedensellik ilişkisinin varlığına ulaşılmıştır. Son olarak kripto paralar arasındaki nedensellik ilişkisi sınandığında BTC fiyatlarındaki negatif değişimlerden ETH fiyatlarındaki pozitif değişimlere, ETH fiyatlarındaki negatif değişimlerden BTC fiyatlarındaki pozitif değişimlere doğru bir nedensellik ilişkisi tespit edilmiştir.

Magsud GUBADLI, Vedat Sarıkovanlık

Bu çalışmada, kripto piyasasının önde gelen altı kripto para biriminin (Bitcoin, Stellar, Litecoin, Ethereum, Tether ve Ripple) volatil yapısı, asimetrik ilişki ve/ve ya kaldıraç etkisinin var olup olmadığı test edilmektedir. 09/11/2017-31/07/2022 dönemini kapsayan ve WinBUGS uygulaması ile yapılan bu çalışmada öncelikle logaritmik fark alınarak getiri serisi hesaplanmıştır. Bu kapsamda 100.000 tekrarla örneklem sınaması yapılmış olup katsayıların başlangıç eğiliminden çıkması için tahminlerin ilk 10.000 örneklemi dışlanarak kalan 90.000 örneklemle analiz gerçekleştirilmiştir. Asimetrik stokastik volatilite modeli tahmin sonuçlarına göre kripto para birimlerinin oynaklık kalıcılığı, oynaklığın öngörülebilirliği ve para birimlerinin kendi getirilerinin şoku ile oynaklıklarının etkisi arasındaki korelasyon düzeyi ilgili parametreler ile değerlendirilmiştir. Belirtilen zaman aralığında çalışmamızda kullanılan tüm kripto para birimleri için yoğun bir volatilite kümelenmesi olduğu gözlemlenmiştir. Bu volatilitenin sürekli olduğu ve düşük öngörülebilirliğin varlığı ampirik olarak asimetrik stokastik volatilite modeli ile elde edilen bulgular arasındadır. Ayrıca çalışmanın sonuçlarına göre Ethereum kripto para birimi dışındaki diğer beş para biriminin hiçbirinde ne kaldıraç etkisi ne de asimetrik ilişkisinin hiçbiri gözlemlenmemiştir.

İlayda İSABETLİ FİDAN, Tuğba Güz

In recent years, digital innovations especially emerged depend on Blockchain technology have caused a substantial transformation in the finance sector as in other sectors. Different financial assets have been revealed and began to be used as an investment tool along with this transformation in the markets. Cryptocurrencies that have a digital structure hold an important place among these assets. Dramatically increases in the daily transaction volume of currencies in the market have brought along different types of risks. These risks raised uncertainty on these currencies. Moreover, because cryptocurrencies are mostly used for the purpose of investment and speculation, it is important to understand the volatility movements and co-movements of cryptocurrencies and is substantially important, particularly because volatility can influence investment decisions. This study aims to determine the volatility transmission between cryptocurrencies to find useful answers about the volatility and the efficiency of markets. Daily logarithmic return series between 18 January 2018 – 14 February 2021 were used to analyze the volatility of five of the most common cryptocurrencies, namely Bitcoin (BTC), Ethereum (ETH), Litecoin (LTC), Ripple (XRP), IOTA by applying the RALS-ADF test, EGARCH, and DCC-GARCH models. We determined whether the market is efficient or not, and tested the existence of the asymmetric effect and volatility transmission in the market. According to our results, volatility shocks are not obtained persistent for only BTC. Furthermore, the presence of asymmetric effects and leverage effect valid for four cryptocurrencies. While asymmetric effects observed for BTC, no leverage effect has been observed during the period. We also analyzed nine pair-wise cryptocurrencies applying the DCC-GARCH model and we found that dynamic conditional correlation coefficients are statistically significant and positive for each pair.

Monica Alexiadou, Emmanouil Sofianos, Periklis Gogas, Théophilos Papadimitriou

In this study we investigate possible long-range trends in the cryptocurrency market. We employed the Hurst exponent in a sample covering the period from 1 January 2016 to 26 March 2021. We calculated the Hurst exponent in three non-overlapping consecutive windows and in the whole sample. Using these windows, we assessed the dynamic evolution in the structure and long-range trend behavior of the cryptocurrency market and evaluated possible changes in their behavior towards an efficient market. The innovation of this research is that we employ the Hurst exponent to identify the long-range properties, a tool that is seldomly used in analysis of this market. Furthermore, the use of both the R/S and the DFA analysis and the use of non-overlapping windows enhance our research’s novelty. Finally, we estimated the Hurst exponent for a wide sample of cryptocurrencies that covered more than 80% of the entire market for the last six years. The empirical results reveal that the returns follow a random walk making it difficult to accurately forecast them.

Leonardo H.S. Fernandes, Werner Kristjanpoller, Benjamin Miranda Tabak

This paper performs the asymmetric multifractal cross-correlation analysis to examine the COVID-19 effects on three relevant high-frequency fiat currencies, namely euro (EUR), yen (YEN) and the Great Britain pound (GBP), and two cryptocurrencies with the highest market capitalization and traded volume (Bitcoin and Ethereum) considering two periods (Pre-COVID-19 and during COVID-19). For both periods, we find that all pairs of these financial assets are characterized by overall persistent cross-correlation behavior [Formula: see text]. Moreover, COVID-19 promoted an increase in the multifractal spectrum’s width, which implies an increase in the complexity for all pairs considered here. We also studied the Generalized Cross-correlation Exponent, which allows us to verify that there is no asymmetric behavior between Bitcoin and fiat currencies and between Ethereum and fiat currencies. We conclude that investing simultaneously in major fiat currencies and leading cryptocurrencies can reduce the portfolio risk, leading to improvement in the investment results.

José Luís Miralles Quirós, María del Mar Miralles Quirós

Since the formulation of the Efficient Market Hypothesis, countless studies have been developed that try to either prove or refute it. Event studies, analysing the impact of different events on asset prices, are one of the most important research fields but there is a lack of evidence on cryptocurrencies. For that reason, we analyse the existence of over- and under- reaction effects on Bitcoin after hourly price shocks defined by filter sizes. We also do this using three alternative approaches. Our results show clear evidence of overreaction after negative shocks. We also observe that these overreactions tend to be greater as more hours pass after the event, with those that occur between 6 and 24 hours after the event being especially important. These results have important economic implications because they show that investors would be able to develop a profitable trading strategy simply by focusing on investing after negative shocks.