The emergence of Bitcoin Exchange-Traded Funds (ETFs) marks a significant milestone in the evolution of cryptocurrency investment. This research paper investigates the potential impact of Bitcoin ETFs on existing cryptocurrency exchanges, focusing on liquidity dynamics and institutional investment trends. By examining the mechanisms through which ETFs may draw liquidity away from direct Bitcoin exchanges, this study aims to shed light on the evolving landscape of cryptocurrency trading. Additionally, the paper explores the potential for Bitcoin ETFs to attract increased institutional investment in the cryptocurrency market, analyzing the factors that may contribute to this trend. Through a comprehensive analysis of market data and expert insights, this research provides valuable insights into the evolving relationship between Bitcoin ETFs and traditional cryptocurrency exchanges.
We provide an overview of the academic literature on Automated Market Makers for Decentralized Exchanges. Our review puts an emphasis on contributions from researchers in economics and finance. We cover papers that study the optimal design of Automated Market Makers. Then we discuss models that leverage the insights from the literature on two-sided markets to characterize the equilibrium size of liquidity pools and the incentives of liquidity providers. Finally, we review recent research on the interactions between Miner Extractible Value and Decentralized Exchanges.
This work proposes a novel methodology to help in decision making in the cryptocurrency market. Two investment strategies have been designed for Ethereum (ETH), based on predictions of the price and trend of this cryptocurrency using real data. The two Ethereum cryptocurrency prediction systems rely solely on past values of other contextual stock indices, market indicators and online trends, and ignore any technical indicators of price evolution. Real data from cryptocurrency market has been collected and processed with different feature selection methods. Applying a regression approach with Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) networks, prediction models for the ETH price for 1, 7 and 15 days are obtained and compared. Also, support vector machine (SVM) is applied to predict the ETH price trend by applying a classification approach. In both approaches, sentiment analysis has been included to check its effect on the prediction results. The reliability of these prediction models in the current market has been evaluated by designing two original knowledge-based investment strategies. They are tested over two different time periods with real cryptocurrency market data. The results show that it is possible to generate up to 5.16 profit factor with few operations using these models. Furthermore, adding sentiment analysis has shown to have little influence. In this way, we contribute to the advancement of our knowledge of this volatile and still young cryptocurrency market, and specifically of the evolution of Ethereum and the factors that can influence its behavior.
Ksenija Doroslovački, Nikola Gradojević, Albane Tarnaud
This article develops a method for extracting information related to the underlying stock and cryptocurrency market sentiment from European put and call option prices. We study the evolution of market sentiment and predictability of prices in the S&P 500 index and Bitcoin (BTC/USD) futures markets during the 2020–2022 period. Several innovative temporal entropic and nonentropic measures of market sentiment based on a pessimistic, a market consensus, and an optimistic view are proposed in our nonlinear forecasting models. We show that these measures have significant predictive power for future spot prices at longer forecast horizons, where they statistically and economically outperform alternative models. We also find that the BTC/USD market is more susceptible to extreme sentiments reflected in demand-based shocks, while the information regarding the degree of pessimism in relation to the market consensus is more useful in forecasting the spot S&P 500 index movements in the presence of systemic shocks.
Cryptoassets are extremely volatile with possible volatility jumps and infrastructure noise, making the estimation of true volatility process challenging. When the high-frequency data are not available, the true volatility needs to be estimated to be further studied or forecasted. The GARCH-family models have become a norm in the field. Here, we examine the performance of 6 GARCH-type specifications with 4 distributional assumptions and compare them with 4 non-parametric range-based models built on the daily ‘candles’. Our study focuses on five popular cryptocurrencies (Bitcoin, Ethereum, BNB, XRP, and Dogecoin) between 1 July 2019 and 30 September 2022, utilizing Binance 5-minute data for realized measures as the high-frequency estimators of the true volatility process. The results reveal that the Garman-Klass estimator clearly outperforms the GARCH-family models in all studied settings, and the other range-based estimators remain competitive with the GARCH-family models. These results are crucial for studies on volatility in cryptoassets where using the GARCH-type models is a standard. When the high-frequency data are not available, the range-based estimators, and the Garman-Klass estimator in particular, should be preferred as proxies for the true volatility process over the GARCH-type models, be it in the in-sample, more qualitative studies, or the forecasting, out-of-sample exercises.
Essais sur les dynamiques de marché et la stabilité en Finance Décentralisée Cette thèse se concentre sur la compréhension et l'évaluation de la stabilité de la Finance Décentralisée, dans le but de renforcer l'ensemble de l'écosystème financier.Le premier chapitre explore les Automated Market Makers (AMM), soulignant leur évolution depuis les marchés de prédiction vers les Constant Function Market Makers (CFMMs), et examine leur compétitivité par rapport aux plateformes d'échange centralisées. Il aborde également la valeur maximale extractible (MEV) et ses implications pour les DEXs.Le deuxième chapitre se penche sur la microstructure des DEXs, en particulier Uniswap, et évalue sa capacité à aligner les prix avec ceux des marchés centralisés. Il met en lumière l'impact des coûts d'inventaire sur la précision des prix d'Uniswap, montrant une réactivité réduite des traders aux écarts de prix lorsque les tailles de pool augmentent, mais une convergence plus rapide des prix pour les paires stablecoin-stablecoin.Le troisième chapitre analyse la contagion financière au sein de Compound V2, un protocole de prêt décentralisé sur Ethereum. Il propose une méthodologie pour construire les bilans comptables des pools de liquidité de Compound et simule des scénarios de défaut pour évaluer la robustesse du protocole.Dans l'ensemble, cette thèse approfondit notre compréhension de la DeFi en examinant sa stabilité, sa microstructure de marché et les risques de contagion. Son objectif est de contribuer à un écosystème financier plus résilient et durable en enrichissant la discussion sur la DeFi.
Cryptocurrency market has striking development and aims to build open, transparent and efficient financial market. More applications are tried to build on blockchain and using cryptocurrency in multiple scenarios, and DeFi ecosystem is set up. Many niche cryptocurrencies related to DeFi market may have wide utility and demand in the future. Therefore, more research is needed to focus these cryptocurrencies and try to establish sturdy price prediction. In this study, two cryptocurrencies, Binance Coin (BNB) and Huobi Tokens (HT), which are rooted in two crypto exchange platform are used for price prediction based on three machine learning models, i.e., Random Forest (RF), Long Short-Term Memory (LSTM) and Extreme Gradient Boosting (XGBoost). Four prediction windows are selected (1, 3, 7 and 30 days span). The results for different prediction window are compared and discussed. 1 day prediction window outperforms all prediction windows with the average MSE 0.0117. Additionally, RF and XGBoost outperform LSTM with lower MSE and more stable performance, while RF and XGBoost have average MSE 0.0157 and 0.0158 separately. The research tries to predict cryptocurrencies that based on relatively niche market and discuss the performance in comprehensive ways, aiming at providing novel insights into cryptocurrency price prediction.
We examine the linkages between Bitcoin, Ethereum, Tether and USD Coin returns—in the context of the post-epidemic landscape. Our paper utilizes the underpinning concept of the fractal market hypothesis. By employing our theoretical nexus alongside the continuous wavelet transform, we elucidate the changes in investor demand during notable events. Our results suggest that all four cryptocurrencies exhibited significant volatility in response to the March 2020 pandemic and the Delta variant announcements. Moreover, the fall of Three Arrows Capital and FTX affected all cryptocurrencies, while Silicon Valley Bank’s liquidation only impacted the stablecoins. Pairwise, investors heavily demand stablecoins during financial turmoil, which causes unconditional synchronization. The two cryptocurrency categories are anti-phasing in a market regime where traders are widely unanimous, but in-phasing when the sentiment consists of divergent perceptions. These cross-cryptocurrency phases of demand persist in the long run for USD Coin but are only medium-term for Tether.
Soccer is the most popular sport in the world, and bitcoin is a global asset. Penalty shootouts represent an important stage in a soccer match and generate an enormous amount of public interest and attention. Bitcoin trading experiences a significant drop in volume and volatility during penalty shootouts at major international soccer tournaments. After the conclusion of penalty shootouts, the intensity of bitcoin trading exhibits a swift and a strong rebound. Significant fluctuations in trading volume and volatility of bitcoin around penalty shootouts are primarily driven by the variation in number of trades rather than in trade size.
Sergio Luis Náñez Alonso, Javier Jorge-Vázquez, Miguel Ángel Echarte Fernández, David Sanz Bas
Abstract A number of financial bubbles have occurred throughout history. The objective of this study was to identify the main similarities between Bitcoin price behavior during bubble periods and a number of historical bubbles. Once this had been carried out, we aimed to determine whether the solutions adopted in the past would be effective in the present to reduce investors’ risk in this digital asset. This study brings a new approach, as studies have previously been conducted analyzing the similarity of Bitcoin bubbles to other bubbles individually, but these were not conducted in such a broad manner, addressing different types of bubbles, and over such a broad time period. Starting from a dataset with 9967 records, a combined methodology was used. This consisted of an analysis of the standard deviations, the growth rates of the prices of the assets involved, the percentage increase in asset prices from the origin of the bubble to its peak and its fundamental value, and, finally, the bubble index. Lastly, correlation statistical analysis was performed. The results obtained from the combination of the above methods reveal the existence of certain similarities between the Bitcoin bubbles (2011, 2013, 2017, and 2021) and the tulip bubble (1634–1637) and the Mississippi bubble (1719–1720). We find that the vast majority of the measures taken to avoid past bubbles will not be effective now; this is due to the digital and decentralized nature of Bitcoin. A limitation of the study is the difficulty in making a comparison between bubbles that occurred at different historical points in time. However, the results obtained shed light and provide guidance on the actions to be taken by regulators to ensure the protection of investors in this digital asset.
This study investigates the application of the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm for detecting anomalies in Bitcoin trading data. With the growing significance of Bitcoin in the financial market, identifying irregular trading patterns is crucial for maintaining market integrity and preventing market manipulation. Utilizing a dataset from Kaggle, which includes features such as date, timestamp, open, high, low, close, volume, and number of trades, the data was aggregated from minute-by-minute to hourly intervals for more manageable analysis. The DBSCAN algorithm effectively identified a primary cluster comprising 29,612 data points and flagged 2 points as anomalies, achieving a precision of 1.0, recall of 0.0068, F1-score of 0.0135, and an AUC-ROC of 0.5034. The optimal parameters, determined through sensitivity analysis, were epsilon (ε) = 0.1 and min_samples = 3, yielding the highest silhouette score of 0.21499. These results underscore the algorithm's ability to accurately label anomalies while highlighting the challenge of comprehensive anomaly detection. The study contributes to the field of financial anomaly detection by demonstrating the effectiveness of DBSCAN in analyzing high-dimensional, noisy datasets. It also addresses gaps in the literature regarding the application of density-based clustering methods to Bitcoin trading data. Despite its contributions, the study acknowledges limitations, such as potential data aggregation impact and the need for further validation with different datasets. Future research directions include integrating additional features like social media sentiment and exploring hybrid approaches that combine supervised and unsupervised methods.
Purpose This study investigates the effect of the day of the week on the volatility of cryptocurrencies. Thus, we reveal investors' perceptions of the day of the week. Design/methodology/approach The EGARCH model consists of the day of the week for 2019–2022 and the volatility of 11 cryptocurrencies. Findings Empirical results show that the weekend harms cryptocurrency volatility. Also, there was positive cryptocurrency volatility at the beginning of the week. Our findings show that weekdays and weekends significantly impact cryptocurrency volatility. Besides, cryptocurrency investors are sensitive to market movements, disclosures, and regulations during the week. Holiday mode and cognitive shortcuts may cause cryptocurrency traders to remain passive on weekends. Research limitations/implications This study has some limitations. We include 11 cryptocurrencies in the analysis by limiting cryptocurrencies according to market capitalizations. Further studies may analyze a larger sample. In addition, further studies may examine the moderator and mediator effects of other financial instruments. Practical implications The empirical results have research, social and practical conclusions from different aspects. Our analysis may contribute to determining trading strategies, risk management, market efficiency, regulatory oversight, and investment decisions in the cryptocurrency market. Originality/value The calendar effect in financial markets has extensive literature. However, cryptocurrencies' weekday and weekend effect needs to be adequately analyzed. Besides, studies analyzing cryptocurrency volatility are limited. We contribute to the literature by investigating the impact of days of the week on cryptocurrency volatility with a large sample and current data.
Abstract We study the statistical properties of the Bitcoin return series and provide a thorough forecasting exercise. Also, we calibrate state‐of‐the‐art machine learning techniques and compare the results with econometric time series models. The empirical assessment provides evidence that the application of machine learning techniques outperforms econometric benchmarks in terms of forecasting precision for both in‐ and out‐of‐sample forecasts. We find that both deep learning architectures as well as complex layers, such as LSTM, do not increase the precision of daily forecasts. Specifically, a simple recurrent neural network describes a sensible choice for forecasting daily return series.
The bubbles and spikes in cryptocurrency prices increase considerably the risk on investments in these assets. In the traditional time series literature bubbles are viewed as nonstationary and non-estimable components of a process. In this paper, we adopt a different approach and consider the bubbles as inherent features of a strictly stationary causal-noncausal (mixed) Vector Autoregressive (VAR) process. This approach allows us to model and estimate the common bubbles and spikes in cryptocurrency prices. It also provides us linear combinations of cryptocurrencies that eliminate common bubbles analogously to the cointegrating vectors eliminating common trends in unit root processes. They are used to build cryptocurrency portfolios immune to the risk of common bubbles that ensure stable investment strategies. The mixed VAR model is estimated from the US Dollar prices of Bitcoin, Ethereum, Ripple, and Stellar over the period 2017–2019. We document the common bubbles and illustrate the behavior of bubble-free portfolios.
We present tail risk analysis of cryptocurrencies (Bitcoin, Ethereum and Litecoin), non-fungible tokens, stocks (FTSE 100 and S&P 500) and Gold from November 12, 2017 to March 31, 2022 using conditional model-based Value-at-Risk (VaR). We explored which model specification and distributional innovation could best capture the tail risk in these assets. Using the VaR and other risk metrics, we showed that there is no superior model/metric for capturing tail risk. We found that, for all the assets, non-Gaussian distributional assumptions best modelled the asymmetry and fat-tails in the distributions of the returns; though there was more homogeneity in the distributional assumptions for Gold unlike the other assets. Our research is crucial for internal risk modelling and may increase global investor confidence for those who blend conventional and unconventional assets. Also, this study can help investors make informed decisions about asset allocation and risk tolerance in the events of extreme market conditions. Understanding the tail risks in financial assets can help investors hedge and diversify against risk in their portfolios. The theoretical implications also show a trade-off between the different assets as the presence of tail risk reflect the potential of returns, yet possible losses in the presence of extreme events. Last, the findings reinforce the need for risk managers to re-focus their attention to a set of superior models rather than a single best model for risk assessment.
Abstract We aim to identify the determinants of non‐fungible tokens (NFTs) returns. The 10 most popular NFTs based on their price, trading volume, and market capitalisation are examined. Twenty‐three potential drivers of the returns of each NFT are considered. We employ a Bayesian LASSO model which takes into account stochastic volatility and leverage effect. The results indicate that NFTs returns are primarily driven by volatility and ethereum returns. We find a weak connection between NFTs returns and conventional assets, such as stock, oil, and gold markets.
Purpose This study aims to investigate the conditional volatility of the Asian stock market concerning Bitcoin and global crude oil price movement. Design/methodology/approach This study uses the newest Dynamic Conditional Correlation (DCC)-Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model to examine the conditional volatility of the stock market for Bitcoin and crude oil prices in the Asian perspective. The sample stock market includes Chinese, Indian, Japanese, Malaysian, Pakistani, Singaporean, South Korean and Turkish stock exchanges, with daily time series data ranging from 4 April 2015−31 July 2023. Findings The outcome reveals the presence of volatility clustering on the return series of crude oil, Bitcoin and all selected stock exchanges of the current study. Secondly, the outcome of DCC, manifests that there is no short-run volatility spillover from crude oil to the Malaysian, Pakistani and South Korean and Turkish stock markets, whereas Chinese, Indian, Japanese, Singapore stock exchanges show the short-run volatility spillover from crude oil in the short run. On the other hand, in the long run, there is a volatility spillover effect from crude oil to all the stock exchanges. Thirdly, the findings suggest that there is no immediate spillover of volatility from Bitcoin to the stock markets return volatility of China, India, Malaysia, Pakistan, South Korea and Singapore. In contrast, both the Japanese and Turkish stock exchanges exhibit a short-term volatility spillover from Bitcoin. In the long term, a volatility spillover effect from Bitcoin is observed in all stock exchanges except for Malaysia. Lastly, based on the outcome of conditional variance, it can be concluded that there was increase in the return volatility of stock exchanges during the period of the COVID-19 pandemic. Research limitations/implications The analysis below does not account for the bias induced due to certain small sample properties of DCC-GARCH model. There exists a huge literature that suggests other methodologies for small sample corrections such as the DCC connectedness approach. On the other hand, decisive corollaries of the conclusions drawn above have been made purely based on a comprehensive investigation of eight Asian stock exchange economies. However, there is scope for inclusive examination by considering other Nordic and Western financial markets with panel data approach to get more robust inferences about the reality. Originality/value Most of the empirical analysis in this perspective skewed towards the Nordic and Western countries. In addition to that many empirical investigations examine either the impact of crude oil price movement or Bitcoin performance on the stock market return volatility. However, none of the examinations quests the crude oil and Bitcoin together to unearth their implication on the stock market return volatility in a single study, especially in the Asian context. Hence, current investigation endeavours to examine the ramifications of Bitcoin and crude oil price movement on the stock market return volatility from an Asian perspective, which has significant implications for the investors of the Asian financial market.
Often referred to as A Dark Forest, Ethereum is home to predatory trading bots that prey on user transactions. Frontrunning is made simpler on Ethereum as builders & validators are incentivised to process the highest fee transactions first. One suggested mitigation strategy is to process transactions in a pseudo-random order, preventing frontrunners from predictably affecting transaction execution order. XRP Ledger, one of the oldest blockchains to use pseudorandom ordering, is launching an Automated Market Maker. This study investigates whether frontrunning techniques commonly observed in Ethereum Automated Market Makers are feasible on the XRP Ledger Automated Market Maker. In summary, our findings demonstrate that with minor adjustments, the conventional Sandwich Attack is feasible. Additionally, we unveil a distinctive attack facilitated by the integration with the Close Limit Order Book.
This work enhances the operational efficiency and balances slippage prices stability and arbitrage opportunities. The predominant mechanism in existing exchange operations is the automated market maker, which eliminates the need for customers to agree on prices and utilizes the inverse formula X × Y = K. Several existing market maker schemes, such as constant product market maker and constant mean market maker, are analyzed and compared. There are market stakeholders—such as investors, customers, arbitrageurs, and trading platforms— perceive risks and opportunities differently amidst the high volatility of the cryptocurrency market. This study discusses the merits, drawbacks, potential opportunities, and challenges associated with the mechanism. We explore the exchange efficiency and methods to balance stable slippage prices for customers and arbitrage opportunities.