The increasing interaction between the equity market and cryptocurrencies has raised concerns about volatility spillovers; however, empirical evidence about sectoral-specific spillover effects in emerging markets is scarce and hard to find. Existing research mainly concentrates on developed markets and aggregate equity indices, leaving a research gap in comprehending how sectoral indices variations impact market interactions in developing financial markets like Thailand. This article investigates the mean and volatility spillover effects between the Thai stock market and leading cryptocurrencies from April 2019 to April 2024. Applying bivariate VAR (1)-BEKK-GARCH (1,1) with an asymmetry model, this study examines the aggregate and sectoral-specific mean and volatility spillovers across major Thai stock market sectors. The findings reveal the significant mean spillover effect from cryptocurrencies to the Thai stock market with sectoral variation, while sectors such as industrials and financials exerted significant linkages, and the agricultural and food sector remains unaffected. Additionally, volatility spillovers were predominantly transmitted from the Thai equity market to cryptocurrency. Moreover, asymmetry effects were observed, with the asymmetry effects mainly transmitted from the Thai equity market to cryptocurrency. These findings provide critical insights for both individual and institutional investors on risk management and portfolio diversification while also helping policymakers with guidance on regulatory measures to mitigate systemic risks in emerging financial markets.
This paper provides the first empirical evidence of whether the introduction of US spot Bitcoin ETFs affected the returns and volatility of major cryptocurrencies. Using data from December 18, 2017 to March 15, 2024, we apply an event-study methodology within a GARCH-based framework. Our results reveal a significant effect of the introduction of spot Bitcoin ETFs on cryptocurrency returns and volatility. The analysis shows a positive impact for Bitcoin, Ethereum, and Litecoin spot price returns around the event date. The volatility of Bitcoin and Ripple spot markets decreased following the introduction of spot Bitcoin ETFs, which supports the stabilization hypothesis for these two cases. We also examine the volatility spillovers using a wavelet coherence approach, and reveal significant volatility spillovers from Grayscale Bitcoin ETF to Bitcoin futures and to a lesser extend to the Bitcoin spot market. Our findings enhance the limited understanding of the price discovery and functioning of the cryptocurrency markets, which could be useful for investors, regulators, and policymakers. • Study the impact of introduction of Spot Bitcoin ETFs on the cryptocurrency market. • Apply event study methodology within a GARCH framework. • Find a positive impact for Bitcoin, Ethereum, and Litecoin spot price returns. • Volatility of Bitcoin and Ripple decreased, supporting the stabilization hypothesis. • Wavelet coherence analysis reveals volatility spillovers from Bitcoin ETF to Bitcoin futures.
As the cryptocurrency market rapidly evolves and solidifies its significance within the global financial system, understanding the dynamic characteristics of returns has become increasingly critical. This study examines nine major cryptocurrencies by market capitalization from 1 January 2017, to 22 November 2023, employing the Mann-Kendall breakpoint test, Pettitt test, wavelet analysis, and TVP-VAR model to uncover the trends, breaks, and cross-category correlations of returns. The findings reveal that most cryptocurrencies exhibit no significant return trends. However, the smart contract platform category experienced a pivotal shift in the latter half of 2019, followed by a substantial improvement in market performance. Additionally, smart contract platforms and alternative currencies exhibit similar return patterns, whereas stablecoins remain relatively independent. Nevertheless, during exceptional periods, the trends of all three asset classes tend to converge. Before breakpoints, cryptocurrency correlations generally intensify, while post-breakpoint correlations gradually weaken. During special market events, smart contract platforms exert a pronounced influence, whereas stablecoins maintain stability. This study bridges the gap in long-term return dynamics and cross-category interactions, providing investors with profound insights into market mechanisms and offering policymakers crucial guidance for regulatory and risk management strategies.
Diego Mazzitelli, Elia Fiorenza, Inès Belgacem, Carmelo Arena
Objective of this manuscript is both to tracing the evolution of money, and examining its transition from commodity money to fiat money, up to the emergence of cryptocurrencies. It highlights the inherent issues of the barter system, emphasizing the urgencies and necessities that favored the adoption of legal tender. Subsequently, the impact of the creation of the Euro on the European economy—both historically and geopolitically—will be analyzed, contextualizing the European Union's institutional process. In a response to the crisis, Bitcoin (the first decentralized cryptocurrency) will be introduced, along with an illustration of the supporting Blockchain technology will be provided. Finally, the proposal of American Senator Lummis, who suggests a massive purchase of Bitcoin to be used as a strategic reserve through the “Bitcoin Act” program, will be explored, prompting several reflections on the future of the petrodollar as a reserve instrument. Through these reflections, the reader could develop their own thoughts on the importance of evolving towards forms of money more suited to an increasingly digitized and decentralized economy. In conclusion, by proposing an analogy between the ancient monetary practices on Yap and cryptocurrencies, we aim to stimulate the reflection that innovation is not only desirable in this fast-paced world but essential.
Mustafa Raza Rabbani, Sabia Tabassum, Miklesh Prasad Yadav, Umar Nawaz Kayani
Purpose This study aims to explore and analyze time-varying linkages of Islamic equity indices with the Sukuk (Islamic bond) market index, Halal cryptocurrency and Shariah-compliant exchange traded fund (ETF). Design/methodology/approach To study the dynamic connectedness of constituent series, the daily adjusted closing price from December 31, 2019, to September 11, 2023, has been used. This period is taken considering the unprecedented COVID-19 and Russia–Ukraine tussle under study. Standard GARCH and E-GARCH models are used to forecast symmetrical and asymmetrical volatility. Further, dynamic conditional correlation (DCC)-GARCH and Diebold and Yilmaz (2012) connectedness models demystify the spillover among examined assets. Findings There is no spillover from Dow Jones Islamic Market (DJIM) to Taddawul Sukuk and Bond Market Index (TSBI) and X8X in the short run, while there is spillover in the long run. However, DJIM and HLALETF exhibit volatility spillover in long run and short run as well. Further, Diebold and Yilmaz (2012) reveals that DJIM is the highest receiver, and TSBI is the most diminutive receiver of the shock. On the other hand, HLAL-ETF is the highest transmitter, and TSBI stands out as the lowest transmitter to the network connection. Originality/value Following an extant literature review, and to their understanding, it’s a novel study that unfolds dynamic linkages among selected markets during two major global crises.
Cryptocurrencies have attracted significant attention due to their high risk, extreme volatility, regulatory controversies, and scandals. Investors and policymakers are drawn to them for their potential to enhance diversification and deliver high returns. This study examines the impact of incorporating cryptocurrencies into investment portfolios, focusing on their ability to improve risk-adjusted returns and diversification. A rolling asset allocation strategy employing the maximum Sharpe Ratio within a Markowitz framework was applied to weekly data from 2018 to April 2024. The analysis compares two unconstrained portfolios and two constrained portfolios, which impose a concentration limit on cryptocurrency investments. Results reveal that in 70% of the rolling periods examined, portfolios with cryptocurrency allocations outperformed non-cryptocurrency portfolios in terms of Sharpe Ratios. However, the heightened volatility of cryptocurrencies significantly increased portfolio risk, with annualized weekly standard deviations ranging from 18% to 25%, compared to 12% to 15% for portfolios without cryptocurrency exposure. These findings illustrate the dual nature of cryptocurrencies: they can act as both a source of instability and an opportunity for diversification. The study underscores the necessity of a cautious and strategic approach to incorporating cryptocurrencies into investment plans, given their inherent risks and unpredictable behavior.
Kamel Touhami, Ilyes Abidi, Mariem Nsaibi, Maissa Mejri
This study investigates the impact of environmental variables, such as carbon emissions and temperature anomalies, on cryptocurrency returns. While existing research has primarily focused on economic and financial determinants, the influence of environmental factors remains underexplored. Using Dynamic Conditional Correlation GARCH (DCC-GARCH) and Time-Varying Coefficients Vector Autoregression (TVC-VAR) models, this study provides empirical evidence that environmental variables significantly affect the volatility and returns of Bitcoin, Ethereum, and Tether. The results show that Bitcoin and Ethereum are highly sensitive to CO2 emissions and temperature fluctuations, while Tether demonstrates a more moderate response. Moreover, the impact of these environmental factors evolves over time, underscoring their dynamic nature in cryptocurrency valuation. These findings highlight the importance of incorporating environmental variables into forecasting models to enhance risk management and investment strategies. This study contributes to the literature by bridging the gap between environmental concerns and cryptocurrency market behavior, offering valuable insights for investors, regulators, and policymakers.
Kripto paralar 21. yüzyılın ilk çeyreğine damgasını vuran finansal varlıklardır. Finansal piyasalarda işlem görmeye başlamalarının ardından kısa süre içerisinde işlem hacimlerinin artması ile çok sayıda yeni kripto para birimi üretilerek piyasada işlem görmeye başlamıştır. Kripto paraların üretim süreçleri, fiziksel varlığa sahip olmamaları, merkeziyetsiz yapıları gibi geleneksel finansal varlıklardan ayrılan özellikleri dikkat çekmiştir. Dikkat çeken bir diğer önemli özellikleri ise şüphesiz kripto para birimlerinde yaşanan ciddi fiyat dalgalanmaları olmuştur. Kripto para birimlerinin yaşamış oldukları bu fiyat dalgalanmaları piyasanın volatil yapısını ön plana çıkarmıştır. Bu nedenle kripto varlıklar arasındaki volatilite yayılımın analiz edilmesi gerek yatırımcılar gerekse araştırmacılar açısından önem kazanmıştır. Bu çalışmada kripto para piyasasında en yüksek piyasa değerine sahip 4 kripto para birimi arasındaki volatilite yayılımı analiz edilmiştir. Analizlerde BTC (Bitcoin), ETH (Ethereum), BNB (Binance Coin) ve SOL (Solano) için 13.07.2020 ile 05.09.2024 tarihleri arasına ait günlük getiriler kullanılmış ve volatilite yayılımının analizi için TVP-VAR modeli oluşturularak kripto para birimleri arasındaki dinamik bağlantı incelenmiştir. Analiz bulgularından, kripto para birimlerinin volatilitelerindeki toplam dinamik bağlantının Covid-19 Pandemisi ve Bitcoin ETF’lerinin onaylanmasına ilişkin gelişmelerden etkilendiği ve bu dönemlerde artış gösterdiği tespit edilmiştir. Ayrıca, kripto para birimleri arasındaki toplam volatilite yayılımının gücünün yüksek olmadığı, kripto para birimlerinden BNB ve BTC’nin analiz dönemi içerisinde volatilite yayıcısı, ETH ve SOL’un ise volatilite alıcısı özellik gösterdiği bulgusu elde edilmiştir. Kripto para birimleri arasında volatilite yayıcısı olan değişkenler etki güçleri açısından sıralandığında en güçlü volatilite yayıcısı olan para biriminin BNB olduğu ve bunu BTC’nin takip ettiği belirlenmiştir. Diğer yandan SOL, volatilite alıcısı olan kripto para birimleri arasında volatiliteyi en çok alan kripto para birimi olurken, ETH ise ikinci sıradadır. Kripto para birimlerinin volatilitelerindeki değişimin açıklanmasında öncelikle ilgili kripto para biriminin kendi geçmiş fiyat şoklarının etkili olduğu belirlenmiştir. Analizlerde dikkat çeken bir diğer husus ise özellikle BNB ve BTC’nin SOL’a güçlü şekilde volatilite yaymasıdır. Analize dahil edilen 4 kripto para biriminin volatilite yayılım ilişkisinin çok yüksek olmaması, aynı portföyde bulundurulabilecekleri ve birbirlerine risk bulaştırıcı etkilerinin sınırlı olabileceği şeklinde değerlendirilebilir. Bunun yanı sıra BNB’nin en yüksek volatilite yayıcısı olma özelliği dikkate alınarak portföylerin oluşturulması ve takip edilmesi, yatırım verimliliği açısından önem taşıyacaktır. Benzer şekilde SOL’un da diğer kripto para birimlerinden güçlü şekilde volatilite alması, yatırım süreçlerinde dikkat edilmesi gereken bir diğer husus olarak değerlendirilebilir.
We investigate the long-range cross-correlation and cross-multifractality between the “dirty” and “clean” cryptocurrencies and the major financial assets: the Dow Jones Index (DJI), the Euro–Dollar exchange rate (EURUSD), and Gold. The analysis shows a high long-range correlation between most pairs with some exceptions, including the DJI–Ripple and Gold–Polygon. When the DJI is paired with clean cryptocurrencies such as Polygon and Cardano, they exhibit multifractal properties. As for the EURUSD–BTC and Gold–BTC, these two pairs demonstrated the highest level of multifractality in their corresponding pairs. All pairs of cryptocurrencies and main financial indices are persistent, with the exceptions of EURUSD–POLYGON (H = 0 . 4970 ± 0 . 0048 for q =2), GOLD–BTC (H = 0 . 5039 ± 0 . 0058 for q =2) and GOLD–LTC (H = 0 . 5044 ± 0 . 0057 for q =2) that are Brownian, and GOLD–POLYGON (H = 0 . 4917 ± 0 . 0055 for q =2) which is anti-persistent. For q =5, all are anti-persistent, except DJI-Eth, XRP, and ADA are Brownian, and EURUSD-XRP is persistent. We also assessed the asymmetric persistence behavior when the market is upward or downward and found that for the pairs involving dirty cryptocurrencies with DJI and EURUSD, there is a higher level of persistence during the downward market. On the other hand, Gold-related pairs were almost symmetric. Thus, we identified the complexity and variability of the cryptocurrency pairs with the traditional financial instruments, which shows their various reactions to the changes in the market and types of assets.
Riadh Benammar, Anas Elmelki, Nadia Arfaoui, Adel Boubaker
ABSTRACT This paper investigates how the geopolitical risk (GPRD), economic policy uncertainty (EPU) index, and Twitter economic uncertainty (TEU) related to the Russo‐Ukrainian conflict can affect cryptocurrency returns (Bitcoin [BTC], Ethereum [ETH], Ripple [XRP], Dogecoin [DOGE], Litecoin [LTC], Cardano [ADA], BNB, and TRON [TRX]) over the period ranging from January 1, 2020, to April 24, 2023. Using the Spectral Breitung Candelon causality and wavelet coherence methods, interesting findings are reported. This study reports noteworthy findings. First, we observe that during the armed battle, ADA, BNB, DOGE, LTC, TRX, and XRP appear as hedges against GPRD. However, we found a negative impact on BTC and ETH. Second, the results show that EPU and TEU have no effect on cryptocurrency, respectively. These findings provide a comprehensive overview of cryptocurrency fluctuations during the ongoing conflicts in Ukraine. Finally, findings show that only ADA, BNB, DOGE, LTC, TRX, and XRP could be used as hedging tools during times of uncertainty. These results have practical implications for cryptocurrency investors and elements influencing its returns, especially during uncertain times.
Ever since the emergence of cryptocurrencies, scholars have grappled with the question of whether they are forms of money or not. The most interesting problem, however, is not if these instruments are already money, but whether they could become money. One crucial aspect in this regard is the potential (or lack thereof) of a privately-issued cryptocurrency to become the monetary unit of account. Drawing on Marx’s theory of money and making the hypothesis that cryptocurrencies are digital commodities, the article argues that cryptocurrencies create a unit of account (BTC) to describe a novel monetary instrument (a Bitcoin coin) aspiring to become a new form of world money. So far, they have not been widely used to denominate prices, incomes, or credits/debts except in certain, still limited but growing, areas of the on-chain digital world. Things could change if the use of cryptocurrencies spills over to the off-chain (digital and non-digital) world. Nevertheless, the adoption of cryptocurrencies as units of account would face several challenges in international and national circulation, crucially among them, the action of states to remain in control of the monetary unit.
Cryptocurrencies have become a significant asset class, attracting considerable attention from investors and researchers due to their potential for high returns despite inherent price volatility. Traditional forecasting methods often fail to accurately predict price movements as they do not account for the non-linear and non-stationary nature of cryptocurrency data. In response to these challenges, this study introduces the Helformer model, a novel deep learning approach that integrates Holt-Winters exponential smoothing with Transformer-based deep learning architecture. This integration allows for a robust decomposition of time series data into level, trend, and seasonality components, enhancing the model’s ability to capture complex patterns in cryptocurrency markets. To optimize the model’s performance, Bayesian hyperparameter tuning via Optuna, including a pruner callback, was utilized to efficiently find optimal model parameters while reducing training time by early termination of suboptimal training runs. Empirical results from testing the Helformer model against other advanced deep learning models across various cryptocurrencies demonstrate its superior predictive accuracy and robustness. The model not only achieves lower prediction errors but also shows remarkable generalization capabilities across different types of cryptocurrencies. Additionally, the practical applicability of the Helformer model is validated through a trading strategy that significantly outperforms traditional strategies, confirming its potential to provide actionable insights for traders and financial analysts. The findings of this study are particularly beneficial for investors, policymakers, and researchers, offering a reliable tool for navigating the complexities of cryptocurrency markets and making informed decisions.
Eleni Koutrouli, Polychronis Manousopoulos, John Theal, Laura Tresso
As crypto assets become more widely adopted, crypto asset markets and traditional financial markets may become increasingly interconnected. The close linkages between these markets have potentially important implications for price formation, contagion, risk management and regulatory frameworks. In this study, we assess the correlation between traditional financial markets and selected crypto assets, study factors that may impact the price of crypto assets and identify potentially significant events that may have an impact on Bitcoin and Ethereum price dynamics. For the latter analyses, we adopt a Bayesian model averaging approach to identify change points in the Bitcoin and Ethereum daily price time series. We then use the dates and probabilities of these change points to link them to specific events, finding that nearly all of the change points can be associated with known historical crypto asset-related events. The events can be classified into broader geopolitical developments, regulatory announcements and idiosyncratic events specific to either Bitcoin or Ethereum.
This study investigates return spillovers among the 15 most capitalized cryptocurrencies during the Russia-Ukraine war and the COVID-19 pandemic. Data were extracted from the Coin Market Cap database to ensure a comprehensive analysis of market behavior, covering a daily series from January 2020 to December 2023. The research employs three autoregressive techniques (TVP-VAR, LASSO VAR, and Ridge VAR) to verify the robustness of findings regarding market fragility influenced by non-economic shocks. The study identifies extensive return spillovers primarily driven by Bitcoin and Ethereum, with considerable influences from Cardano, Litecoin, and Polkadot. The results show Ethereum as a primary spillover transmitter in the cryptocurrency market, taking that position formerly held by Bitcoin. Despite the speculative nature of cryptocurrencies, there is potential for diversification through two stablecoins, Tether and USD Coin, which exhibit limited spillover effects from other cryptocurrencies and negative correlations with one another. As a stablecoin, DAI served as a potential diversifier during the COVID-19 pandemic but not during the Ukraine war. The study offers practical insights for investors on managing crypto portfolios during geopolitical and global health crises and the strategic use of stablecoins. Societally, the study examines the need for enhanced regulatory frameworks to reduce systemic risks in the highly interconnected cryptocurrency market. JEL Classification: G01, G11.
Brij B. Gupta, Akshat Gaurav, Juan Piñeiro Chousa, Ángeles López Cabarcos · 5 authors
This study suggests a method for forecasting Decentralised Finance (DeFi). In order to represent DeFi cryptocurrencies, we have used DeFi Pulse Index (DPI) that tracks the performance of some of the largest protocols in the DeFi. To get prices and sentiment analysis from social media, we used the LunarCRUSH dataset. We then conducted a time series study of DPI price using a Bi-LSTM and Long Short-Term Memory (LSTM) model and compared it withwith LSTM, BiLSTM, and GRU models. The mean square error of our proposed model is 0.0005745, and the mean absolute error is 0.01891.