Paul Delfabbro, Daniel L. King, Jennifer N. Williams
BACKGROUND AND AIMS: Crypto-currency trading is a rapidly growing form of behaviour characterised by investing in highly volatile digital assets based largely on blockchain technology. In this paper, we review the particular structural characteristics of this activity and its potential to give rise to excessive or harmful behaviour including over-spending and compulsive checking. We note that there are some similarities between online sports betting and day trading, but also several important differences. These include the continuous 24-hour availability of trading, the global nature of the market, and the strong role of social media, social influence and non-balance sheet related events as determinants of price movements. METHODS: We review the specific psychological mechanisms that we propose to be particular risk factors for excessive crypto trading, including: over-estimations of the role of knowledge or skill, the fear of missing out (FOMO), preoccupation, and anticipated regret. The paper examines potential protective and educational strategies that might be used to prevent harm to inexperienced investors when this new activity expands to attract a greater percentage of retail or community investors. DISCUSSION AND CONCLUSIONS: The paper suggests the need for more specific research into the psychological effects of regular trading, individual differences and the nature of decision-making that protects people from harm, while allowing them to benefit from developments in blockchain technology and crypto-currency.
\nAmaç – Çalışmada önemli fiyat dalgalanmalarına sahip kripto paralardan en yüksek işlem hacmine sahip olan Bitcoin’in volatilite dinamiklerini tespit etmek için Bitcoin getirilerinin yükseliş/kazandıran ve düşüş/kaybettiren rejimleri, rejim geçiş olasılıkları ve rejimde kalma sürelerinin tespit edilmesi amaçlanmıştır. Yöntem – Çalışmada Bitcoin getirilerinin yükseliş/kazandıran ve düşüş/kaybettiren rejimleri, rejim geçiş olasılıkları ve rejimde kalma süreleri hem değişimlerin hem de rejim geçiş olasılıklarının hesaplanmasına imkan veren Markov Rejim Değişim Modeli kullanılmıştır. Bulgular – Çalışma kapsamında çalışmaya konu periyotta Bitcoin getiri serisi için en uygun modelin üç rejimli MSIH(3)-AR(1) modeli olduğu tespit edilmiştir. Modele ilişkin analizler yapıldığında ise söz konusu modelin doğrusal modele göre daha güçlü sonuçlar verdiği görülmektedir. Üç rejimden oluşan modelde katsayısı negatif olan rejim 1 daralma rejimi dönemini, katsayıları pozitif olan rejim 2 geçiş ve rejim 3 ise genişleme rejimi dönemini göstermektedir. Bitcoin getiri serisinin bir rejimdeyken bir sonraki dönemde aynı rejimde kalma olasılıkları yüksek iken bir sonraki dönemde özellikle rejim 1’den diğer rejimlere, diğer rejimlerden ise rejim 1’e geçiş olasılıklarının düşük olduğu tespit edilmiştir. Tartışma – Çalışma kapsamında ele alınan dönem için Bitcoin getirilerinin rejim kalıcılığının yüksek olduğu tespit edilmiştir. Bu bağlamda yatırımcıların, Bitcoin getirilerinin incelenen dönemde hangi rejimde olduğunu bilmesi durumunda, bir sonraki dönemde bu rejimde kalma olasılığını tahminini yaparak yatırım kararını buna göre verme imkanı bulunmaktadır. Bununla birlikte ortalama rejimlerde kalma sürelerinin düşük olduğu göz önüne alındığında özellikle Bitcoin’i portföylerinde bulunduran aktif yatırımcıların sürekli olarak bu aracın rejim değişimlerini takip etmesi durumunda portföylerinin faydasını arttırma imkanı yakalayacağı görülmektedir.\n
This paper examines interlinkages and hedging opportunities between nine major cryptocurrencies for the period between 30 September 2015 and 4 June 2020, which notably includes the coronavirus disease 2019 (COVID-19) outbreak lasting from early 2020 through the end of the sample period. The results of dynamic conditional correlation (DCC) analysis using a minimum connectedness approach show a high degree of correlation between cryptocurrencies throughout the sample period. However, the correlations reach their minimum values during the COVID-19 pandemic, which indicates that cryptocurrencies acted as a hedge or safe haven during the stressful period of the COVID-19 pandemic. The weight of cryptocurrencies was significantly reduced and their hedging effectiveness varied greatly during the pandemic, which indicates that investors’ preferences changed during the COVID-19 period.
Christoph J. Börner, Ingo Hoffmann, Lars M. Kürzinger, Tim Schmitz
This paper evaluates and assesses the risk associated with capital allocation in cryptocurrencies (CCs). In this regard, we take a basket of 27 CCs and the CC index EWCI$^-$ into account. After considering a series of statistical tests we find the stable distribution (SDI) to be the most appropriate to model the body of CCs returns. However, as we find the SDI to possess less favorable properties in the tail area for high quantiles, the generalized Pareto distribution is adapted for a more precise risk assessment. We use a combination of both distributions to calculate the Value at Risk and the Conditional Value at Risk, indicating two subgroups of CCs with differing risk characteristics.
Shaen Corbet, Yang Hou, Yang Hu, Charles Larkin · 6 authors
We examine the interactions between cryptocurrency price volatility and liquidity during the outbreak of the COVID-19 pandemic. Evidence suggests that these developing digital products have played a new role as a potential safe-haven during periods of substantial financial market panic. Results suggest that cryptocurrency market liquidity increased significantly after the WHO identification of a worldwide pandemic. Significant and substantial interactions between cryptocurrency price and liquidity effects are identified. These results add further support to the argument that substantial flows of investment entered cryptocurrency markets in search of an investment safe-haven during this exceptional black-swan event.
AI and data driven solutions have been applied to different fields and achieved outperforming and promising results. In this research work we apply k-Nearest Neighbours, eXtreme Gradient Boosting and Random Forest classifiers for detecting the trend problem of three cryptocurrency markets. We use these classifiers to design a strategy to trade in those markets. Our input data in the experiments include price data with and without technical indicators in separate tests to see the effect of using them. Our test results on unseen data are very promising and show a great potential for this approach in helping investors with an expert system to exploit the market and gain profit. Our highest profit factor for an unseen 66 day span is 1.60. We also discuss limitations of these approaches and their potential impact on Efficient Market Hypothesis.
Alexander Fleiss, Gihyen Eom, Daria Tikhonova, Eric Tu
We compare the explainability of cryptocurrency returns from macro and microeconomic risk factors during stressed and normal market environments, in particular, analyzing the effects of the Covid-19 pandemic to cryptocurrency return explainability. We find that risk-premiums are encapsulated within cryptocurrency-specific market factors in both stressed and normal market conditions. Furthermore, cryptocurrency factors, particularly relating to liquidity, momentum, and counterparty risk, showed evidence of providing stronger predictability of cryptocurrency returns during the Covid-19 pandemic compared to pre-pandemic levels. We find that during the stressed market environment, Fama-French 5 factors continue to provide low explainability to cryptocurrency returns.
Abstract This paper adopts the fractional cointegrated vector autoregressive (FCVAR) model to examine high‐frequency price discovery of bitcoin spot and futures prices from December 18, 2017 to July 31, 2020. We find that bitcoin spot and futures prices exhibit long memory properties and they are fractionally cointegrated. The result shows that the bitcoin futures market dominates the price discovery process. Interestingly, during the Covid‐19 pandemic, the bitcoin price discovery leadership has switched to the spot market. Moreover, we find that the bitcoin futures market follows a long‐run contango. The nonfractional CVAR model overestimates the price discovery of the futures market.
This dissertation contributes to the growing body of research on cryptocurrencies by addressing their economic, behavioral, and financial dimensions through a series of empirical essays. The studies collectively examine the determinants of cryptocurrency pricing, the role of investor sentiment and political uncertainty, and the implications of advanced portfolio optimization techniques—including machine learning approaches—for cryptocurrency investment management. The findings offer new insights into how digital assets behave as alternative investments, how they respond to external shocks, and how quantitative methods can be used to enhance portfolio performance in this highly volatile and evolving market. The first essay, Do FEARS Drive Bitcoin?, explores the relationship between investor sentiment and Bitcoin returns using a novel sentiment index derived from financial and media-based fear measures (FEARS). Employing econometric time-series models, the study finds that heightened investor fear significantly predicts short-term increases in Bitcoin trading volumes and volatility, consistent with Bitcoin’s perception as both a speculative and hedging instrument. However, the analysis also reveals asymmetric effects: while fear-driven demand raises short-term prices, sustained pessimism weakens long-term valuation. Robustness tests confirm the persistence of sentiment effects across multiple proxies and subperiods, demonstrating that behavioral factors remain central to cryptocurrency price formation. The second essay, Risk-Based Portfolio Optimization for Cryptocurrencies, examines how traditional risk-based allocation frameworks—such as minimum variance, equal risk contribution, and risk parity—perform in a cryptocurrency context characterized by extreme returns and tail dependencies. Using a dataset of major digital assets, the analysis compares the performance of various optimization strategies under different market regimes. The findings reveal that while risk parity strategies deliver superior diversification benefits, they remain vulnerable to extreme downside risk. Incorporating tail-risk measures and conditional performance adjustments substantially improves risk-adjusted returns, emphasizing the need for adaptive and non-normal risk frameworks in digital asset management. The third essay, Bitcoin and Global Political Uncertainty – Evidence from the U.S. Election Cycle, investigates Bitcoin’s role as a hedge or safe haven during periods of heightened political uncertainty. Using event-study and regression approaches, the results show that Bitcoin exhibits strong hedging characteristics during politically volatile periods, particularly around U.S. election cycles. However, its behavior varies asymmetrically with the type of uncertainty—economic versus institutional—highlighting that Bitcoin’s hedging function is conditional rather than universal. The fourth essay, Cryptocurrencies and the Low Volatility Anomaly, tests whether the well-documented low-volatility anomaly in equity markets extends to the cryptocurrency universe. Using portfolio sorting and cross-sectional regression analyses, the study finds that low-volatility cryptocurrencies outperform their high-volatility counterparts on a risk-adjusted basis, even after accounting for liquidity and size effects. This evidence challenges the perception of cryptocurrencies as uniformly speculative assets and suggests that market inefficiencies and behavioral biases may sustain persistent return anomalies in digital asset markets. The final essay, Beyond Risk Parity – A Machine Learning-Based Hierarchical Risk Parity Approach on Cryptocurrencies, proposes a novel portfolio optimization framework that integrates machine learning techniques with hierarchical clustering methods. By capturing complex non-linear relationships between assets, the hierarchical risk parity (HRP) approach outperforms traditional covariance-based methods in terms of diversification, turnover reduction, and out-of-sample stability. Empirical tests confirm that the machine learning-enhanced HRP model achieves higher Sharpe ratios and lower drawdowns across multiple rebalancing frequencies, demonstrating its robustness for high-dimensional and noisy cryptocurrency data. Collectively, the essays provide a comprehensive and multi-faceted understanding of the cryptocurrency market from both behavioral and quantitative perspectives. They highlight the dual nature of digital assets—as speculative vehicles sensitive to sentiment and uncertainty, and as emerging investment instruments that can be systematically managed through advanced quantitative techniques. The dissertation advances academic discussions on asset pricing, risk management, and market efficiency in the context of decentralized finance, while offering practical insights for institutional investors navigating the challenges and opportunities of the rapidly evolving digital asset ecosystem.
Massimo La Morgia, Alessandro Mei, Francesco Sassi, Julinda Stefa
Cryptocurrencies are increasingly popular. Even people who are not experts have started to invest in these assets, and nowadays, cryptocurrency exchanges process transactions for over 100 billion US dollars per month. Despite this, many cryptocurrencies have low liquidity and are highly prone to market manipulation. This paper performs an in-depth analysis of two market manipulations organized by communities over the Internet: The pump and dump and the crowd pump. The pump and dump scheme is a fraud as old as the stock market. Now, it has new vitality in the loosely regulated market of cryptocurrencies. Groups of highly coordinated people systematically arrange this scam, usually on Telegram and Discord. We monitored these groups for more than 3 years, detecting around 900 individual events. We report on three case studies related to pump and dump groups. We leverage our unique dataset of the verified pump and dumps to build a machine learning model able to detect a pump and dump in 25 seconds from the moment it starts, achieving the results of 94.5% of F1-score. Then, we move on to the crowd pump, a new phenomenon that hit the news in the first months of 2021, when a Reddit community inflated the price of the GameStop stocks (GME) by over 1,900% on Wall Street, the world’s largest stock exchange. Later, other Reddit communities replicated the operation on the cryptocurrency markets. The targets were DogeCoin (DOGE) and Ripple (XRP). We reconstruct how these operations developed and discuss differences and analogies with the standard pump and dump. We believe this study helps understand a widespread phenomenon affecting cryptocurrency markets. The detection algorithms we develop effectively detect these events in real-time and helps investors stay out of the market when these frauds are in action.
Since 2018, the cryptocurrency trading landscape has evolved from a collection of spot markets (fiat for cryptocurrency) to a hybrid ecosystem featuring complex and popular derivatives products. In this paper we explore this new paradigm through a study of BitMEX, one of the first and most successful derivatives platforms for leveraged cryptocurrency trading. BitMEX trades on average over 3 billion dollars worth of volume per day, and allows users to go long or short Bitcoin with up to 100x leverage. We analyze the evolution of BitMEX products—both settled and perpetual offerings that have become the standard across other cryptocurrency derivatives platforms. We additionally utilize on-chain forensics, public liquidation events, and a site-wide chat room to describe the diverse ensemble of amateur and professional traders that forms this community. These traders range from wealthy agents running automated strategies, to individuals trading small, risky positions and focusing on very short time-frames. Finally, we discuss how derivative trading has impacted cryptocurrency asset prices, notably how it has led to dramatic price movements in the underlying spot markets.
Chetan G. Shinde, Atharav Upare, Vishal Pawar, Ajay Raut
We propose a new blockchain-based framework for a completely decentralized stock market and bitcoin exchange in this paper. By proposing a groundbreaking framework utilizing blockchain to build a decentralized bitcoin and stock exchange network, this paper discusses the shortcomings of conventional centralized stock exchange platforms, High transaction costs, vulnerable centralized governance, and a lack of clarity in consumer behavior and algorithms are just a few of the problems. Blockchain technology consists of a large number of computer nodes that share a shared ledger securely without the need for intermediaries of any sort. The proposed blockchain-based solution addresses the disadvantages of the centralized stock exchange architecture by ensuring the integrity and security of the properties and orders of the owner, by self-enforcing intelligent agreements between parties, and by consensus algorithms, by achieving democratic and effective decisions on the execution and settlement of orders. Intelligent contracts are used in the proposed architecture to enforce the validation of the owner's rights as well as the proper execution and settlement of orders, reducing the need for a central authority to ensure that the stock exchange process is accurate. The proposed system proposes a hybrid platform that incorporates cryptocurrency and stock trading. The solution was tested for a subset of rules for the Stock Exchange by implementing a prototype in Ethereum.
The legitimacy of virtual currencies as an alternative form of monetary exchange has been the centre of an ongoing heated debated since the catastrophic global financial meltdown of 2007-2008. Our study tests the informational market efficiency of cryptomarkets by investigating the weak-form efficiency of the top-five cryptocurrencies using random walk testing procedures which are robust to asymmetries and unobserved smooth structural breaks. Moreover, our study employs two frequencies of cryptocurrency returns, one corresponding to daily returns and the other to weekly returns. Our findings validate the random walk hypothesis for daily series hence validating the weak-form efficiency for daily returns. On the other hand, weekly returns are observed to be stationary processes which is evidence against weak-form efficiency for weekly returns. Overall, our study has important implications for market participants within cryptocurrency markets.
In this paper I document a positive relation between the volatility of liquidity and expected returns. Specifically, I analyze the relationship between the idiosyncratic volatility of market liquidity and the returns of the five largest cryptocurrencies by market capitalization. I find that the correlation between liquidity volatility and returns is overall significantly positive, but highly time-varying. This implies that investors demand a premium for a high variation in liquidity volatility. I furthermore find that the correlation between returns and the level of liquidity is mostly positive, thus, when liquidity is low, expected returns are high. The results corroborates results from other financial markets.
Unlike conventional cryptocurrencies, Islamic ones are new technologies backed by tangible assets and are characterised by their fundamental values. After the COVID-19 outbreak, cryptocurrency responses have shown different behaviour to stock market reactions. However, there is a lack of studies on the efficiency of Islamic and green cryptocurrencies during the pandemic. This paper attempts to analyse the behaviour of three typical families of cryptocurrencies (conventional, Islamic, and green) extracted according to their availability in daily frequencies during COVID-19. For this purpose, their efficiency levels are studied before and after the outbreak by employing multifractal detrended fluctuation analysis (MFDFA) to make the best predictions and strategies. The inefficiency of the cryptocurrencies is assessed through a magnitude of long-memory (MLM) efficiency index, and the impact of COVID-19 on their efficiency is evaluated. The primary results show that HelloGold was the most efficient market before the COVID-19 outbreak and that subsequently Ethereum has been the most efficient. In addition, the findings reveal that the cryptocurrency reactions are not similar and show more resilience in the Ethereum and Litecoin markets than in other cryptocurrency markets. The main contribution of this study is the evaluation of the impact of COVID-19 on the various classes of crypto money. This work has practical implications, as it provides new insights into trading opportunities and market reactions. Moreover, he work has theoretical implications based on its evaluation of three distinct models from different doctrine viewpoints.
We model the dynamics of the cryptocurrency (CC) asset class via a stochastic volatility with correlated jumps (SVCJ) model with rolling-window parameter estimates. By analyzing the time-series of parameters, stylized patterns are observable which are robust to changes of the window size and supported by cluster analysis. During bullish periods, volatility stabilizes at low levels and the size and volatility of jumps in mean decreases. In bearish periods though, volatility increases and takes longer to return to its long-run trend. Furthermore, jumps in mean and jumps in volatility are independent. With the rise of the CC market in 2017, a level shift of the volatility of volatility occurred. All codes are available on Quantlet.com.
Jiahua Xu, Krzysztof Paruch, Simon Cousaert, Yebo Feng
As an integral part of the decentralized finance (DeFi) ecosystem, decentralized exchanges (DEXs) with automated market maker (AMM) protocols have gained massive traction with the recently revived interest in blockchain and distributed ledger technology (DLT) in general. Instead of matching the buy and sell sides, automated market makers (AMMs) employ a peer-to-pool method and determine asset price algorithmically through a so-called conservation function. To facilitate the improvement and development of automated market maker (AMM)-based decentralized exchanges (DEXs), we create the first systematization of knowledge in this area. We first establish a general automated market maker (AMM) framework describing the economics and formalizing the system's state-space representation. We then employ our framework to systematically compare the top automated market maker (AMM) protocols' mechanics, illustrating their conservation functions, as well as slippage and divergence loss functions. We further discuss security and privacy concerns, how they are enabled by automated market maker (AMM)-based decentralized exchanges (DEXs)' inherent properties, and explore mitigating solutions. Finally, we conduct a comprehensive literature review on related work covering both decentralized finance (DeFi) and conventional market microstructure.
Ahmet Faruk Aysan, Asad Ul Islam Khan, Humeyra Topuz, Ahmet Semih Tunalı
This paper explores the applicability of universal cryptocurrency exchange by analyzing crypto exchanges of Binance, Latoken, Kucoin and Qash, which also have their own cryptocurrencies in the crypto market. Results of the recursive Johansen cointegration test proved that even though all of the cryptocurrencies have cointegration among each other, Binance positively disassociated itself from the others after it moved to Malta on 23 March 2018. Based on the daily prices of cryptocurrencies over the period from 6 November 2017 to 10 November 2019, taken from coinmarketcap, we conclude that Binance can be considered as a survival of the fittest among all of the crypto exchanges in this natural experiment.
We investigate the market microstructure of Automated Market Makers (AMMs), the most prominent type of blockchain-based decentralized exchanges. We show that the order execution mechanism yields token value loss for liquidity providers if token exchange rates are volatile. AMMs are adopted only if their token pairs are of high personal use for investors, or the token price movements of the pair are highly correlated. A pricing curve with higher curvature reduces the arbitrage problem but also investors' surplus. Pooling multiple tokens exacerbates the arbitrage problem. We provide statistical support for our main model implications using transaction-level data of AMMs.
Abstract This study examines the portfolio diversification benefits of alternative currency trading in Bitcoin and foreign exchange markets. The following methods are applied for the analysis: the spillover index method of Diebold and Yilmaz (Int J Forecast 28(1): 57–66, 2012. 10.1016/j.ijforecast.2011.02.006 ), the spillover asymmetry measures of Barunik et al. (J Int Money Finance 77: 39–56, 2017. 10.1016/j.jimonfin.2017.06.003 ), and the frequency connectedness method of Barunik and Křehlík (J Financ Econom 16(2): 271–296, 2018. 10.1093/jjfinec/nby001 ). The findings identify the presence of low-level integration and asymmetric volatility spillover as well as a dominant role of short horizon spillover among Bitcoin markets and foreign exchange pairs for six major trading currencies (US dollar, euro, Japanese yen, British pound sterling, Australian dollar, and Canadian dollar). Bitcoin is found to provide significant portfolio diversification benefits for alternative currency foreign exchange portfolios. Alternative currency Bitcoin trading in euro is found to provide the most significant portfolio diversification benefits for foreign exchange portfolios consisting of major trading currencies. The findings of the study regarding spillover dynamics and portfolio diversification capabilities of the Bitcoin market for foreign exchange markets of major trading currencies have significant implications for portfolio diversification and risk minimization.