This article employs a Markovian regime-switching autoregressive approach to examine triangular arbitrage across forex and cryptocurrency markets during the coronavirus disease 2019 crisis. The findings suggest the following: (1) profitable triangular arbitrage tends to occur in the turbulent period during the crisis, significantly outperforming cryptocurrency investments; and (2) the persistent profitability of triangular arbitrage ensues from strong memory of high returns, low risk, and shock response to global quantitative monetary easing policy. Regulatory authorities should consolidate cryptocurrency supervision systems and establish cross-border coordination mechanisms to stabilize exchange rates and enhance market efficiency.
May 19, 2021¡2021 18th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON)
Cryptocurrencies are in great demand in society. The number of beginners on the cryptocurrency market increases every day. Most of them only focus on a high return on the cryptocurrency investment. However, they do not aware of the high risk from its volatility. Trading is the most popular way to invest with digital currencies due to its potential returns. The cryptocurrency exchange requires a high learning curve for beginners. There exists a strategy to minimize risk in the cryptocurrency investment called "arbitrage". It exploits the market inefficiency to discover a profitable method. This strategy has been investigated in traditional markets like stock markets. Furthermore, some researchers studied arbitrage on general exchange platforms that are operated by companies. However, to the best of our knowledge, there is no research works on arbitrage in the Decentralized Exchange (DEX). The DEX recently emerged with a huge amount of trading volume and profits. The high profit is along with the high risk. Thus, risk-reducing in the cryptocurrency investment is a crucial topic. We demonstrate arbitrage strategy on DEX. The strategy used in this work is to invest an amount of Ether and receive a higher return in each row. The market inefficiency on the current DEX platforms is searched by using an automatic method. We further investigate important factors, which should be considered for profit-maximizing in DEX arbitrage.
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.
In our model, a venture seeks capital through an initial coin offering (ICO). The ICO enables the venture to collect demand information from decentralized investors. The venture makes a tradeoff between ensuring the projectâs success and forecasting market demand through token size and token price. We find that the higher the demand uncertainty and production cost, the greater the ventureâs incentive to learn from its investors. To identify when the venture has an incentive to raise funds through an ICO, we compare it with traditional bank financing and analyze the ventureâs preference between the two financing options. The results show that when demand uncertainty is high, an ICO can provide both financing and information benefits, whereas when demand uncertainty is low, although the venture does not collect information through an ICO, it can still get financing benefit. Only when demand uncertainty is intermediate, the venture prefers bank financing. In addition, we find that although the revenue sharing effect of the ICO results in an underinvestment issue, it can also alleviate the loss caused by the increased cost of production. Therefore, with high information accuracy, as production cost goes up, ICOs become more attractive than bank financing for the venture.
Abstract This paper adopts the fractional cointegrated vector autoregressive (FCVAR) model to examine highâfrequency 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 Covidâ19 pandemic, the bitcoin price discovery leadership has switched to the spot market. Moreover, we find that the bitcoin futures market follows a longârun 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 techniquesâincluding machine learning approachesâfor 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 Bitcoinâs 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 frameworksâsuch as minimum variance, equal risk contribution, and risk parityâperform 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 â Evidence from the U.S. Election Cycle, investigates Bitcoinâs 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 uncertaintyâeconomic versus institutionalâhighlighting that Bitcoinâs 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 â 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 assetsâas 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 worldâs 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.
Over the last years, cryptocurrencies have gained popularity as a means of exchange, but mostly as an investment asset that can yield important earnings. Accurate cryptocurrency price prediction is the holy grail of investors, yet the task is extremely complex and tedious since cryptocurrencies exhibit high volatility and steep fluctuations compared to fiat money, while they depend on a plethora of factors related to the blockchain network, market trends, social popularity and the prices of other (crypto)currencies. Thus, simple statistical methods are not able to capture the complexity of cryptocurrency exchange rate, forcing researchers to turn to advanced machine learning techniques. In this work, we present a methodology for building deep learning models to forecast the price of cryptocurrencies and apply it to the prediction of Ether price, resulting in short-and long-term forecasts that achieve an accuracy of up to 84.2%.
Decentralized cryptocurrency exchange protocols such as Uniswap, Curve and other types of Automated Market Makers (AMMs) maintain a liquidity pool (LP) of two or more assets constrained to maintain at all times a mathematical relation to each other, defined by a given function or curve. We propose a dynamic AMM approach where input from a market price oracle is used to modify the mathematical relationship between the assets so that the pool price continuously and automatically adjusts to be identical to the market price. This approach eliminates arbitrage opportunities.
In view of explosive trends and excessive trades in the cryptocurrency markets, this paper contributes to the existing literature by bringing in the limelight the effect of liquidity on the herding behavior in the cryptocurrency market. Results from a first applied herding model including contemporaneous and lagged squared market returns demonstrated that market-wide herding exists within falling markets. The incorporation of liquidity highlights further evidences on herding behavior across cryptocurrencies during high and low liquid days, which varies across percentiles. Our findings bring handy implications for topics of portfolio and risk management, as well as regulation.
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 productsâboth 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.