Abstract This paper explores financial networks of cryptocurrency prices in both time and frequency domains. We complement the generalized forecast error variance decomposition method based on a large VAR model with network theory to analyze the dynamic network structure and the shock propagation mechanisms across a set of 40 cryptocurrency prices. Results show that the evolving network topology of spillovers in both time and frequency domains helps towards a more comprehensive understanding of the interactions among cryptocurrencies, and that overall spillovers in the cryptocurrency market have significantly increased in the aftermath of COVID-19. Our findings indicate that a significant portion of these spillovers dissipate in the short-run (1â5 days), highlighting the need to consider the frequency persistence of shocks in the network for effective risk management at different target horizons.
People are starting to see the cryptocurrency market as a viable source of income and investment, similar to the stock market, as the concept of cryptocurrencies continues to gain popularity. Predicting Bitcoin returns is related to financial machine learning, which uses time series to forecast price variance. This study starts with the daily close price of Bitcoin for its initial dataset. The price is transformed into percentages and binary classes, which categorize into âUpâ and âDownâ, after which a time series is applied to produce two datasets: a categorical dataset for classification and a numerical dataset for regression. For classification that represents a Binary classification in asset-price forecasting, k-fold cross-validation is applied to ensure that the best classifiers are selected for testing and analysis. Most of the regression analysis was based on visualization, which displayed the predicted prices by each regressor in front of the original values and helped analyze the modelsâ results more accurately. The outcomes of this study were achieved by anticipating bitcoin returns using classification and regression machine learning models, despite the approachesâ low accuracy and significant precision rate to the âUpâ class. At this stage, with a significant limitation regarding the dataset and a lack of other indicators, a model capable of predicting future variations is considered a beneficial addition for many trading tools or even for crypto market analysts.
Abstract Volatility of Bitcoin has a long memory, we modeled such a character using FIGARCH processes, afteward we went on for pricing Futures and Options, the price of Futures depends on many factors in the market, we have proposed a model for futures contracts which links their price to spot price and volatility, after calibrating our model the result was consistent with market values, the pandemic of Covid which started earlier in 2020 after hitting the district of Wuhan just before; had not really an effect on derivatives markets until july 2021 when the market started a downward trend. We price Options using a sample of volatilities that we consider determinstic for a matter of calculous. We finally compare our model for Futures to the same model but with a constant volatility. JEL Classification. G13
This study presents an analysis of the impact of asset price bubbles on the markets for cryptocurrencies and con-siders the standard risk management measure Value-at-Risk (“VaR”). We apply the theory of local martingales, present a styled model of asset price bubbles in continuous time and perform a simulation experiment featuring one- and two-dimensional Stochastic Differential Equation (“SDE”) systems for asset value through a Constant Elasticity of Variance (“CEV”) process that can detect bubble behavior. In an empirical analysis across several widely traded cryptocurrencies, we find that the estimated parameters of one-dimensional SDE systems do not show evidence of bubble behavior. However, if we estimate a two-dimensional system jointly with an equity market index, we do detect a bubble, and comparing bubble to non-bubble economies it is shown that asset price bubbles result in materially inflated VaR measures. The implication of this finding for portfolio and risk management is that rather than acting as a diversifying asset class, cryptocurrencies may not only be highly correlated with other assets but have anti-diversification properties that materially inflate the downside risks in portfolios combining these asset types. We also measure the model risk arising from mispecifying the process driving cryptocurrencies by ignoring the relationship to another representative risk asset through applying the principle of relative entropy, where we find that across all cryptocurrencies studied that the distributions of a distance measure between the simulated distributions of VaR are almost all highly skewed to the right and very heavy-tailed. We find that in the majority of cases that the model risk “multipliers” range in about two to five across cryptocurrencies, estimates which could be applied to establish a model risk reserve as part of an economic capital calculation for risk management of cryptocurrencies.
In May 2022, an apparent speculative attack, followed by market panic, led to the precipitous downfall of UST, one of the most popular stablecoins at that time. However, UST is not the only stablecoin to have been depegged in the past. Designing resilient and long-term stable coins, therefore, appears to present a hard challenge. To further scrutinize existing stablecoin designs and ultimately lead to more robust systems, we need to understand where volatility emerges. Our work provides a game-theoretical model aiming to help identify why stablecoins suffer from a depeg. This game-theoretical model reveals that stablecoins have different price equilibria depending on the coin's architecture and mechanism to minimize volatility. Moreover, our theory is supported by extensive empirical data, spanning $1$ year. To that end, we collect daily prices for 22 stablecoins and on-chain data from five blockchains including the Ethereum and the Terra blockchain.
While the majority of earlier studies used autocorrelation-based methodologies to explore the dependency structure for Bitcoin, this paper follows Benoit Mandelbrot in taking a fractal point of view. It shows that both Bitcoin and S&P 500 returns exhibit fractal-like behavior. Further evidence suggests that the infinite-variance-hypothesis cannot be rejected for both assets supporting Mandelbrotâs (1963) early study on cotton price changes. This result holds across non-overlapping subsamples. Following Mandelbrot (2008), Hurst exponents are estimated using rescaled/range analysis. The key findings are that (i) Bitcoin returns exhibit a higher level of persistence than S&P 500 returns across various subsamples, (ii) the level of persistence in Bitcoin returns has not changed across time, (iii) the S&P 500 moved from efficiency in the first subsample to inefficiency in the ex-post June 17, 2018 period, (iv) even if it was assumed that the variance of S&P 500 returns is finite, the kurtosis remains statistically undefined. The study concludes that correlation-based methods used to explore the S&P 500 universe result in misleading answers.
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.
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.
<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>
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.
Investors are looking for objects in which they invest their funds successfully, evaluating the effectiveness of alternative markets and their instruments. Historically, cash flow indicators most effectively reflected the mood of the masses in relation to any financial asset, both in the short-and long-term. This article examines in detail the queue of already completed, but not confirmed transactions in the bitcoin network. The mempool is able to timely display the growth in the number of transactions awaiting confirmation, which makes it a leading indicator of future cash flows that could affect the trading volumes and market prices of bitcoin. This study evaluates bitcoin mempool priorities and two different analyses have been conducted for this purpose. Firstly, the mempool periods are examined through a statistical analysis. Secondly, the performance determinants of mempool are assessed with q-ROF Multi-SWARA. In addition to q-ROF sets, weights are computed with IFS and PFS. Demonstrated here is that the results of all fuzzy sets are identical. This outcome explains the reliability of the findings and they indicate that a transaction is the most important determinant of the bitcoin mempool. It emerged that the adjusted mempool data (+16.7%) for 7-day and 30-day moving averages was able, with a time lag of 24â48 h, to indicate significant volatility of future bitcoin trading volumes (+1.6%) on average. The obtained values confirm the empirical conclusion reached here that the mempool growth leads to cash flow growth. An increase in future cash flows results in a substantial rise in future trading volumes. The key takeaway from the analysis is that mempool is able to effectively predict future increases in trading volumes based on the prior cash flow growth projected into mempool growth. However, as a price indicator, mempool does show mixed results with mostly uncertainty in the direction of price movement.
In this article, we discuss the central banksâ attitude to cryptocurrencies and focus more on European Central Bank. First, based on the analysis of scientific literature, we show that cryptocurrency is money and performs all of the functions of money, such as the exchange medium, value storage, and accounts unit. We found a positive correlation between the level of economic development of a country and the level of regulation and the integration of crypto cryptocurrencies into the economic system. We discuss not only the approach of the ECB to cryptocurrencies, but also how it developed. According to the study data, the ECB only began to respond to cryptocurrency as an equivalent monetary instrument in 2021, established regulatory mechanisms, and developed the digital euro project.
Abstract This study introduces a novel pairs trading strategy based on copulas for cointegrated pairs of cryptocurrencies. To identify the most suitable pairs and generate trading signals formulated from a reference asset for analyzing the mispricing index, the study employs linear and nonlinear cointegration tests, a correlation coefficient measure, and fits different copula families, respectively. The strategyâs performance is then evaluated by conducting back-testing for various triggers of opening positions, assessing its returns and risks. The findings indicate that the proposed method outperforms previously examined trading strategies of pairs based on cointegration or copulas in terms of profitability and risk-adjusted returns.
The concept of digital cash has the potential to completely change how people think about money. Digital currency has emerged as a possible alternative for exchanging currency and traditional payment systems, in addition to a popular investment option due to its potential for high returns. One of the three main varieties of digital currency is cryptocurrency that is secured by blockchain technology. Bitcoin, Ethereum, and many other cryptocurrencies exist in crypto markets. Investing in cryptocurrencies still carries risks and uncertainties due to the price volatility. It is thus important to approach such investments with caution and thoroughly research the market and its risks before making investment decisions. This paper presents an application of AI technology for learning the price movement of Ethereum (ETH) which is second only to Bitcoin in market capitalization. Based on the Technical factor, the XGBoost model is constructed for classification of return on Ethereum close price. The technical indicators such as moving averages and relative strength index, together with the Bitcoin price trend are chosen to determine influence on Ethereum price further used for computing the short-term return separate into 3 classes: downtrend, sideway, and uptrend. The model performance is measured by multiclass ROC-AUC, achieving the micro-average ROC-AUC of 0.66 saying the model is reasonably good at predicting the overall trend of ETH price.
Radhakrishna Dodmane, K. R. Raghunandan, Krishnaraj Rao N S, Bhavya Kallapu ¡ 7 authors
The advancements in communication speeds have enabled the centralized financial market to be faster and more complex than ever. The speed of the order execution has become exponentially faster when compared to the early days of electronic markets. Though the transaction speed has increased, the underlying architecture or models behind the markets have remained the same. These models come with their own disadvantages. The disadvantages are usually faced by non-institutional or small traders. The bigger players, such as financial institutions, have an advantage over smaller players because of factors such as information asymmetry and access to better infrastructure, which give them an advantage in terms of the speed of execution. This makes the centralized stock market an uneven playing field. This paper discusses the limitations of centralized financial markets, particularly the disadvantage faced by non-institutional or small traders due to information asymmetry and better infrastructure access by financial institutions. The authors propose the usage of blockchain technology and the data highway protocol to create a decentralized stock exchange that can potentially eliminate these disadvantages. The data highway protocol is used to generate new blocks with a flexible finality condition that allows for the consensus mechanism to configure security thresholds more freely. The proposed framework is compared with existing frameworks to confirm its effectiveness and identify areas that require improvement. The evaluation of the proposed approach showed that the improved highway protocol boosted the transaction rate compared to the other two mechanisms (PoS and PoW). Specifically, the transaction rate of the proposed model was found to be 2.2 times higher than that of PoS and 12 times higher than that of the PoW consensus model.