Blockchain Papers

Thiago Henrique Roza, Hermano Tavares, Félix Henrique Paim Kessler, Ives Cavalcante Passos

In general terms, financial investments can be understood as the acquisition of an asset, with the aim of generating

Foued Saâdaoui, Hana Rabbouch

Multifractal analysis is a forecasting technique used to study the scaling regularity properties of financial returns, to analyze the long-term memory and predictability of financial markets. In this paper, we propose a novel structural detrended multifractal fluctuation analysis (S-MF-DFA) to investigate the efficiency of the main cryptocurrencies. The new methodology generalizes the conventional approach by allowing it to proceed on the different fluctuation regimes previously determined using a change-points detection test. In this framework, the characterization of the various exogenous factors influencing the scaling behavior is performed on the basis of a single-factor model, thus creating a kind of self-explainable machine learning for price forecasting. The proposal is tested on the daily data of the three among the main cryptocurrencies in order to examine whether the digital market has experienced upheavals in recent years and whether this has in some ways led to a structured multifractal behavior. The sampled period ranges from April 2017 to December 2022. We especially detect common periods of local scaling for the three prices with a decreasing multifractality after 2018. Complementary tests on shuffled and surrogate data prove that the distribution, linear correlation, and nonlinear structure also explain at some level the structural multifractality. Finally, prediction experiments based on neural networks fed with multi-fractionally differentiated data show the interest of this new self-explained algorithm, thus giving decision-makers and investors the ability to use it for more accurate and interpretable forecasts.

Jacek Karasiński

This study employs robust martingale difference hypothesis tests to examine return predictability in a broad sample of the 40 most capitalized cryptocurrency markets in the context of the adaptive market hypothesis. The tests were applied to daily returns using the rolling window method in the research period from May 1, 2013 to September 30, 2022. The results of this study suggest that the returns of the majority of the examined cryptocurrencies were unpredictable most of the time. However, a great part of them also suffered some short periods of weak-form inefficiency. The results obtained validate the adaptive market hypothesis. Additionally, this study allowed the observation of some differences in return predictability between the examined cryptocurrencies. Also some historical trends in weak-form efficiency were identified. The results suggest that the predictability of cryptocurrency returns might have decreased in recent years also no significant relationship between market cap and predictability was observed.

Nurazlina Abdul Rashid, Mohd Tahir Ismail, Noor Wahida Md Junus

Predicting cryptocurrency prices are difficult due to dynamic data. At the same time, the hidden market behavior of trend and seasonal components in the history data is also critical as it provides an idea of what the price pattern will be in the future. Hence, this research proposes to identify and model the hidden pattern behavior in terms of component time series instead of removing it via the linear structural time series (STS) model approach. This study focuses on the top five cryptocurrencies relying on the highest market capitalization. From the results obtained, the top five cryptocurrencies have a different trend model, either deterministic or stochastic, which relies on the behavior of data. The five cryptocurrencies also show the crypto winter event, where the trend is downward after six months every year. The linear STS is the best model for predicting three cryptocurrencies’ prices for nonstationary and volatility data behavior. It can also handle the hidden component behavior and is easy to interpret. Since the linear STS model can indirectly retain the information of data, it will assist investors and traders in accurately predicting cryptocurrency prices.

Samuel W. Akingbade, Marian Gidea, Matteo Manzi, Vahid Nateghi

We present a heuristic argument for the propensity of Topological Data Analysis (TDA) to detect early warning signals of critical transitions in financial time series. Our argument is based on the Log-Periodic Power Law Singularity (LPPLS) model, which characterizes financial bubbles as super-exponential growth (or decay) of an asset price superimposed with oscillations increasing in frequency and decreasing in amplitude when approaching a critical transition (tipping point). We show that whenever the LPPLS model is fitting with the data, TDA generates early warning signals. As an application, we illustrate this approach on a sample of positive and negative bubbles in the Bitcoin historical price.

Joseph Wang, Chih‐Hung Lai

Recently, due to the ease of buying and selling cryptocurrencies and the continuous influence of social media, people have invested in the cryptocurrency market to obtain passive income. However, the volatility of the cryptocurrency market has caused many investors to lose their money. Although most people are aware of the high risks of cryptocurrencies along with the high rate of return, investing in cryptocurrencies has always been a topic of continuous discussion among researchers and investors. With the development of artificial intelligence (AI) and machine learning, machine learning has been applied to financial investment, and the research effect is remarkable recently. Thus, we propose a new self-adaptive trading system based on box theory and the K-means clustering algorithm. In the box theory, good buying or selling points occur when the oscillation box is broken and falls upward or below to enter the next box. This system predicted the upper and lower boundaries of the Oscillation Box through the K-means clustering algorithm and the sliding window method. Because of the sliding window method, prediction becomes more flexible and can be used in the market with the obtained upper and lower boundaries in a trading system. We also evaluated various market conditions (bull market, bear market, and fluctuant market) to construct the best K-means trading algorithm. After using Ethereum for backtesting, in the 4-month of July to November 2022), the transaction showed a 75 % winning rate, the final Return on Investment (ROI) of 33%, and a market gain of around 6%. This trading model is equipped with the ability of self-adjustment so that investors do not need to put effort on the market while maintaining a stable and considerable return on investment.

Jiacong Yuan

Cryptocurrencies have become a world-class phenomenon, with governments, companies and investors facing huge challenges and opportunities. Bitcoin is the one of most widely known cryptocurrencies. People prefer to use Bitcoin as an asset to invest for profit and hedge risk than for its payment function. This is the reason for the study of bitcoin market is so important. Many scholars had used daily data on bitcoin to construct different GARCH models to analyse the volatility of its returns. This research builds a GARCH model for the logarithmic return series of the bitcoin price to understand the volatility of the bitcoin price over the experimental time horizon. The experimental results show that the price of bitcoin is more volatile and vulnerable to external shocks. The analysis suggests that Bitcoin is more clearly a speculative financial instrument and that the price of Bitcoin is highly frothy.

Aliaksandr Kavaliou, Olga Peniaz

The article analyzes the correspondence of the emergence of cryptocurrencies to two important theoretical ideas of the Austrian school of economics - the regression theorem of L. von Mises and the concept of denationalization of money by F. Hayek. The analysis shows the consistency of the new economic phenomenon with the regression theorem, since the posses-sion of bitcoin as an asset confirms the presence of some value before being used as a medi-um of exchange. Cryptocurrency competition is similar to the ideas of F. Hayek, but takes place in the conditions of maintaining the state monopoly on emission. At the same time, the commodity security of stablecoins corresponds to the commodity security of private currencies.

Chekwube V. Madichie, Franklin N. Ngwu, Eze A. Eze, Olisaemeka D. Maduka

Cryptocurrencies have, over the years, gained an unprecedented prominence in financial discourse, with the market fielding over 5,300 digital currencies and reaching over $2 trillion in market capitalisation in 2022. The surge in market values of digital currencies and their popularity in the world of e-commerce have remained unabated and equally received special attention from researchers focusing on identifying the underlying factors that drive changes in their market values. Thus, this study models the dynamics of the prices of cryptocurrencies alongside their interconnectedness, focusing on Bitcoin, Ethereum, and Litecoin along the time and frequency dimensions of monthly data from 1 March 2016 to 05/31/2022. Based on the ARDL model, results show that the volume of transactions of Bitcoin, Ethereum, and Litecoin, oil prices, and gold prices exert a more significant positive influence on their prices in the longrun than in the shortrun. However, the publicity of the selected cryptocurrencies (google search rates) does not significantly influence their prices. Interestingly, results from the Wavelet Granger causality tests show no causality between the raw series of Bitcoin, Ethereum, and Litecoin prices. However, a bi-directional causality exists between Bitcoin and Ethereum prices during the longrun in their low frequencies, a unidirectional causality running from Bitcoin to Litecoin prices during the longrun in their low frequencies, and a unidirectional causality running from Litecoin to Ethereum prices during the shortrun, medium run and longrun in their high, medium, and low frequencies. These findings have profound implications for the global financial market and investor decisions.

Bahareh Amirshahi, Salim Lahmiri

The combination of Deep Learning and GARCH-type models has been proved to be superior to the single models in forecasting of volatility in various markets such as energy, main metals, and especially stock markets. To verify this hypothesis for cryptocurrencies market, we constructed various Deep Learning models based on Feed Forward Neural Networks (DFFNNs) and Long Short-Term Memory (LSTM) networks and evaluated their performance in forecasting the volatility of 27 cryptocurrencies. Then, different hybrid models were built in which the outputs of three GARCH-type models, namely GARCH, EGARCH, and APGARCH, with three different assumptions for the residuals’ distribution were fed into the DFFNN and LSTM networks. In other words, GARCH-type models were utilized as feature extractors and the deep learning models leveraged a sequence of extracted features as their inputs to produce the volatility of the next day. Our findings revealed that not only the deep learning models improve the forecasts of GARCH-type models with any distribution assumption, the forecasts of GARCH-type models as informative features can significantly increase the predictive power of the studied deep learning models; namely, the DFFNN and LSTM models.

Maria Chiara Pocelli, Manuel L. Esquível, Nadezhda P. Krasii

We present an analysis on variability Bitcoin characteristics that help to quantitatively differentiate Bitcoin from the state-owned traditional currencies and the asset Gold. We provide a detailed study on returns of exchange rates—against the Swiss Franc—of several traditional currencies together with Bitcoin and Gold; for that purpose, we define a distance between currencies by means of the spectral densities of the ARMA models of the returns of the exchange rates, and we present the computed matrix of the distances between the chosen currencies. A statistical analysis of these matrix distances is further proposed, which shows that the distance between Bitcoin and any other currency or Gold is not comparable to any of the distances between currencies or between currencies and Gold and not involving Bitcoin. This result shows that Bitcoin is essentially different from the traditional currencies and from Gold, at least in what concerns the structure of its variance and auto-covariances.

Bogdan Andrei Dumitrescu, Carmen Obreja, Ionel Leonida, Dănuț Georgian Mihai · 5 authors

This paper contributes to the literature dedicated to the interlinkages between cryptocurrencies and currencies by investigating whether Bitcoin price movements affect the exchange rates of a sample of nine European countries with non-euro currencies. By resorting to the novel unconditional quantile regression, we show that there is a statistically significant link between Bitcoin price movements and changes in nominal exchange rates. In normal market conditions, an increase in the price of Bitcoin can be associated with an appreciation of the currencies from our sample, while during the COVID-19 pandemic, the relationship inversed. In addition, we find heterogeneities in this relationship, depending on the level of change in the nominal exchange rate. The results emphasize the relevance of Bitcoin price movements to the conduct of monetary policy through the exchange rate channel and that investors in cryptocurrencies and various financial assets denominated in the currencies from our sample can benefit from diversification by including both types of assets in their portfolios.

Ruixue Jing, Luis E. C. Rocha

A cryptocurrency is a digital asset maintained by a decentralised system using cryptography. Investors in this emerging digital market are exploring the profitability potential of portfolios in place of single coins. Portfolios are particularly useful given that price forecasting in such a volatile market is challenging. The crypto market is a self-organised complex system where the complex inter-dependencies between the cryptocurrencies may be exploited to understand the market dynamics and build efficient portfolios. In this letter, we use network methods to identify highly decorrelated cryptocurrencies to create diversified portfolios using the Markowitz Portfolio Theory agnostic to future market behaviour. The performance of our network-based portfolios is optimal with 46 coins and superior to benchmarks up to an investment horizon of 14 days, reaching up to 1,066% average expected return within 1 day, with reasonable associated risks. We also show that popular cryptocurrencies are typically not included in the optimal portfolios. Past price correlations reduce risk and may improve the performance of crypto portfolios in comparison to methodologies based exclusively on price auto-correlations. Short-term crypto investments may be competitive to traditional high-risk investments such as the stock market or commodity market but call for caution given the high variability of prices.

Siva Kumar A, P. V. Gopirajan, Beulah Jackson

A virtual currency known as cryptocurrencies holds all business online. It’s virtual money that wouldn’t materialize like complicated conventional paper currency. Thus, this study emphasizes a distinction between distributed paper currency and cryptocurrencies, where these individuals may access information without outside interference. Because of its considerable market swings, such cryptocurrencies have an influence upon commerce as well as foreign diplomacy. Virtual currencies which are available in the market, such as Bitcoin (BTC), Ethereum (ETH), Terra (LUNA), Solana (SOL), Cardano (ADA), Tether (USDT), Binance coin (BNB), USD coin, XRP coin, Avalanche coin (AVAX) and Lite coin (LTC), etc. This study focussed on a detailed analysis of the literature about Machine Learning (ML) methods used for predictions. This proposed work also focused on implementing an efficient Machine Learning (ML)-based time series model for predicting BTC cryptocurrency prices. Long Short-Term Memory (LSTM) forecasting theory was established to accommodate the fluctuation of bitcoin prices and achieve great precision. The effectiveness of the LSTM in predicting the price of a cryptocurrency is demonstrated by this suggested study’s comparison between it and comparable time-series models.

Simeng Liu

Bitcoin, as a virtual cryptocurrency with both property of investment and currency, is widely investigated for its potential as a safe haven in world volatility. This paper, using classic time series model VAR and ARMA-GARCH, aims to study whether Bitcoin has safe-haven value in geopolitical events, which is based on the Russia-Ukraine conflict. By quantifying the impact of Russia-Ukraine conflict with crude oil prices and considered logarithmic yield, this study finds out both the positive and negative effects to Bitcoin yield from temporary shocks and long-term fluctuations of geopolitical. The VAR accumulation shows that geopolitics will have cumulative net positive impacts on Bitcoin's yield in the short term, that is, Bitcoin can be seen as a short-term safe haven for investors with brief profit needs. However, more results show that the impact of geopolitics on Bitcoin is difficult to determine, and the long-term impact is close to zero. The inadequate evidence of safe-haven value means that long-term investors need to consider Bitcoin cautiously. Based on the current background of Russia-Ukraine conflict, the study can both promote the academic understanding of Bitcoin’s value in geopolitical conflict, and help the investors make the right choice in world volatility.

G. V. Satya Sekhar

The price of cryptocurrency is always volatile and is influenced by various factors like market returns, prices of stocks, gold, and correlation of prices of cryptocurrency. Modeling and forecasting the prices of cryptocurrencies and measuring the volatility with the GARCH specification (Engle, 1982) has become standard among researchers. Several applications and extensions of GARCH model is proposed by Bollerslev (1986). Later, an integrated GARCH model (Engle & Bollerslev, 1986) states that the persistence parameter is equal to one. A combination of short and long memory conditional models for the mean and the volatility to analyze crypto returns is done with the help of ARFIMA (Autoregressive Fractionally Integrated Moving Average) and FIGARCH (Fractionally Integrated Generalized Autoregressive Conditionally Heteroskedastic) Model. This paper intended to understand various mathematical models for volatility of crypto currencies and also to find research gaps in the existing literature. A comprehensive overview is the need of the study.

Roberto Mota Navarro, F. Leyvraz, Hernán Larralde

The study of order volumes in financial markets has shown that these display several non-trivial statistical properties. Most studies have been focused on the bulk properties of volume of incoming orders or of realized transactions rather than the dynamical aspects. The present work is a study of the dynamical properties of volume. Unlike previous works, we studied the volume available at the spread rather than the volume of incoming orders or of realized transactions. We found evidence that suggests mean reverting volume changes and strong asymmetries in the equilibrium of sell and buy orders as well as the presence of clustering.

Susovon Jana, Krishna Dayal Pandey, Tarak Nath Sahu

The current study aims to explore the dynamic connectedness between stock and cryptocurrency markets and to determine the role of cryptocurrencies in the stock market as a hedge, diversifier, or safe haven. The study uses daily data of four stock indices and six cryptocurrencies, covering a period of January 2016 to December 2022. The analysis is conducted using the ADCC-GARC method with the wavelet coherency. The results indicate both stock and cryptocurrency markets exhibit long-run volatility persistence. The properties of Bitcoin, Ethereum, Binance Coin, Dogecoin, and Ripple vary between a range of hedges and diversifiers in different stock markets, which can change depending on market circumstances. However, only Tether has shown that it can act as a safe haven investment in all studied stock markets over time.

Tabito Kawakami

No abstract is available for this record.

Mohammad Ashraful Ferdous Chowdhury, Mohammad Abdullah, Masud Alam, Mohammad Zoynul Abedin · 5 authors

This paper examines the efficiency and asymmetric multifractal features of NFTs, DeFi, cryptocurrencies, and traditional assets using Asymmetric Multifractal Cross-Correlations Analysis covering the period from November 2017 to February 2022. Considering the full sample with a significant variation among asset classes, the study reveals DeFi-DigiByte is the most efficient while the cryptocurrency-Tether is the least efficient. However, S&P 500 showed high efficiency before COVID-19, and DeFi-Enjin Coin advanced as the most efficient asset during COVID-19. The volatility dynamics of NFTs, DeFi, and cryptocurrencies follow strong nonlinear cross-correlations, but evidence of weaker nonlinearity exists in traditional assets. Additionally, the sensitivity to smaller events in bull markets is high for NFTs and DeFi. The findings have significant implications for portfolio diversification when an investor's portfolio set includes traditional assets and cryptocurrency and relatively new blockchain-based assets like NFTs and DeFi.

Eray Gemi̇ci̇, Müslüm Polat, Remzi Gök, Muhammad Asif Khan · 6 authors

In this paper, we examine the effect of explosive behaviors in the Bitcoin market on the top 10 largest stock markets of developed and emerging countries. The daily dataset, including the Dow Jones Industrial Index (DJIA), Nasdaq (NSQ), Shanghai Composite Index (SSE), Nikkei 225 (N225), Hang Seng Index (HSI), Shenzhen Composite Index (SZSE), Euronext Amsterdam Index (AEX), London Stock Exchange (LSE), Toronto Stock Exchange (TSX), and Bombay Stock Exchange (BSE), spans July 21, 2010, to December 9, 2022. We first investigate the existence of explosive price behaviors using the bubble detection test of Phillips and Shi and the results provide evidence of multiple bubble episodes, coinciding with the monetary policy actions of the FED and ECB. Then, we address the question of whether the explosive behaviors detected affect the variance of equity returns by employing a GARCH model. The impact is negative, albeit its magnitude and significance vary among stock indices.

Diming Xu

Contemporarily, under the impacts of COVID-19 and regional conflicts with radicalness fiscal policy, the prices of cryptocurrency have been fluctuated dramatically. Among various types of cryptocurrency, Ethereum is one of the most volatility assets. In order to avoid risks as well as gain extra return in the crypto market, it is necessary to construct accurate prediction approach. In this paper, the Long Short-Term Memory algorithm will be used to predict the future price of Ethereum by learning Ethereum's past price direction data. price trend by learning Ethereum's past price trend data. Based on the analysis, the predicted values of the trained model fit well with the actual data, with the regression evaluation index R2 of 97.08% and MAPE of 6.89%. According to the results, it is feasible to predict the future price trend through the past price trend data. Nevertheless, it should be noted that the stochastic process in data training might lead to the instability of model performances. Hence, it is necessary to train the data several time to select the best models. Overall, these results shed light on guiding further exploration of cryptocurrency price forecasting in terms of the state-of-art neural networks.

Azza Béjaoui, Wajdi Frikha, Ahmed Jeribi, Aurelio F. Bariviera

This paper examines the dynamic connectedness between Gulf countries and BRICS stocks markets with a sample of cryptocurrencies, as well as two newly developed digital assets, namely NFT and DeFi, and Gold. The period under examination spans from January 2019 until September 2022. Our analysis is based on wavelet coherence, which is a suitable methodology considering the nonlinear dynamics present in data. Our empirical results clearly identify nontrivial time-varying connectedness between different assets and the stock markets. Asymmetric patterns in the interconnections of newly developed digital assets, cryptocurrencies, Gold and emerging market indices are well-documented, especially during the advent of the health and political events. Our empirical findings have relevant implications for portfolio managers, investors and researchers about portfolio allocation, investment strategies and potential diversification benefits of NFT and DeFi digital assets.

Bikramaditya Ghosh, Elie Bouri, Jung Bum Wee, Noshaba Zulfiqar

No abstract is available for this record.