Yiming Zhao, Sultan Salem, Areej M. AL-Zaydi, Jin-Taek Seong · 6 authors
Among the different financial sectors, the modeling and forecasting of log-returns of cryptocurrency have received considerable attention. Numerous statistical models have been put forward to analyze the log returns of the cryptocurrency. However, as per our knowingness and immense literature search, we did not find published shreds of evidence about modeling cryptocurrency's log-returns while manipulating trigonometric-based statistical models. This paper provides a worthwhile endeavor to fill out this amusing research gap by manipulating a new trigonometric-based statistical methodology called the generalized sine-G family. Utilizing the generalized sine-G, a statistical model called the generalized sine-Logistic distribution is introduced. The generalized sine-Logistic distribution is applied for modeling the log-returns of two cryptocurrencies. Using certain decisive tools, it is observed that the generalized sine-Logistic is the best-suited distribution for modeling the given log-returns data sets. Additionally, this study uses various sophisticated and robust econometric techniques, such as the Least Absolute Shrinkage and Subset Selection, Markov Switching Generalized Autoregressive Conditional Heteroscedasticity (MSGARCH), and Step Indicator Saturation (SIS) model with different distributions, to predict (in-sample) the log-returns data sets. The effectiveness of each method is assessed through a popular loss function known as the root-mean-square error (RMSE).
The paper's recognition of the emerging phenomenon of cryptocurrencies. The rise of cryptocurrencies’ value on the market and the growing recognition around the arena open some demanding situations and concerns for business and commercial economics. The studies changed realized by way of the technique description, literature evaluation, and carried out research. This paper discusses the primary developments in the academic studies related to the Present Scenario of Cryptocurrency, a short overview of Cryptocurrency, cryptocurrencies through market capitalization, Cryptocurrencies Trending in Asia, Cryptocurrency in India, Cryptocurrency Exchanges, and cryptocurrency rules internationally. Keywords: Cryptocurrency, Bitcoin, Ethereum, Ripple, Virtual Currency, Blockchain *, Cyber Security, Blockchain Wallets, Distributed Ledger.
Abstract We analyze the connectedness between major cryptocurrencies and nonfungible tokens (NFTs) for different quantiles employing a time-varying parameter vector autoregression approach. We find that lower and upper quantile spillovers are higher than those at the median, meaning that connectedness augments at extremes. For normal, bearish, and bullish markets, Bitcoin Cash, Bitcoin, Ethereum, and Litecoin consistently remain net transmitters, while NFTs receive innovations. However, spillover topology at both extremes becomes simpler—from cryptocurrencies to NFTs. We find no markets useful for mitigating BTC risks, whereas BTC is capable of reducing the risk of other digital assets, which is a valuable insight for market players and investors.
Green finance is becoming more and more important as a way to fund environmentally friendly initiatives and lower carbon emissions. Green bonds have emerged as a significant financing tool in this context, and it is critical to understand how they interact with other components of the finance ecosystem, such as cryptocurrency and carbon markets, particularly during recent crises such as the COVID-19 outbreak and the Ukraine invasion. This study aims to empirically investigate the lead-lag associations between major cryptocurrency markets and green finance measured in terms of green bonds. For empirical estimation, the wavelet analysis and spectral Granger-causality test are employed to analyze the daily data, covering the period from 2018 to 2023. The results show that the correlation between the returns of the green bond market and cryptocurrencies is not stable over time, which rises from the short- to long-run horizon. However, the co-movements between these assets tend to be different and, in some cases, strong, especially during recent crises. Furthermore, the Granger causality test demonstrates the existence of a bi-directional causality between the prices of the cryptocurrencies and green bonds. These findings have significance for portfolio managers, investors, and researchers interested in investing strategies and portfolio allocation, suggesting that green markets may be used as a hedge and diversification tool for cryptocurrencies in the future.
Abstract This study examines the market efficiency in the prices and volumes of transactions of 41 cryptocurrencies. Specifically, the correlation dimension (CD), Lyapunov Exponent (LE), and approximate entropy (AE) were estimated before and during the COVID-19 pandemic. Then, we applied Student’s t -test and F -test to check whether the estimated nonlinear features differ across periods. The empirical results show that (i) the COVID-19 pandemic has not affected the means of CD, LE, and AE in prices, (ii) the variances of CD, LE, and AE estimated from prices are different across pre-pandemic and during pandemic periods, and specifically (iii) the variance of CD decreased during the pandemic; however, the variance of LE and the variance of AE increased during the pandemic period. Furthermore, the pandemic has not affected all three features estimated from the volume series. Our findings suggest that investing in cryptocurrencies is advantageous during a pandemic because their prices become more regular and stable, and the latter has not affected the volume of transactions.
This research aimed to investigate the relationship between climate policy uncertainty (CPU), clean energy (ENERGY), carbon emission allowance prices (CARBON), and Bitcoin returns (BTC) for the period from August 2012 to August 2022. The empirical analysis strategies utilized in this study included the Fourier Bootstrap ARDL long-term coefficient estimator, the Fourier Granger Causality, and the Fourier Toda–Yamamoto Causality methods. Following the confirmation of cointegration among the variables, we observed a positive relationship between BTC and CARBON, a positive relationship between BTC and CPU, and a negative relationship between BTC and ENERGY. In terms of causal associations, we identified one-way causality running from CARBON to BTC, BTC to CPU, and BTC to the ENERGY variable. The study underscores the potential benefits and revenue opportunities for investors seeking diversified investment strategies in light of climate change concerns. Furthermore, it suggests actionable strategies for policymakers, such as implementing carbon taxes and educational campaigns, to foster a transition towards clean energy sources within the cryptocurrency mining sector and thereby mitigate environmental impacts.
The aim of this study is to examine whether the assets known as safe-haven assets during crises fulfill these qualities for equity investors in Turkey during the Covid-19 pandemic. According to the results obtained under the assumption of GJR-GARCH (1,1) error terms, no asset has shown safe-haven characteristics against the stock market. However, when the BIST100 index depreciates by 5%, Ethereum, silver and Government Bonds show strong safe-haven characteristics, US dollar and Euro show weak safe-haven characteristics. When the BIST100 index depreciates by 2.5%, Bitcoin, gold and DJIMTR show weak safe haven asset characteristics. If BIST100 depreciates by 1%, gold and Government Bonds show strong safe-haven characteristics, and Bitcoin, Ethereum, Silver, the US dollar and Euro show weak safe-haven characteristics.
The aim of this paper is to examine the relationship between uncertainty indices (Geopolitical Uncertainty Index and Global Economic Policy Uncertainty Index) and cryptocurrencies. This study evaluated the behavior of cryptocurrencies with the evolution of uncertainties (GPU, EPU) on returns and volatility in terms of safe heaven as in traditional specualtive assets it increases their volaitility and reduces risk. For this purpose, this study examines the relationship between uncertanities indices, gold returns and crptocurrency by using the OLS regression for the monthly data from April 2017 to April 2022. The findings of this study indicate that the return and volatility of cryptocurrency increases. In particular, we note that the cryptocurrency market could serve as a weak hedge and safe against GEPU during a bull market; It could be considered a strong hedge, but in most cases could not serve as a safety against GPR. However, in case of Gold it is found that it serves as weak hedge against uncertainity indices and is not considered as safe heaven against GEPU and GPR. This study expands the current research on uncertainity indices and provides unique insight about the speculative nature of cryptocurrencies and safe heaven.
Abstract Cryptocurrencies are in the center of attention of investors, public authorities and researchers, but the interest has shifted from purely financial aspects regarding the way of trading, lack of regulation and supervision of transactions, volatility, correlation with other assets to aspects related to sustainability taking in account the high energy consumption generated by the mining process and the impact on environmental pollution. Bitcoin was chosen for the research considering the dominance that this financial asset has on the cryptocurrency market and its position as alpha currency.The article focuses on the relationship between Bitcoin transactions and energy consumption, for period 1st January 2019—31st of May 2022, this interval having significant price movements. The authors made a prediction of the Bitcoin price using a complex meta-model and SQL analytical functions. The analysis is based on 15 fundamental variables in order to forecast the price: Bitcoin data (prices and volume), electricity price and traded quantity on day-ahead market (DAM), gas price and traded quantity on DAM, inflation in EU, EU-ETS emissions certificates and oil prices. The study reveals the importance of the relationship Bitcoin—energy—carbon emissions, elements that capture the impact of the mining process on the environment from the perspective of energy consumption. Investors on the Bitcoin market must be aware not only of the importance of financial aspects on the price of cryptocurrencies (inflation, demand, offer), but also of other elements related to the evolution of energy prices (electricity, oil, gas, renewable energy) and the evolution of emissions certificates prices. Considering the promotion of the principles of sustainable development on the capital market, portfolio investors have become increasingly attentive to the social and environmental performance of financial assets. This study aims to make financial market players aware of the non-financial implications of their transactions. In addition, the energy transition and the reconfiguration of the energy mix are elements of impact on the cryptocurrency market through the technical levers involved in the mining process.
Purpose : The burgeoning cryptocurrency market offers an extremely profitable means of generating high-returning investments in a sector that is increasingly in demand. The goal of this research study was to examine the volatility of four cryptocurrencies, Bitcoin, Ethereum, Tether, and BNB, and determine how these fluctuations affected inflation for Indian investors. Methodology : The purpose of this research study was to examine the volatility of four cryptocurrencies, Bitcoin, Ethereum, Tether, and BNB, and to determine how these fluctuations affected inflation for Indian investors (“Cryptocurrency prices in India Today,†2023). To analyze volatility margins and determine trading volumes during such periods, data were retrieved using the MS Excel function, and Bollinger Bands were generated using the R console 4.4 open access program. Findings : To analyze volatility margins and determine trade volumes at such times, Bollinger Bands were constructed using R console 4.4 open access software and data for bitcoin transactions was gathered using an MS Excel function. While Tether and BNB showed only mild volatility, Bitcoin and Ethereum were shown to be quite volatile.Practical Implications : The findings of this research paper could prove to be very useful for academicians, investors (existent and prospective), and policymakers in the present and future markets. Originality : The goal of this research was to identify safer cryptocurrencies, which is a major problem rather than just one of utility. This is one of the very few research studies that focused on examining safer cryptocurrencies to buy in the upcoming years.
We develop a liquidity-sensitive multivariate volatility framework to improve the estimation of time-varying covariance structures under market frictions. We introduce two novel portfolio-level liquidity measures, liquidity jump and liquidity diffusion, which capture magnitude and volatility of liquidity fluctuation, respectively, and construct liquidity-adjusted return and volatility that reflect real-time liquidity variability. These liquidity-adjusted inputs are integrated into a VECM-DCC/ADCC-Bayesian model, allowing for conditional and posterior covariance estimation under liquidity stress. Applying this framework to portfolios of cryptocurrencies and US stocks, we find that traditional models misrepresent volatility and co-movement, while liquidity-adjusted models yield more stable and interpretable risk structures, particularly for portfolios of cryptocurrencies. The findings support the use of liquidity-adjusted multivariate models as statistically grounded tools for assessing the propagation of portfolio risk under market frictions, with implications for asset pricing, market microstructure design, and portfolio management.
The decentralised nature of cryptocurrency, coupled with its potential for significant financial returns, has elevated its status as a sought-after investment opportunity on a global scale. Nonetheless, the inherent unpredictability and volatility of the cryptocurrency market present considerable challenges for investors aiming to forecast price movements and secure profitable investments. In response to this challenge, the current investigation was conducted to assess the efficacy of three Machine Learning (ML) algorithms, namely, Gradient Boosting (GB), Random Forest (RF), and Bagging, in predicting the daily closing prices of six major cryptocurrencies, namely, Binance, Bitcoin, Ethereum, Solana, USD, and XRP. The study utilised historical price data spanning from January 1, 2015 to January 26, 2024 for Bitcoin, from January 1, 2018 to January 26, 2024 for Ethereum and XRP, from January 1, 2021 to January 26, 2024 for Solana, and from January 1, 2019 to January 26, 2024 for USD. A novel approach was adopted wherein the lagging prices of the cryptocurrencies were employed as features for prediction, as opposed to the conventional method of using opening, high, and low prices, which are not predictive in nature. The data set was divided into a training set (80%) and a testing set (20%) for the evaluation of the algorithms. The performance of these ML algorithms was systematically compared using a suite of metrics, including R2, adjusted R2, Mean Square Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). The findings revealed that the GB algorithm exhibited superior performance in predicting the prices of Bitcoin and Solana, whereas the RF algorithm demonstrated greater efficacy for Ethereum, USD, and XRP. This comparative analysis underscores the relative advantages of RF over GB and Bagging algorithms in the context of cryptocurrency price prediction. The outcomes of this study not only contribute to the existing body of knowledge on the application of ML algorithms in financial markets but also provide actionable insights for investors navigating the volatile cryptocurrency market.
As a matter of fact, stock market prediction remains a challenging and crucial aspect of investment decision-making. Contemporarily, cryptocurrencies are one of the hottest underlying assets on account of its high volatility. In this case, based on high accuracy prediction, it is available to achieve large extra return from the crypto markets. With this in mind, this study investigates the usage of machine learning algorithms, including LSTM, GRU, as well as bi-LSTM, for predicting cryptocurrency prices, focusing on Bitcoin (BTC), Ethereum (ETH), as well as Litecoin (LTC). According to the analysis, the study reveals that the GRU model consistently outperforms other algorithms, MAPE and RMSE values across all three cryptocurrencies. These findings underscore the reliability and efficiency of the GRU model in cryptocurrency price prediction. Furthermore, the research compares the model's performance with previous studies, reaffirming its effectiveness and potential for practical application in investment strategies as well as decision-making.
Ritesh Patel, Mariya Gubareva, Muhammad Zubair Chishti
Using the cross-quantile & wavelet quantile correlation methods, we investigate the connectedness between cryptocurrency environment attention index (ICEA) and green crypto, renewable energy crypto, and green conventional market. The interdependence of ICEA with the considered assets is weak, providing investors with new avenues for reducing systematic risk of their portfolios. The cross-quantile correlations intensity between ICEA and green conventional emerging markets is especially low. ICEA appears as a strong diversifier for the Cardano cryptocurrency and sustainability-conscious industries from the developed economies. The WQC indicates a low level of connectedness of the ICEA with the selected assets. The ICEA does not remain significantly connected with any of the asset. This study provides valuable implications for the investors, portfolio managers and policy markets for the portfolio diversification.
In the financial markets, international conflicts have a crucial influence. The ongoing conflict between Israel and Palestine is one of them which poses hazards to international politics and the economy. This study is the first study that examines the potential influence of the Israel-Palestine conflict on the cryptocurrency market. To this end, the event study methodology is used for the period 01.03.2023 – 17.10.2023, and the top ten cryptocurrencies are chosen for analysis based on their market capitalization. The results show that although the Israel-Palestine conflict affected certain cryptocurrencies (including BTC, TRX, SOL, and ETH), it had no statistically significant effect on the market as a whole. Furthermore, the majority of the effect was statistically positive, which may be an indication that the cryptocurrency market is considered a safe haven. Moreover, the abnormal returns were usually recorded in the days before the event, suggesting that the event had been anticipated by some cryptocurrencies. Investors and financial analysts may benefit from these results by considering the cryptocurrency market as an alternative investment tool in these uncertain times and using these findings to diversify their portfolios and create hedging strategies.
Using daily returns on large-cap altcoins, this paper uses power-law functions to model cryptocurrency-specific exposure to events exhibiting potentially large standard deviations. Since our analysis provides evidence for power-law behavior in the returns on cryptocurrencies, co-fractality analysis is employed to explore potential co-dependencies in the heavy-tailed part of return distributions. The findings indicate that the potential arrival of events exhibiting large standard deviations in Bitcoin returns can hardly be diversified using other sample altcoins. Other altcoins exhibit very similar features in terms of co-dependencies. Further results show that co-fractal behavior is not specific to any subsample.
Abstract In this paper, we study the return and volatility connectedness between cryptocurrencies and DeFi Tokens, considering the impact of different uncertainty indices on their connectivity. Initially, we estimate a TVP‐VAR model to obtain the total connectedness between the two markets. We find that returns on the cryptocurrencies transmit significantly larger shocks and, thus, are responsible for most variations in the majority of DeFis' returns. Then, to analyse the impact of uncertainty on total return and volatility connectedness, we use four factors, namely, Economic Policy Uncertainty (EPU), The Chicago Board Options Exchange Volatility Index (VIX), Infectious Disease Equity Market Volatility Tracker (ID‐EMV) and Geopolitical Risks (GPR). We find that except for geopolitical risks, all three measures have a positive impact on return and volatility connectedness, while GPR exerts a negative impact. Finally, we provide implications for researchers, market participants and policymakers.
This study examines the interconnectedness between central bank digital currencies (CBDC) index, digital assets and financial stability. First, we use the CBDC index as a measure of financial stability and examine its connectedness with other known measures of financial stability used in the literature. Secondly, we analyse the connectedness of CBDC index with digital assets such as cryptocurrencies and non-fungible tokens and various measures of financial stability. By analysing index returns of CBDC data and applying various connectedness measures to CBDC index, cryptocurrencies, stablecoins and NFTs, we gain insights into the relationships among these assets within a framework. The findings reveal a significant level of connectedness between CBDCs index, digital assets and financial stability. Our analysis shows a weak positive connectedness between CBDCs index and digital assets, indicating that movements in the CBDC index are not closely related to the performance of various digital assets and have a very small contribution to the changes in the returns of digital assets. Furthermore, the study finds bidirectional connectedness between CBDCs and other financial stability measures, suggesting that changes in CBDC performance can influence the overall stability of the financial system, and vice versa. This highlights the importance of carefully considering the design and implementation of CBDCs to ensure they support financial stability objectives.
Kripto para birimleri, 2009 yılında ortaya çıkmalarından bu yana oldukça popüler hale gelmiştir. Özellikle Bitcoin'in 3 Ocak 2009'da piyasaya sürülmesinden sonra, diğer kripto para birimlerinin piyasaya çıkışı hız kazanmıştır. Bu popülerlik artışının ardından, kripto para birimlerinin tahmini önemli bir konu haline gelmiştir. Bu çalışmanın ana amacı, Bitcoin (BTC), Ethereum (ETH) ve Binance (BNB) kripto para getirilerini öngörmek için geleneksel zaman serisi yöntemlerinden olan ARIMA-GARCH ile birlikte LSTM (Long Short-Term Memory) derin öğrenme yaklaşımını kullanarak elde edilen tahmin performanslarını karşılaştırmaktır. Bu çerçevede, çalışma literatüre yeni bir katkı sunmayı amaçlamaktadır. Her bir kripto para birimi için farklı zaman aralıklarında günlük veriler kullanılmış ve bu veriler %90 eğitim ve %10 test verisi olarak bölünmüştür. Çalışmada, yöntemler RMSE ve MSE değerlendirme kriterleri kullanılarak karşılaştırılmıştır. Genel olarak, BTC serisinde ARIMA-GARCH yöntemi eğitim verisinde daha iyi sonuçlar gösterirken, test verisi için LSTM yöntemi daha etkili olmuştur. BNB serisinde ise hem eğitim hem de test verisi için LSTM yöntemi daha üstün performans sergilemiştir. ETH serisinde ise her iki veri seti için ARIMA-GARCH yöntemi daha iyi sonuçlar ortaya koymaktadır. Bu çalışma, finansal veri tahmininde her iki yöntemin de önemli bir performans sergileyebildiğini vurgulamaktadır.
The research paper takes a deep dive into crypto currency market dynamics and regulation to bridge the gap of understanding these issues with implications for the economy. The arrival of crypto-currencies changes the terrain of finance completely by bringing in a new asset class with unprecedented level of volatility. Through this research paper, we will try to conduct a thorough investigation into the relationships within the crypto currency market, which will cover trend analysis, volatility patterns, and sketching the volatile regulatory status that accompanies this evolving environment. By adopting a holistic strategy including quantitative data analysis, qualitative research and regulatory watch, this paper will contribute to the knowledge about the mechanisms behind crypto currency markets and provide actionable strategies to the stakeholders enabling them to determine a clear course of action in this dynamic environment. The research is seeking to offer essential impressions about crypto currency ecosystem dynamics and regulatory pressure as a contribution to getting a bigger understanding of the sector which is emerging fast.
This paper explores whether the overall evolution of Bitcoin log-prices would manifest a log-period power-law singularity (LPPLS) signature, eventually resulting in the arrival of a finite-time singularity. Calibrating the LPPLS model using daily data on Bitcoin covering the 2011—2023 period, this study indeed finds evidence for a strong LPPLS signature suggesting the arrival of a spontaneous singularity in the year 2129. Further striking evidence suggests that Bitcoin will experience what we term a close-to-singularity-condition near to the year 2050—a remarkable coincidence with the recently documented arrival of a finite-time singularity in U.S. equities.
Bitcoin (BTC) represents an emerging asset class, offering investors an alternative avenue for diversification across various units of exchange. The recent global banking crisis of 9 March 2023 has provided an opportunity to reflect on how Bitcoin’s perception as a speculative asset may be evolving. This paper analyzes the volatility behavior of BTC in comparison to gold and the traditional financial market using GARCH models. Additionally, we have developed and incorporated a bank index within our volatility analysis framework, aiming to isolate the impact of financial crises while minimizing idiosyncratic risk. The aim of this work is to understand Bitcoin’s perception among investors and, more importantly, to determine whether BTC can be considered a new asset class. Our findings show that in terms of volatility and price, BTC and gold have responded in very similar ways. Counterintuitively, the financial market seems not to have experienced high volatility and significant price swings in response to the March 9th crisis. This suggests a consumer tendency to seek refuge in both Bitcoin and gold.
Bitcoin'in 2009 yılında ortaya çıkmasıyla birlikte, birçok sektör üzerindeki etkileri gözlemlenmiştir. Ancak, kripto para piyasalarındaki yüksek volatilite ve merkezi bir kontrol olmaması, kripto paraların geleceği konusunda belirsizlik yaratmaktadır. Bu anlamda, finansal sektörlerin dinamik yapısı gereği diğer sektörlerden daha hızlı etkilendikleri doğal olarak kabul edilmektedir. Bu araştırmanın temel amacı, Bitcoin, Ethereum, Litecoin ve Ripple gibi dört kripto para biriminin yatırım aracı olarak potansiyelini değerlendirmektir. Bu amaç doğrultusunda, 1 Ocak 2018 - 1 Ocak 2023 tarihleri arasında, seçili kripto para birimlerinin getiri oranlarının volatilite özellikleri modellenmeye çalışılmıştır. Otoregresif koşullu değişen varyans modelleri (Autoregressive conditional heteroskedasticity - ARCH) analizi kullanılarak yapılan çalışmada, modelin volatilite tahmininin anlamlı sonuçlar vermesi üzerine VAR analizi ve Granger nedensellik ilişkileri eklenerek desteklenmiştir. Bu testlerin sonucunda kripto para birimlerinin risk profili incelenmiş ve gelecekteki fiyat hareketlerine ilişkin bir tahmin sağlanması amaçlanmıştır. Bu şekilde, kripto para birimlerinin potansiyel bir yatırım aracı olarak değerlendirilmesi konusunda tespitler yapılarak literatüre katkıda bulunulmuştur. Bu bağlamda, serilerde ARCH etkisi gözlemlenmiştir. Yapılan VAR ve Granger Nedensellik testleri sonucunda, Bitcoin'deki bir değişikliğin diğer altcoin'leri önemli ölçüde etkilediği ancak Ripple'da anlamlı bir etkinin olmadığı sonucuna varılmıştır.
Cryptocurrency, a digital currency managed by decentralized networks, has gained immense popularity since the inception of Bitcoin. These digital assets, often characterized by extreme price volatility, have generated substantial interest from investors. Traditional financial models struggle to account for the unique dynamics and complexities of cryptocurrencies, prompting the adoption of deep learning techniques. This study investigates the use of Long Short-Term Memory (LSTM), Neural Networks, and Deep Learning (CNN) in predicting cryptocurrency prices. These deep learning models leverage various data sources, such as technical indicators and sentiment analysis, to gain a comprehensive understanding of cryptocurrency markets. The research evaluates the performance of these models using Root Mean Squared Error (RMSE) as the primary metric. The results demonstrate that the hybrid model, combining LSTM, Neural Networks, and Deep Learning, exhibits the highest predictive accuracy across multiple cryptocurrencies, including Bitcoin (BTC), Ethereum (ETH), and Binance Coin (BNB). However, challenges persist, such as model adaptability to unforeseen market events and data noise. Future developments may involve incorporating external factors and interdisciplinary collaboration to create more holistic valuation models. Despite these challenges, the study underscores the potential of hybrid deep learning models in enhancing cryptocurrency valuation accuracy and their relevance in risk management strategies for investors and traders.