Abstract This paper examines the dynamic interplay between the global geopolitical risk and eleven decentralized finance (DeFi) digital currencies during the inflationary burden caused by the Russia-Ukraine war episodes. Daily data spanning from 13 October 2021 to 29 October 2024 and the innovative Quantile-Vector Autoregressive (Q-VAR) methodology are employed for estimating the pairwise, joint and network linkages at the lower, middle and upper quantiles. High levels of geopolitical risk are more connected with bull markets of the DeFi assets and new war episodes strengthen this relation. Geopolitical tensions combined with high inflation lead to the GPR becoming major determinant of DeFi markets so contributing to the transition to the digital decentralized cashless financial system. Maker is the leading DeFi asset in this transition and constitutes a promising successor of fiat currencies that suffer from devaluation generated by conflicts.
The aim of this research is to investigate the long-term relationships among the dollar exchange rate (TRY/USD), gold (GAU/USD), the Borsa Istanbul 100 Index (BIST 100) and the prices of Bitcoin (BTC/USD), Ethereum (ETH/USD), and Binance Coin (BNB/USD). Since the series contain structural breaks, Fourier unit root tests were used to model the structural breaks. As the method of this study, the relationships between variables in the long term were examined by using Fourier Shin (FSHIN) and Shin (1994) (SHIN) cointegration tests. The findings of this study showed that cryptocurrencies are cointegrated among themselves under structural breaks in the long term; investment instruments are cointegrated among themselves. In addition, as a result of this study, it was determined financial instruments and cryptocurrencies do not move in along over time under structural breaks.
Abstract This study examines the return connectedness between decentralized finance (DeFi)’s and the Association of Southeast Asian Nations (ASEAN) stock markets using the quantile vector autoregressive framework, which allows us to investigate the connectedness at conditional quantiles. Our sample includes four major DeFi’s and six ASEAN stock markets, spanning from March 2018 to December 2022. The static results indicate a moderate level of return transmission between the system at mean and median quantile. This propagation increases substantially under extreme market conditions, establishing an asymmetric transmission across quantiles. Despite being a relatively new asset class, DeFi dominates the equity market and acts as the primary shock transmitter to the system in most instances. The dynamic analysis reveals that total system connectedness fluctuates over time and quantiles. The total system connectedness peaked during the COVID-19 and the Russia–Ukraine conflict period, indicating the impact of global events on system transmission. The optimal weight and hedge ratio estimated using the DCC-GARCH model indicate that DeFi is beneficial for portfolio construction and risk management. The rising trend in dynamic optimal weight and hedge ratio during the COVID-19 pandemic demonstrates that investors should decrease their investments in DeFi and increase hedging costs. Therefore, portfolio managers and investors should readjust their portfolio allocation in a timely manner according to different market states to build additional effective hedging and diversification strategies to avoid large losses and to reduce portfolio risk exposure.
In this study, we examined the regime-dependent dynamics and interrelationships among major cryptocurrencies, Bitcoin (BTC), Ethereum (ETH), and Monero (XMR), using high-frequency one-minute data from January 2020 to April 2025. To capture the presence of latent structural shifts without assuming Markovian transitions, we employed a Gaussian Mixture Model (GMM), which flexibly clustered distributions into two, empirically distinct regimes. Regime-specific Vector Autoregressive (VAR) models were then estimated to analyze interdependencies, spillovers, and shock transmission mechanisms across these digital assets. In the calm regime, the return dynamics were primarily self-driven, with limited cross-asset responses. Conversely, the volatile regime exhibited stronger and more persistent interlinkages, with BTC consistently acting as the principal transmitter of shocks to ETH and XMR, while ETH acts as a secondary transmitter, whereas XMR remains largely a risk recipient, absorbing external shocks with limited feedback into the system. These findings were corroborated through impulse response functions and forecast error variance decompositions, which consistently revealed asymmetric interdependence structures across the regimes. The Granger causality indicated more stable and statistically significant causal relationships in the calm regime than in the volatile regime. Furthermore, the Bai-Perron structural break tests confirmed the absence of significant deterministic breaks in the return series, reinforcing the validity of the GMM-based regime identification. These findings have practical implications for investors, regulators, and risk managers when modeling contagion and developing risk management strategies in cryptocurrency markets, especially during periods of heightened volatility.
Purpose This study aims to investigate the potential impact of total value locked (TVL) fluctuation in decentralized finance (DeFi) on banks deposits for a sample of 21 countries, including the Eurozone members and the USA, over the period from July 2018 to October 2023. Design/methodology/approach Panel vector autoregression model has been used to analyze the existence of a relationship between TVL and banks deposits. In the second stage, the impulse response function has been exercised to find out the response of banks deposits among each nation. Findings Empirical findings exhibit that while increases in TVL have a significant negative long-run association with banks deposits in a global perspective, the effect is weak, suggesting modest disruption to traditional banking services to date. However, the impact varies between short and long-run effects at the country level. Six nations exhibit substantial negative long-run effects, whereas eight countries experience only temporary decreases in deposits following TVL upticks that rebound over time, reflecting the lack of trust in DeFi services and the engagement in speculative instead of long-term activities. Research limitations/implications This study provides general insights into DeFi’s impact on banking performance and deposit stability. However, it likely has limitations in scope and time frame. Future research could explore long-term effects, specific DeFi protocols and cross-country comparisons. Practical implications This study’s findings provide key insights for policymakers, central banks and bank managers on the impact of DeFi growth on banking performance and deposit stability. The results highlight the need for adaptive regulatory frameworks and investment strategies to address the emerging DeFi ecosystem. At the country level, the observed variations suggest the importance of tailored policy approaches. These insights are crucial for developing effective regulations and strategies in the evolving financial landscape. Social implications DeFi growth may alter financial access and inclusion, raising concerns about consumer protection and literacy. It could shift power dynamics in finance, potentially reducing traditional intermediaries’ influence while creating new forms of inequality. Balancing innovation with social responsibility is crucial for equitable distribution of benefits. Originality/value Although numerous studies have been conducted on the effects of cryptocurrencies on bank deposits and stock performance across various regions, they have largely overlooked the DeFi and its role in influencing deposits as a new competitor to traditional banks services. This study aims to address this gap by examining the influence of TVL developments on banks deposits, and to what extent it can be served as an alternative to banking services.
Muhammad Danish Khan Jumain, Abdul Hafizh Mohd Azam
Bitcoin is a new asset class in the global financial system where it is known as cryptocurrency or digital currency. Bitcoin has begun to gain the world’s attention as the price of Bitcoin has been rising since 2009 as its usage increases day by day. The popularity of bitcoin has made some investors see it as a safe place. But is this true for the ASEAN-5 currencies? Therefore, this research paper aims to identify whether Bitcoin can act as a hedge for currencies in the ASEAN-5 countries namely Malaysia, Thailand, Indonesia, Singapore and the Philippines. This study uses daily data from December 1, 2014 until December 30, 2022. Empirical results of the study based on the GARCH (1,1) model show that Bitcoin can serve as a weak hedge for the ASEAN- 5 currencies except for Singapore where there is evidence of a strong hedge in the Lion City. This is particularly due to a unique Singapore's monetary policy tools which are based on their exchange rate unlike with other countries that mainly use interest rate as their tool.
Bitcoin has received a lot of attention from both investors and analysts, as it forms the highest market capitalization in the cryptocurrency market. The use of parametric GARCH models to characterize the volatility of Bitcoin returns is widely observed in the empirical literature. In this paper, we consider an alternative approach involving nonparametric method to model and forecast Bitcoin return volatility. We show that the out-of-sample volatility forecast of the nonparametric GARCH model yields superior performance relative to an extensive class of parametric GARCH models. The improvement in forecasting accuracy of Bitcoin return volatility based on the nonparametric GARCH model suggests that this method offers an attractive and viable alternative to the commonly used parametric GARCH models.
ABSTRACT This paper provides a first economic analysis of liquid staking tokens, which are derivatives representing a share of staked tokens in Proof‐of‐Stake blockchains. We document substantial time‐variation in the “liquid staking basis” as given by the price difference between a derivative staking token and its underlying cryptocurrency. We find evidence that staking rewards, concentration risks, limits to arbitrage, and behavioral factors influence this basis. The liquid staking basis is wider when the yields offered by the liquid staking protocol are low relative to the alternative of staking directly, when cryptocurrency returns are more volatile, and when secondary market liquidity is low. In contrast, it is smaller when investors pay more attention to liquid staking and when investor sentiment is positive. Furthermore, liquid staking tokens contribute a significant and overall growing amount to price discovery in the underlying cryptocurrencies.
The chapter examines the nature of long-run nonlinear trends of the closing price of Ethereum in terms of USD, from 2015m 08 to 2023m 05 using the econometric model of the Box and Jenkins’ ( 1976 ) methodology of ARIMA (p, d, q) and Hamilton (2018) decomposition. Additionally, the forecast behaviour for 2025m 01 was computed with/without the Hamilton regression filter. The automatically selected ARIMA model of Ethereum price is convergent, and its forecast path for 2025m 01 showed insignificance with seasonal fluctuations. However, its decomposition model is cyclical, cyclically trending, and seasonally fluctuated, with forecast behaviour that is convergent, stable and significant without seasonal variation.
Carlos Esparcia, Tarek Fakhfakh, Francisco Jareño, Achraf Ghorbel
Abstract This study examines the link between stocks and decentralized finance (DeFi) in terms of returns and volatility. Major G7 exchange-traded funds (ETFs) and various highly traded DeFi assets are considered to ensure the robustness of the empirical experiment. Specifically, this study applies the vector autoregression generalized autoregressive conditional heteroskedasticity (VAR-GARCH) model to examine the information transmission of these two markets on a two-way basis and the dynamic conditional correlation (DCC)-GARCH model to assess the bivariate correlation structure between each DeFi and ETF pair. The volatility spillover analysis proves a contagion effect occurred between different geographic markets, and even between markets of different natures and typologies, during the most turbulent moments of the COVID-19 crisis and the war in the Ukraine. Our results also reveal a weak positive correlation between most DeFi and ETF pairs and positive hedge ratios that approach unity during turbulent times. In addition, DeFi assets, except for the Bazaar (BZR) Protocol, can offer diversification gains when included in financial investment portfolios. These results are particularly relevant for portfolio managers and policy-makers when designing investment strategies, especially during periods of financial crisis.
This paper aims to leverage Bayesian nonlinear expectations to construct Bayesian lower and upper estimates for prices of Ether options, that is, options written on Ethereum, with conditional heteroscedasticity and model uncertainty. Specifically, a discrete-time generalized conditional autoregressive heteroscedastic (GARCH) model is used to incorporate conditional heteroscedasticity in the logarithmic returns of Ethereum, and Bayesian nonlinear expectations are adopted to introduce model uncertainty, or ambiguity, about the conditional mean and volatility of the logarithmic returns of Ethereum. Extended Girsanov’s principle is employed to change probability measures for introducing a family of alternative GARCH models and their risk-neutral counterparts. The Bayesian credible intervals for “uncertain” drift and volatility parameters obtained from conjugate priors and residuals obtained from the estimated GARCH model are used to construct Bayesian superlinear and sublinear expectations giving the Bayesian lower and upper estimates for the price of an Ether option, respectively. Empirical and simulation studies are provided using real data on Ethereum in AUD. Comparisons with a model incorporating conditional heteroscedasticity only and a model capturing ambiguity only are presented.
The BRICS countries intend to create a common payment platform for conducting transactions in digital currencies at the international level – the BRICS Bridge project. The article provides a framework for the development of international CBDC transaction platform that would address the problem of high transaction costs and excessive transaction time for international settlements, which are currently one of the main issues in the traditional economy. Processes and governance structure of the platform are described in the work. Research has shown that CBDC settlements on a distributed ledger technology (DLT)-based platform can reduce information fragmentation and increase information flow for regulatory bodies. However, the use of DLT in international settlements is associated with numerous technical and legal risks, including privacy and data security risks, financial monitoring complications, contradictions between national and international legislation, and bank disintermediation risks. To mitigate these risks, a scheme for conducting international digital transactions based on DLT, combining national and international legislation, and ensuring data privacy and security is proposed.
This chapter aims to analyze the use of distributed ledger technology (DTL) for interbank payments by investigating the research that central banks are doing to propose DLT-based wholesale central bank digital currency (W-CDBC). The findings reveal that countries are researching W-CBDC using mostly experiment research methods publishing their research through study papers, experiment papers, and projects. The DLT-based W-CBDC is being tested by countries to implement use cases such as the decentralized real time gross settlement system, the tokenized syndicated loan, the tokenization of bonds, the tokenization of assets, the securities settlement, the delivery versus payment, and the implementation of liquidity-saving mechanism. Central bank research is commonly focused on the technological dimension of W-CBDC implementation. This chapter contributes to a better understanding of the trends in implementing W-CBDC and gives researchers, central banks, and IT developers more knowledge to further the research in their countries.
Decentralized finance (DeFi) is a brand-new disruptive procedure that encourages the use of blockchain technology for developing and distributing a variety of financial goods and services. This study investigates the time-varying and asymmetric interplay between DeFi and CEE stock returns, concentrated around the COVID-19 outbreak and the Russo-Ukrainian conflict. While the associations between other cryptocurrencies and conventional assets have been studied, DeFi assets have not. For this purpose, we employ the multivariate DECO-GARCH model and cross-quantilogram framework. The results reveal a positive equicorrelation between DeFi and CEE stock market returns. Notably, the influence of DeFi on CEE stock markets is greater during the COVID-19 outbreak and the Russo-Ukrainian conflict than in the other periods. Furthermore, the cross-quantilogram estimations uncover that CEE stock markets depend less on the DeFi market at longer lag lengths. This means that the diversification benefits of DeFi against CEE stock market returns are more important for long-run investment horizons. In general, our research offers a new understanding of dependence structures, which might help investors make better investment decisions and direct their trading strategies.
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.