Due to recent developments, several countries have given the green light to cryptocurrencies, and big companies now accept them as a payment method. This article aimed to assess the random walk behaviour of the cryptocurrency market by analysing Bitcoin returns. The study observed daily Bitcoin closing prices from January 2016 to December 2023. We employed rigorous statistical tests, including the run test, generalised spectral test, automatic portmanteau test and wild bootstrap automatic variance ratio test. Furthermore, the rolling window technique was used to discern whether market efficiency was time-varying or static, involving dividing the data into four fixed rolling windows. Our overall empirical results revealed that Bitcoin price fluctuations were unpredictable, inferring market efficiency. The findings implied that Bitcoin prices adhered to a random walk pattern, making it challenging to identify abnormal trends in this emerging market.
Japjeet Singh, Ruppa K. Thulasiram, A. Thavaneswaran, Alex Paseka
There have been several studies in the literature discussing the profitability with various trading strategies. Two common strategies are pairs trading and momentum strategies. The momentum strategy aims to exploit the phenomenon of momentum, where securities that have performed well in the past are likely to continue performing well in the future. The concept behind a pairs trading of stocks is similar to the statistical idea of cointegration. The goal of pairs trading is to profit from the relative price movements of the two assets, rather than from the absolute price movements of either asset. This strategy is generally implemented using algorithmic trading techniques, and it is often used by traders and investors to take advantage of mispricing in the market. In this study we first compare these two strategies and implement them to study for their profitability. We considered two major cryptocurrencies (Bitcoin and Ethereum) for these two trading strategies and show that with daily price data, dual momentum strategy generates significantly better results than the pairs trading strategy.
Cryptocurrencies have increasingly been traded against fiat currencies and as a result, governments globally have been trying to regulate these largely decentralized currencies. In this study, event study methodology has been used to evaluate the effect of regulatory announcements made by 25 countries. Based on cryptocurrency usage and returns of three major cryptocurrencies, namely Bitcoin, Ether, and XRP, this study finds that regulatory news results in significant abnormal returns for Bitcoin and Ether, but not for XRP. The authors find that irrespective of the type of news, abnormal returns are almost always negative. The countries have also been clustered based on their abnormal returns and it has been found that country characteristics such as income level, technological readiness and innovation potential affect the magnitude of abnormal returns. Thus, cryptocurrencies being global in essence, their regulatory oversight in countries do not exist in isolation, but they are also affected by the countries' development.
Weihao Han, David Newton, Emmanouil Platanakis, Charles Sutcliffe ¡ 5 authors
Abstract Cryptocurrency returns are highly nonnormal, casting doubt on the standard performance metrics. We apply almost stochastic dominance, which does not require any assumption about the return distribution or degree of risk aversion. From 29 longâshort cryptocurrency factor portfolios, we find eight that dominate our four benchmarks. Their returns cannot be fully explained by the threeâfactor coin model of Liu et al. So we develop a new threeâfactor model where momentum is replaced by a mispricing factor based on size and riskâadjusted momentum, which significantly improves pricing performance.
Purpose This paper examines and forecasts correlations between cryptocurrencies and major fiat currencies using Generalized Autoregressive Score (GAS) time-varying copulas. The authors examine to which extent the multivariate GAS method captures the volatility persistence and the nonlinear interaction effects between cryptocurrencies and major fiat currencies. Design/methodology/approach The authors model tail dependence between conventional currencies and Bitcoin utilizing a Glosten-Jagannathan-Runkle Generalized Autoregressive Conditional Heteroscedastic model (GJR-GARCH)-GAS copula specification, which allows detecting the leptokurtic feature and clustering effects of currency returns distribution. Findings The authors' results show evidence of multiple tail dependence regimes, implying the unsuitability of applying static models to entirely describe the extreme dependence between Bitcoin and fiat currencies. Compared to the most common constant copulas, the authors find that the multivariate GAS copulas better forecast the volatility and dependency between cryptocurrencies and foreign exchange markets. Furthermore, based on the value-at-risk (VaR) and expected shortfall (ES) analyses, the authors show that the multivariate GAS models produce accurate risk measures by adding cryptocurrencies to a portfolio of fiat currencies. Originality/value This paper has two main contributions to the existing literature on cryptocurrencies. First, the authors empirically examine the tail dependence structure between common conventional currencies and bitcoin using GJR-GARCH GAS copulas which consider the leptokurtic feature and clustering effects of currency returns distribution. Second, by modeling VaR and ES, the authors test the implication of using time-varying models on the performance of currency portfolios, including cryptocurrencies.
This paper presents an advanced econometric model specifically designed to analyze the intricate relationship between blockchain technology and various economic variables. The model serves as a robust framework for comprehending the impact of blockchain on investment patterns, adoption rates, and market trends. By quantifying these relationships, the model enables predictions regarding future trends in the blockchain industry and facilitates the identification of factors influencing growth or hindering adoption. With its wide-ranging applicability, the model offers profound insights for policymakers, investors, entrepreneurs, and researchers, shedding light on the economic implications of this rapidly evolving technology.The findings of this study reveal a multitude of significant insights regarding the economic implications of blockchain technology. The econometric model demonstrates a strong positive relationship between blockchain investment and adoption rates, indicating that increased investment leads to higher adoption levels. Moreover, the model identifies specific market trends and factors that influence the growth and adoption of blockchain technology. By highlighting these factors, stakeholders can make informed decisions and strategize accordingly.The econometric model forblockchain technology offers numerous applications and implications for various stakeholders. Policymakers can leverage the model's insights to develop regulatory frameworks that foster blockchain innovation while mitigating risks. Investors can utilize the model to make data-driven investment decisions and identify lucrative opportunities within the blockchain industry. Entrepreneurs can gain valuable insights into the factors driving adoption and tailor their business strategies accordingly. Additionally, researchers can expand their understanding of the relationship between technology and economic variables, contributing to the development of new theories and frameworks.
Cryptocurrencies have gained popularity and are increasingly used in the global financial system, despite their volatile nature. They have become an attractive financial instrument for individuals and corporations due to their potentials for high returns, decentralized nature, and exemption from strict government regulations. This study aims to investigate how cryptocurrency volatility affects the performance of companies listed on the Nigerian Exchange Limited (NGX). The study uses an ex post facto research design and the GARCH (1,1) model. Weekly data on Bitcoin and Ethereum were obtained from www.ng.investing.com and used to construct a cryptocurrency composite index with principal component analysis (PCA). The All-Share Index data were extracted from the Security and Exchange Commission (SEC) statistical bulletin between January 2017 and December 2021. The result of the mean equation shows that cryptocurrency trading in Nigeria responds more to positive sentiment and good news than bad news, while the variance equation reveals that current conditional volatility of cryptocurrencies and companies' performance is influenced by their previous shocks and past volatility conditions. The study also found evidence of volatility clustering in companiesâ performance on the NGX. Therefore, investors are advised to exercise caution in an expanding cryptocurrency market, while regulators and policymakers should use relevant indicators to avoid contagion risk that could spread to the stock market. This paper is significant and relevant to achieving the Nigerian government's plan to introduce an official virtual currency.
Dora Almeida, Andreia DionĂsio, Paulo Ferreira, Isabel Vieira
Extraordinary events, regardless of their financial or non-financial nature, are a great challenge for financial stability. This study examines the impact of one such occurrenceâthe COVID-19 pandemicâon cryptocurrency markets. A detrended cross-correlation analysis was performed to evaluate how the links between 16 cryptocurrencies were changed by this event. Cross-correlation coefficients that were calculated before and after the onset of the pandemic were compared, and the statistical significance of their variation was assessed. The analysis results show that the markets of the assessed cryptocurrencies became more integrated. There is also evidence to suggest that the pandemic crisis promoted contagion, mainly across short timescales (with a few exceptions of non-contagion across long timescales). We conclude that, in spite of the distinct characteristics of cryptocurrencies, those in our sample offered no protection against the financial turbulence provoked by the COVID-19 pandemic, and thus, our study provided yet another example of âcorrelations breakdownâ in times of crisis.
This study aimed to uncover the impact of COVID-19 on the leading cryptocurrency (Bitcoin) and on sustainable finance with specific attention to their potential long memory properties. In this article, the application of the selected methodologies is based on a fractal and entropy analysis of the econometric model in the financial market. To detect the regularity/irregularity property of a time series, approximate entropy is introduced to measure deterministic chaos. Using daily data for Bitcoin and sustainable finance, namely DJSW, Green Bond, Carbon, and Clean Energy, we examine long memory behaviour by employing a rescaled range statistic (R/S) methodology. The results of the research present that the returns of Bitcoin, the Dow Jones Sustainability World Index (DJSW), Green Bond, Carbon, and Clean Energy have a significant long memory. Contrastingly, an interdisciplinary approach, namely wavelet analysis, is also used to obtain complementary results. Wavelet analysis can provide warning information about turmoil phenomena and offer insights into co-movements in the timeâfrequency space. Our findings reveal that approximate entropy shows crisis (turmoil) conditions in the Bitcoin market, despite the nature of the pandemicâs origin. Crucially, compared to Bitcoin assets, sustainable financial assets may play a better safe haven role during a pandemic turmoil period. The policy implications of this study could improve trading strategies for the sake of portfolio managers and investors during crisis and non-crisis periods.
Abstract The purpose of the study is to examine higher moment connectedness among 12 cryptocurrencies using data sampled at the 1-minute high-frequency interval. We use methods that demonstrate the heterogeneity of agents from their distinct investing horizons. This includes wavelet multiple cross-correlations, CEEMDAN-based Diebold-Yilmaz (DY) connectedness index and the Barunik-Krehlik (BK) frequency connectedness index. First, our results show that higher moment multiple correlations among the sampled cryptocurrencies are higher at all time scales and the relationship strengthens at lower frequencies. Second, the wavelet cross-correlations show different cryptocurrencies with the potential to lead and lag in the transmission of higher moment shocks to the whole system at different frequencies. Again, the multiple wavelet cross-correlations increase with increasing time scales. The results from the CEEMDAN-based DY connectedness index as well as the BK framework also reveal cyclical connectedness and differences in connectedness across different frequencies. The results show more connectedness of higher moments than the connectedness empirically reported for returns and volatility. Cryptocurrency connectedness has mostly been examined using the first two moments. We extend this line of literature by examining the third and fourth moments, which might be more useful for risk management purposes.
Blockchain technology has enabled decentralized applications and peer-to-peer networks to be sustained by an incentive system based on tokens. At the same time, reputation is a paramount component for any limited-trust or no-trust system. However, in many systems, e.g., proof-of-stake-based systems, tokens are a proxy for reputation, therefore there might be confusion between wealth and reputation. In this paper, we focus on the social layer of dApps and present a two-token system that allows a clear-cut separation between wealth and reputation and fits the case of DAOs where user contribution is essential to maintain and sustain the platform, such as social apps where users are asked to vote about newcomersâ acceptance or to moderate and filter the content proposed by others. The paper delves into the implementation of such tokenomics, describes how the standard ERC-20 token can be adjusted to become a trustworthy reputation meter, and how the model can be optimized to reduce transaction fees. The value of the proposed work lies in moving away from a wealth-affected reputation in favor of a contribution-based one. Using such a system, dApps can reward users more fairly.
<p><big>In this study, we examined the efficiency of cryptocurrencies Bitcoin (BTC), Ethereum (ETH), Litecoin (LTC), Ripple (XRP), DASH, EOS, and MONERO from March 1, 2018, to March 1, 2023. We separated the sample into four subperiods for this purpose: a Tranquil period that includes the period from March 1, 2018, to December 31, 2019; a First Wave that includes the year 2020; a Second Wave that includes the year 2021; and a fourth subperiod that includes Russia&#39;s invasion of Ukraine in 2022-2023. The results are mixed, with some cryptocurrencies exhibiting equilibrium and others exhibiting autocorrelation and predictability in their pricing. When the sample is divided into subperiods, most digital currencies have long memories in their returns during the Tranquil period, BTC, LTC, and XRP exhibit efficiency during the First Wave of the pandemic, while BTC, ETH, and MONERO indicate efficiency during the Second Wave. Most assessed digital currencies showed equilibrium by 2022, with the exception of ETH and MONERO, which exhibit long memories, and LTC, which demonstrates anti-persistence. These results hold significance for investors in these alternative markets, as they suggest that some cryptocurrencies may be more predictable and therefore potentially profitable, whereas others may require greater caution and risk management strategies.</big></p>
Skander Slim, Ibrahim Tabche, Yosra Koubaa, Mohamed Osman ¡ 5 authors
Abstract Motivated by the relationship between trading intensity and volatility and the attractiveness of durationâbased volatility estimators, this paper investigates the ability of price duration to forecast realized volatility of Bitcoin. Using highâfrequency transaction data, trading intensity is measured by price duration and incorporated in the class of heterogeneous autoregressive (HAR) models. Results provide compelling evidence that trading intensity improves the forecasting performance of a highly competitive set of HAR models, commonly used in the literature. HAR extensions that incorporate price duration systematically deliver the lowest forecast errors and generate economically significant gains in volatility targeting exercise over multiple horizons. However, results show no evidence in favor of a unique durationâaugmented model. The predictive ability of price duration is supported by a number of robustness checks, including alternative estimation windows, bull and bear market states, and alternative thresholds that define price events.
The current study analyses five major cryptocurrencies and four global stock markets to explore the hedging, safe haven and diversification roles of cryptocurrencies by employing ADCC-GARCH and Wavelet Coherence Technique. The study has found that stock and cryptocurrency markets return have high volatility persistence in the long run and confirms the bi-directional volatility transmission. Also, the hedging capacity of digital currencies varies depending on market choice. Tether operates as the most effective diversifier for all studied stock indices and is a strong safe haven asset during market turmoil. It is also documented that majority of cryptocurrencies cannot offer diversification advantages.
Abstract We test the hedge property of nonâfungible token (NFT) coins against equity market fluctuations and compare it with the hedge property of Bitcoin. We employ daily the returns of Bitcoin; three NFT coins, namely Theta, Enjin Coin and Decentraland, and three equity market indices: S&P 500, NASDAQ and CAC 40, ranging from 18 January 2018 to 12 January 2021. We estimate the hedge effectiveness of the three NFT coins and Bitcoin against stock market fluctuations. Our results suggest that NFT coins are a better hedge against equity market fluctuations than Bitcoin.
Abstract This paper studies timeâfrequency connectedness among carbon assets, Bitcoin, and global stock markets by using the Diebold and Yilmaz method and the BarunĂk and KĹehlĂk method, to investigate the hedging ability of carbon assets and Bitcoin in global stock markets. Our study finds that both carbon assets and Bitcoin play hedging roles in global stock markets. However, their strength of hedging is negatively correlated with the degree of economic uncertainty and tends to change in different frequency domains. We also show that carbon assets and Bitcoin can act as each other's hedging assets in a great majority of cases. Our results provide useful knowledge for investors to reduce risks and for regulators to regulate carbon assets and cryptocurrency speculation.
Felix Aberu, Jimoh Sina Ogede, Joseph Oluwaseun Ewarawon
The movement of exchange rates generally had demonstrated unpredictable patterns over time as a traditional mode of payment, particularly the black or parallel exchange market that is already fueled by demand pressure. However, cryptocurrency and the parallel or black exchange market are global phenomenon traded outside the government strict regulations in Nigeria, hence, their economic implications and understanding by many persons, banks, policy-makers, governments, and companies remain a priori unclear. Therefore, this study investigates the impacts of cryptocurrency on black or parallel exchange rate market movement in Nigeria from Jan, 2021 to April, 2023 using the autoregressive distributive lag (ARDL) regression analysis and Granger causality test to affirm the hypothesis that, cryptocurrency do not have significant impacts on black market exchange rate movement in Nigeria. The result of the ARDL shows that cryptocurrencies trading are core determinants of the black or parallel market exchange rate movement in Nigeria during the study period. Therefore, we concluded that cryptocurrency has a negative and significant impact on black market exchange rate movement in Nigeria from Jan. 2021 to April, 2023 and recommend that the government as a matter of urgency regulate cryptocurrency in order to curb the excesses that come with it just like in Japan, China, and Australia, since Nigeria still engages foreign currency controls as monetary policy tools, so as to improve on consumersâ confidence on the domestic currency.
Design/Methodology/Approach: This chapter applies tests associated with the adaptive market hypothesis (AMH) and Johansen cointegration test. AMH acknowledges the views of the efficient market hypothesis and behavioural finance approach.Purpose: Cryptocurrencies are considered a new asset class by multiasset portfolio managers. Hence, we examine the AMH and cointegration in the cryptocurrency market to know whether select cryptocurrencies can be diversified.Findings: We find that cryptocurrencies are efficient and there is a long-run relationship among constituent series, and there is no short-run causality derived from bitcoin, Ethereum and litecoin to bitcoin, while stellar and Dogecoin have short-run causality to bitcoin.Originality/Value: This chapter is different from the existing one as this is the first study in which the AMH and Johansen cointegration test are applied to check the efficiency and relationship of Bitcoin, Ethereum, and Monero, Stellar, litecoin and Dogecoin.