This dissertation examines the evolving market microstructure of digital assets, focusing on transaction costs, liquidity provision returns, and the development of innovative exchange mechanisms. In three essays, the research provides empirical evidence on digital asset trading in both traditional and emerging decentralized market architectures. Each essay addresses previously unresolved questions, offering valuable insights for researchers, practitioners, and regulators to better understand and manage the benefits, costs, and risks of trading in digital asset markets.The first essay examines the cost of trading across digital assets in traditional centralized limit-order-book exchanges and a nascent, decentralized market architecture: the Automated Market Maker. By employing a novel methodology the study extends prior research that relies on less detailed, low-frequency information. The findings reveal transaction cost advantages for Automated Market Makers with remarkable stability across varying levels of market volatility, trading volume, and market capitalization. These results offer practical insights into execution venue selection and market design considerations.The second essay explores the evolution of Automated Market Makers, using the introduction of a new generation of these exchange architectures as a case study. In addition to documenting their technical advancements, the research shows that asset pairs migrate to the new Automated-Market-Maker models based on asset-specific fundamentals. The study makes key contributions through two experimental setups, demonstrating that reductions in inventory costs and the introduction of flexible fee tiers deliver welfare benefits for both liquidity demanders and providers. These findings enrich the broader discussion on market design and highlight the potential for innovative mechanisms to enhance efficiency in both decentralized and traditional financial systems.The third essay sheds light on liquidity provision in Automated Market Makers. Leveraging granular profitability data, the study finds that a small subset of liquidity providers dominate liquidity provision. These sophisticated agents achieve significantly higher absolute and relative profits compared to retail participants, while demonstrating a high level of skill. The emergence of these de-facto intermediaries challenges the decentralized finance ethos of disintermediation, highlighting that liquidity provision, even in decentralized markets, remains dominated by specialists. Understanding the composition of participants in these nascent markets is not only crucial for practitioners but also regulators, enabling them to develop targeted and effective policies that promote fair and competitive market environments.
Jeremy EngâTuck Cheah, Thong Dao, Hung Do, Tapas Mishra
ABSTRACT This paper investigates the stability and coâmovement of cryptocurrency assets in Decentralized Finance (DeFi), with a focus on the Speed of Adjustment (SA), the rate at which shocks dissipate, and prices revert to longârun equilibrium. SA provides a critical measure of market efficiency and portfolio allocation in a highly volatile DeFi environment. We extend conventional cointegration analysis by applying a Fractionally Cointegrated Vector Autoregressive framework, which captures slow error corrections. Rolling estimations generate a timeâvarying series of SA, allowing examination of its evolution and crossâasset spillovers. The results reveal multiple cointegrating relationships, heterogeneous adjustment speeds, and strong contagion effects among DeFi assets. For instance, RPL exhibits rapid yet volatile adjustment, while LDO, BAL, and SNX revert more slowly, reflecting distinct riskâreturn tradeâoffs. Spillover analysis highlights high systemic interconnectedness, underscoring challenges for diversification and contagion management. Overall, dynamic SA emerges as a valuable forwardâlooking indicator of stability in digital asset markets.
This study proposes a hybrid model that integrates Wavelet frequency decomposition, convolutional neural networks (CNNs), and Transformers to predict correlation structures among eight major cryptocurrencies. The Wavelet module decomposes asset time series into short-, medium-, and long-term components, enabling multi-scale trend analysis. CNNs capture localized correlation patterns across frequency bands, while the Transformer models long-term temporal dependencies and global relationships. Ablation studies with three baselines (WaveletâCNN, WaveletâTransformer, and CNNâTransformer) confirm that the proposed WaveletâCNNâTransformer (WCT) consistently outperforms all alternatives across regression metrics (MSE, MAE, RMSE) and matrix similarity measures (Cosine Similarity and Frobenius Norm). The performance gap with the WaveletâTransformer highlights CNNâs critical role in processing frequency-decomposed features, and WCT demonstrates stable accuracy even during periods of high market volatility. By improving correlation forecasts, the model enhances portfolio diversification and enables more effective risk-hedging strategies than volatility-based approaches. Moreover, it is capable of capturing the impact of major events such as policy announcements, geopolitical conflicts, and corporate earnings releases on market networks. This capability provides a powerful framework for monitoring structural transformations that are often overlooked by traditional price prediction models.
Blockchain-based cryptocurrency markets present unique analytical challenges due to their decentralized nature, continuous operation, and extreme volatility. Traditional price prediction models often struggle with the binary trade execution problem in these markets. This study introduces a confidence-based classification framework that separates directional prediction from execution decisions in cryptocurrency trading. We develop a neural network system that processes multi-scale market data, combining daily macroeconomic indicators with a high-frequency order book microstructure. The model trains exclusively on directional movements (up versus down) and uses prediction confidence levels to determine trade execution. We evaluate the framework across 11 major cryptocurrency pairs over 12 months. Experimental results demonstrate 82.68% direction accuracy on executed trades with 151.11-basis point average net profit per trade at 11.99% market coverage. Order book features dominate predictive importance (81.3% of selected features), validating the critical role of blockchain microstructure data for short-term price prediction. The confidence-based execution strategy achieves superior risk-adjusted returns compared to traditional classification approaches while providing natural risk management capabilities through selective trade execution. These findings contribute to blockchain technology applications in financial markets by demonstrating how a decentralized market microstructure can be leveraged for systematic trading strategies. The methodology offers practical implementation guidelines for cryptocurrency algorithmic trading while advancing the understanding of machine learning applications in blockchain-based financial systems.
This study aims to understand the relationship among cryptocurrency, stock, and gold markets. Cointegration, structured VAR, and causality tests were used with daily datasets from 11/09/2017 to 11/17/2023. A cryptocurrency basket is accepted as the cryptocurrency market for this study. The stock markets have a one-way relationship both with the gold and cryptocurrency markets in the short-run. All markets have effects on other marketsâ price variances, as well. The price shocks of the markets to each other are not so essential for the prices. However, their own price shocks impact their prices for a few days. The stock market has asymmetric relationships with the gold and cryptocurrency markets. A 1.00 % rise in stock price causes declines in the gold and cryptocurrency prices by 2.35% and 2.42%, respectively. If the gold market or stock market is ignored, a 1.00% rise in gold prices causes a 0.69% rise in cryptocurrency prices, or a 1.00% rise in stock prices raises the cryptocurrency prices by 4.03%.
Cryptocurrency investment is a rapidly growing financial sector, marked by high volatility, decentralized technologies, and significant profit potential. Investors use strategies like long-term holding (âHODLingâ), portfolio diversification, and short-term trading. âHODLingâ relies on long-term value appreciation but requires resilience to price fluctuations. Diversifying with assets like Bitcoin and Ethereum reduces risk due to their low correlation with traditional investments. The crypto market is highly sensitive to geopolitical, economic, and technological factors, attracting investors during economic instability. Advanced models like LASSO and AutoEncoder aid in price prediction and strategy optimization. Despite high return potential, careful risk management is essential due to volatility and regulatory uncertainty. This study experimentally applies identical cryptocurrency portfolios to different investment strategies, identifying the most profitable approach.
StanisĆaw DroĆŒdĆŒ, Robert KluszczyĆski, JarosĆaw KwapieĆ, Marcin WÄ torek
Multifractality in time series analysis characterizes the presence of multiple scaling exponents, indicating heterogeneous temporal structures and complex dynamical behaviors beyond simple monofractal models. In the context of digital currency markets, multifractal properties arise due to the interplay of long-range temporal correlations and heavy-tailed distributions of returns, reflecting intricate market microstructure and trader interactions. Incorporating multifractal analysis into the modeling of cryptocurrency price dynamics enhances the understanding of market inefficiencies, may improve volatility forecasting and facilitate the detection of critical transitions or regime shifts. Based on the multifractal cross-correlation analysis (MFCCA) whose spacial case is the multifractal detrended fluctuation analysis (MFDFA), as the most commonly used practical tools for quantifying multifractality, in the present contribution a recently proposed method of disentangling sources of multifractality in time series was applied to the most representative instruments from the digital market. They include Bitcoin (BTC), Ethereum (ETH), decentralized exchanges (DEX) and non-fungible tokens (NFT). The results indicate the significant role of heavy tails in generating a broad multifractal spectrum. However, they also clearly demonstrate that the primary source of multifractality are temporal correlations in the series, and without them, multifractality fades out. It appears characteristic that these temporal correlations, to a large extent, do not depend on the thickness of the tails of the fluctuation distribution. These observations, made here in the context of the digital currency market, provide a further strong argument for the validity of the proposed methodology of disentangling sources of multifractality in time series.
This study investigates the dynamic relationship between order flow toxicity, measured by the volume-synchronized probability of informed trading (VPIN), and price jumps in the Bitcoin market using high-frequency data and vector autoregressive model (VAR) modelling. By integrating behavioral finance theory to market microstructure framework, we explore how informed trading activity influences jumps in price, and how traders respond to such volatility. Our findings reveal that VPIN significantly predicts future price jumps, with positive serial correlation observed in both VPIN and jump size, suggesting persistent asymmetric information and momentum effects. On the contrary, price jumps occasionally affect VPIN. This study also identifies time-zone and day-of-the-week effects in VPIN, highlighting the role of global trading patterns. The results are robust among the choices of jump tests including Jiang and Oomen (2008) test which is empirically robust against market microstructure noise. These results contribute to a deeper understanding of intraday volatility in cryptocurrency markets and offer practical implications for risk management, trading strategy design, and regulatory oversight.
Abdul Malik, Gayatri Putri, Hesti Putri, Ahmad Badruddin
The proliferation of crypto-assets has raised critical questions about their impact on global financial stability. This study rigorously investigates the structural evolution of the cryptocurrency market's role within the global financial system, testing the hypothesis that it has transitioned from a peripheral, shock-absorbing entity into a systemically significant transmitter of financial risk. We employ a Time-Varying Parameter Vector Autoregression (TVP-VAR) model on daily data from January 1, 2017, to December 31, 2024, examining the dynamic connectedness between a bespoke, rebalanced cryptocurrency index (CRIX20) and key global financial indicators (S&P 500, MSCI World, VIX, DXY). The econometric framework utilizes a Bayesian estimation approach with standard priors, a 200-day rolling window, and a 10-day forecast horizon for Generalized Forecast Error Variance Decompositions (GFEVD). Methodological robustness is confirmed through structural break tests and sensitivity analysis of the forecast horizon. Our findings reveal a profound structural transformation. Prior to mid-2020, the cryptocurrency market was a consistent net receiver of financial spillovers. A structural break, formally identified in the third quarter of 2020, marks a definitive regime shift. Post-break, the crypto market has become a significant and persistent net transmitter of risk to the traditional financial system. The total connectedness index for the entire system shows a marked secular increase, with the crypto market's contribution to systemic risk growing substantially. Gross spillover analysis confirms this shift is driven by a dramatic increase in risk transmission from the crypto market to other assets. In conclusion, the cryptocurrency market can no longer be considered an isolated ecosystem; it is now an integral and potentially destabilizing component of the global financial architecture. The era of crypto-assets as reliable diversifiers has waned, replaced by a new reality where shocks originating within this market pose a credible threat to broader financial stability. These findings present urgent challenges for regulatory oversight, systemic risk monitoring, and portfolio management.
Ifran Khan, Huangbao Gui, Chin Man Chui, Mrs Faryal · 6 authors
This study investigates the dynamic volatility transmission between leading cryptocurrencies (Bitcoin, Ethereum, and Binance Coin) and major Chinese firms in the technology (Tencent and Alibaba), green energy (CATL, BYD, and LONGi), and traditional energy (PetroChina) sectors, including the CSI 300 index. Employing the frameworks of Diebold and Yilmaz (2012) and BarunĂk and KĆehlĂk (2018) on daily data from July 2018 to May 2025, we demonstrate significant cross-market risk transmission. The total connectedness index averages 34.77%, soaring to over 50% during the COVID-19 crisis, underscoring heightened systemic vulnerability. Our key finding identifies the CSI 300 index and cryptocurrencies (BTC, ETH) as the primary net transmitters of volatility shocks, whereas Chinese tech and energy firms (Tencent, CATL, and PetroChina) act as the main net receivers. A critical insight from the frequency decomposition is the absolute dominance of short-term spillovers (1â4 days), which constitute 34.85% of total connectedness, vastly outweighing the minimal effects in the medium- (4â10 days: 0.78%) and long-term (beyond 10 days: 0.52%). Investor sentiment, speculation, and news shocks drive short-term volatility spillovers from cryptocurrencies to stocks, particularly evident in their strong correlation with Chinese tech and energy equities. We attribute these spillovers to shared investor bases, sectoral links like crypto mining's energy demand, and regulatory interdependencies. Our evidence confirms that cryptocurrency markets are now integral to global financial stress, transmitting significant volatility to real-economy sectors. This study offers critical insights for investors and policymakers managing risk in an increasingly interconnected financial landscape.
Purpose This paper aims to delve into the intricate dynamics of the Bitcoin market, combining established financial theories with innovative methodologies to assess market efficiency and identify anomalies. Design/methodology/approach The paper investigates the efficiency of the Bitcoin market through a diverse set of lenses, using statistical methods such as linear and rank correlations, mean absolute error, mean squared error and introducing a unique copula-based approach for modeling dependence structures. The authors explore the weak form of informational market efficiency, focusing on the period before and after 2014. Findings Notable findings from this study include evidence of partial inefficiency, the emergence of anomalies, and the presence of predictability, challenging the assumption of a pure martingale. Structured into sections reviewing relevant literature, outlining this empirical methodology, presenting robust empirical results and concluding with insights and implications, this paper contributes to a deeper understanding of Bitcoinâs market behavior. Originality/value Despite the extensive literature on market efficiency, the Bitcoin market remains relatively unexplored. This study addresses this gap, offering a nuanced analysis that goes beyond traditional measures. This work emphasizes the relevance of adopting innovative approaches to assess market efficiency in a rapidly evolving financial landscape.
Rabbiya Younas, Hafiz Muhammad Raza Ur Rehman, Gyu Sang Choi
Cryptocurrencies function as a digital exchange medium operating on network-based technology, where records are secured using cryptographic algorithms such as Secure Hash Algorithm 2 (SHA-2) and Message Digest 5 (MD5). These cryptocurrencies utilize blockchain technology to provide transparent, reliable, and immutable transactions. Consequently, cryptocurrencies have gained significant traction across multiple sectors, particularly finance. However, their value is still prone to considerable fluctuations, which raises concerns about the risks associated with investments. The emerging discipline of cryptocurrency forecasting has gained popularity worldwide, and academics are employing a variety of deep learning (DL) and machine learning (ML) techniques to investigate the elements that influence cryptocurrency values. Among the various DL methods, LSTM has demonstrated noteworthy efficiency. Nevertheless, there are intrinsic downsides to LSTM, notably due to its sequential nature, which hinders parallelization and complicates the modeling of both short- and long-term dependencies. To address these shortcomings, the Transformer architecture has emerged as a potent solution. The Transformer is widely used in DL for its exceptional parallelization capabilities and its capacity to extract broad, distant data dependencies. Recent studies have explored Transformer-based approaches for cryptocurrency price forecasting, particularly for modeling long-term dependencies. However, these models often exhibit limitations in capturing high-frequency, short-term fluctuations, making them less suitable for short-term prediction tasks. Our proposed methodology introduces a novel Transformer-based hybrid framework designed to enhance forecasting accuracy across various time scales. We evaluate the forecasting accuracy for 10 cryptocurrencies at hourly, daily, and yearly frequencies. The findings show that, in comparison to other DL techniques such as LSTM, RNN, and baseline Autoformer, our model achieves superior accuracy. Furthermore, we benchmark our method against prominent Transformer variants such as Informer and FEDformer, and observe improved performance in both short- and long-term forecasting scenarios. These results indicate that our proposed model consistently outperforms existing state-of-the-art Transformer-based approaches in cryptocurrency price prediction.
Mohamed Amine Nabli, Ikrame Ben Slimane, Haykel Hamdi
Purpose This study explores the quantile-on-quantile connectedness between major European listed football clubs and Bitcoin, providing a deeper understanding of their interdependencies. The selection of these assets is motivated by their prominent roles in both financial and sports markets, especially during periods of market volatility. By employing advanced portfolio optimization strategies, the research examines how these strategies enhance resilience and effectively manage risk during periods of market volatility. Design/methodology/approach Utilizing the quantile-on-quantile connectedness framework by Gabauer and Stenfors (2024), a robustness test is conducted using Quantile Granger Causality analysis by Jeong et al. (2012). Optimal investment portfolios are constructed using three strategies: Minimum Variance Portfolio (MVP), Minimum Correlation Portfolio (MCP) and Minimum Connectedness Portfolio (MCoP). The research analyzes a decade of data (2014â2024) from major European listed football clubs. Findings Results demonstrate that inversely related quantiles exhibit stronger total connectedness than directly related ones, highlighting the importance of managing tail risks. Bitcoin displays characteristics of a safe-haven asset during market downturns, yet under specific conditions, it can act as a shock transmitter for clubs such as Juventus and Olympique Lyonnais. Portfolio analysis indicates that Bitcoin serves as a critical diversification tool, with its optimal allocation varying across different strategies. Research limitations/implications These findings provide important insights into the dynamic relationship between football clubs and Bitcoin, offering practical implications for investors and portfolio managers. This studyâs focus on market volatility and tail risks highlights Bitcoinâs role in improving portfolio resilience, enabling more informed decision-making in investment strategies. Originality/value This study contributes to the existing literature by exploring the novel interplay between European football clubs and Bitcoin using quantile-based connectedness analysis. It underscores the strategic role of Bitcoin as a diversification tool, offering valuable insights into risk management and portfolio optimization in dynamic financial markets.
Binh Thanh Nguyen, Thanh Tuan Chu, Son Ha, Anh Tuan Nguyen
Purpose Our research augments the expanding body of literature concerning the capability of prevalent Large Language Models (LLM) tools in supporting financial professionals. We introduce a framework to leverage ChatGPT to assess market sentiment through the analysis of social media data. Design/methodology/approach We use the LLM models to construct market sentiment indicators based on Twitter tweets and use those indicators to explain Bitcoin return. Findings Our analysis uncovers that sentiment indicators crafted with ChatGPT4o/ChatGPT3.5 significantly affect Bitcoin returns, even when accounting for a broad array of control variables and other pre-established sentiment indicators. Originality/value These insights imply that ChatGPT4o/ChatGPT3.5 could empower financial professionals to discover sentiment information from Twitter tweets that were overlooked by previously introduced sentiment indicators concerning Bitcoin.
Mohammad Vahidpour, Amir Daneshvar, Mohsen Amini Khouzani, Mahdi Homayounfar
Purpose This study aims to enhance cryptocurrency price and trend prediction by applying advanced machine learning (ML) techniques. Given the marketâs high volatility and complexity, the research identifies effective models for different conditions, providing insights for investors and risk management. Design/methodology/approach This study proposes a six-stage framework for cryptocurrency price prediction, integrating advanced ML techniques. Data from ten cryptocurrencies are processed, extracting 37 key features, including return, the Fear and Greed Index and various technical indicators. The model employs Deep Q-Networks (DQN), Long Short-Term Memory (LSTM) and multiple regression methods such as linear regression, support vector regression, ridge, LASSO, decision tree, Random Forest, multi-layer perceptron, stochastic gradient descent, elastic net and Bayesian regression. Model performance is evaluated using trading strategies and metrics like accuracy, sensitivity, recall, MSE, MAE and F1-score. Findings The results indicate that complex models like DQN and LSTM excel in volatile markets due to their ability to capture intricate price patterns, whereas simpler models such as linear regression and ridge regression perform better in stable conditions. The multi-layered parallel design enhances computational efficiency, enabling independent asset evaluation. These findings highlight the potential of artificial intelligence in improving prediction accuracy and supporting informed investment decisions. Originality/value This research introduces a novel six-stage ML framework incorporating diverse predictive models and key features for cryptocurrency forecasting. The multi-layered parallel approach enhances computational efficiency, setting this study apart from existing research. The comparative analysis of models offers valuable guidance for investors, traders and financial analysts navigating volatile cryptocurrency markets.
Ethereumâs introduction of smart contracts has significantly expanded blockchain use cases, enabling decentralized applications. Since all transactions are publicly available, the system can be modeled as a complex network, allowing us to uncover emergent user behavior and explore the underlying dynamics of the ecosystem. In this study, we focus on analyzing the structural differences within the Ethereum system across three distinct market regimes: bull, bear, and sideways. To achieve this, we apply a Hidden Markov Model to the log-return time series to uncover the underlying states, revealing three differentiated states, each corresponding to a specific market regime. Next, we investigate the network structural differences across these regimes, finding meaningful variations. During the bear regime, the out-degree distribution is more heterogeneous, with the largest hub exhibiting more extreme out-degree values. Additionally, during the bull and sideways regimes, we observe higher levels of reciprocity, clustering, and modularity compared to the bear regime. These findings suggest that during bull and sideways markets, the interaction patterns are more complex, and the community structure is more cohesive. Overall, our work underscores how market conditions shape trading patterns and the structural properties of the Ethereum transaction network, providing new insights into the interplay between market regimes, network topology, and user behavior in decentralized ecosystems.
We introduce multiscale topological analysis for studying cryptocurrency price series in the time domain. This is achieved by first performing a coarse-grained procedure on the volatility series at multiple temporal scales, and then constructing consecutive visibility graphs from the resulting coarse-grained series. We show that their degree distribution presents a likely power-law behavior. This scaling characteristics keeps invariant even varying time scale factor. Interestingly, we find that the number of cliques that capturing higher-order relations, presents a clear power-law behavior with the time scale factor. Their associated scaling exponent shows a monotonically decreasing pattern. Our work reveals the function of higher-order topological structure underlying cryptocurrency time series.
Suleiman Dahir Mohamed, Mohd Tahir Ismail, Majid Khan Majahar Ali
Bitcoin market has exhibited substantial volatility over time.Bitcoin returns exhibit high standard deviation.This study employs the GARCH (1,1) model with normal (norm), Studentt (std), and generalized error distributions (ged) to estimate Bitcoin conditional volatility.Bitcoin exhibits fat-tailed returns, volatility clustering, and a remarkably high persistence value.The GARCH (1,1)-ged model showed superior performance compared to other models when evaluated using LL, AIC, and BIC criteria.The indicator saturation (IS) method was employed to concurrently detect historical daily breaks, trend breaks, and outliers in Bitcoin volatility data.The indicator saturation approach revealed that, for the past decade, historical Bitcoin volatility has had 6 outliers, 31 breaks, and 74 trend breaks under the normal distribution, 0 outliers, 26 breaks, and 83 trend breaks under the student-t distribution, and 1 outlier, 29 breaks, and 77 trend breaks under the ged distribution.This shows that assuming a heavy tail led to fewer outliers and breaks, and as the frequency of trend breaks increases, it also shows more volatility clusters represented by GARCH.These discoveries have the potential to comprehend the influence of events on financial markets and guarantee stability in the evaluation of financial risk, management of portfolios, and modeling endeavors.
ABSTRACT This paper develops a model of a cryptocurrency by incorporating mining into the otherwise standard searchâtheoretic monetary framework. As usual, multiple equilibria exist. To obtain a sharp prediction on whether a cryptocurrency' s value will last in the future, I propose a notion of equilibrium refinement based on the feature that mining uses real resources. This refinement eliminates all equilibria where the value of the cryptocurrency is zero at some point in time or converges to zero over time. This result suggests that agents can collectively sustain the value of the cryptocurrency using costly mining as a coordinating device.
This study examines links between global financial stress and cryptocurrency returns from 1 January 2017 to 31 January 2025, while explicitly accounting for commodity markets. We use an econometric toolkit: unit-root and cointegration testing, ARDL bounds, TodaâYamamoto causality, and a two-state Markov Switching model to trace long-run equilibrium and transmission mechanisms across cryptocurrencies (BGCI), systemic stress (OFR-FSI), volatility measures (VIX, VVIX, VSTOXX, VVSTOXX, MOVE), major equities and bonds, and three commodities (gold, oil, copper). Results show robust long-run cointegration between BGCI and several financial variables, including S&P/ASX 200 and the Bloomberg Barclays Bond Index; models that include commodities continue to support these long-term links. TodaâYamamoto tests reveal that stress and volatility indices unidirectionally transmit shocks to cryptocurrencies and commodities, while gold displays a bidirectional relationship with BGCI, indicating a conditional safe haven interaction. Markov Switching estimates show amplified co-movement among BGCI, gold and bonds in stress regimes, with the model predominantly remaining in a normal state. Overall, cryptocurrencies are embedded within the broader financial system; commodities, especially gold, are used to moderate the stress crypto transmission and offer conditional diversification value during turmoil.