Automated Market Makers (AMMs) are major centers of matching liquidity supply and demand in Decentralized Finance. Their functioning relies primarily on the presence of liquidity providers (LPs) incentivized to invest their assets into a liquidity pool. However, the prices at which a pooled asset is traded is often more stale than the prices on centralized and more liquid exchanges. This leads to the LPs suffering losses to arbitrage. This problem is addressed by adapting market prices to trader behavior, captured via the classical market microstructure model of Glosten and Milgrom. In this paper, we propose the first optimal Bayesian and the first model-free data-driven algorithm to optimally track the external price of the asset. The notion of optimality that we use enforces a zero-profit condition on the prices of the market maker, hence the name ZeroSwap. This ensures that the market maker balances losses to informed traders with profits from noise traders. The key property of our approach is the ability to estimate the external market price without the need for price oracles or loss oracles. Our theoretical guarantees on the performance of both these algorithms, ensuring the stability and convergence of their price recommendations, are of independent interest in the theory of reinforcement learning. We empirically demonstrate the robustness of our algorithms to changing market conditions.
This study aimed to examine the weak-form efficiency of some of the most capitalised cryptocurrencies. The sample consisted of 24 cryptocurrencies selected out of 30 cryptocurrencies with the highest market capitalisation as of October 19, 2022. Stablecoins were not considered. The study covered the period from January 1, 2018 to August 31, 2022. The results of robust martingale difference hypothesis tests suggest that the examined cryptocurrencies were efficient most of the time. However, their efficiency turned out to be time-varying, which validates the adaptive market hypothesis. No evidence was found for the impact of the coronavirus outbreak and the Russian invasion of Ukraine on the weak-form efficiency of the examined cryptocurrencies. The differences in efficiency between the most efficient cryptocurrencies and the least efficient ones were noticeable, but not large. The results also allowed to observe some slight differences in efficiency between the cryptocurrencies with the largest market cap and cryptocurrencies with the lowest market cap. However, the differences between the two groups were too small to draw any far-reaching conclusions about a positive relationship between the market cap and efficiency. The obtained results also did not allow us to detect any trends in efficiency.
John Kingsley Woode, Peterson Owusu, Anokye M. Adam, Emmanuel Assifuah-Nunoo · 5 authors
The study extends the literature on the nexus between cryptocurrency and uncertainty. This study proxied the cryptocurrencies and global uncertainty, respectively, with the seven most significant and variationally susceptible cryptos and the comprehensive world uncertainty in measuring the crypto-uncertainty nexus over the period (2015–2022) and further employing the quantile regression approach. The OLS model results point to a blend of both significant and insignificant relationship between global uncertainty and cryptocurrencies. These relationships were further examined in quantiles and further accounted for the impact of investor sentiments (VIX) and volatility (OVX), and the results were largely corroborated with the results from the conventional OLS, except for the Bitcoin, Litecoin, and Ripple markets. It was also discovered that the nexus changes across quantiles. The results revealed a blend of strong and weak hedges and safe havens among the selected cryptos against global uncertainty during normal and extreme market conditions. In the face of global turmoil, it was revealed that the average crypto market could serve as a safe haven. Also, the cryptos with an insignificant nexus with global uncertainty were found to be significantly affected by investor sentiment. These findings were further confirmed by the quantile-on-quantile and causality-in-quantile estimations. Given the intense precariousness and lack of hedge and haven capacities within the majority of the cryptocurrencies, it is pertinent for investors to consider the market in general as a means of diversifying their portfolios and reserve the hedge and haven option to the few markets that possess such luxury.
We study the stochastic structure of cryptocurrency rates of returns as compared to stock returns by focusing on the associated cross-sectional distributions. We build two datasets. The first comprises forty-six major cryptocurrencies, and the second includes all the companies listed in the S&P 500. We collect individual data from January 2017 until December 2022. We then apply the Quantal Response Statistical Equilibrium (QRSE) model to recover the cross-sectional frequency distribution of the daily returns of cryptocurrencies and S&P 500 companies. We study the stochastic structure of these two markets and the properties of investors' behavior over bear and bull trends. Finally, we compare the degree of informational efficiency of these two markets.
Quoc Minh Nguyen, Dat Tran, Juho Kanniainen, Alexandros Iosifidis · 5 authors
Many cryptocurrency brokers nowadays offer a va-riety of derivative assets that allow traders to perform hedging or speculation. This paper proposes an effective algorithm based on neural networks to take advantage of these investment products. The proposed algorithm constructs a portfolio that contains a pair of negatively correlated assets. A deep neural network, which outputs the allocation weight of each asset at a time interval, is trained to maximize the Sharpe ratio. A novel loss term is proposed to regulate the network's bias towards a specific asset, thus enforcing the network to learn an allocation strategy that is close to a minimum variance strategy. Extensive experiments were conducted using data collected from Binance spanning 19 months to evaluate the effectiveness of our approach. The backtest results show that the proposed algorithm can produce neural networks that are able to make profits in different market situations.
Wael Hemrit, Noureddine Benlagha, Racha Ben Arous, Mounira Ben Arab
Summary In this paper, we examine the connectedness between volatilities for various non‐fungible tokens (NFTs) and developed stock markets during the period from July 1, 2018, to June 15, 2022. With the use of the time‐varying connectedness methods to explore the volatility interdependences among these assets, we find that there is a significant volatility connectedness during Russia's invasion of Ukraine and COVID‐19 periods. Evidence emerging from this study advocates the inclusion of NFTs in developed stock markets for medium and long time periods only. The results also suggest that UK and Germany stock markets are the predominant market of spillover transmission, whereas the XTZ is the top net recipient/transmitter of volatility connectedness shocks. Moreover, Chinese stock market and ENJ offer more diversification gains than others, and the volatility connectedness from US stock market to NFTs is more pronounced in the long‐term than the short‐term. Our research provides some urgent and prominent insights to help investors and policymakers to be aware that NFTs are important hedge assets that should be added to stock portfolios during periods of geopolitical stability and in the post‐pandemic times.
The growing global fascination with cryptocurrencies has sparked heightened interest, driven by their pronounced market volatility. This particular study endeavors to assess the risk and rewards associated with four prominent cryptocurrencies, while also delving into an examination of their interrelationships and fluctuation patterns. The investigation is based on daily closing prices spanning from January 1, 2017, to June 30, 2022. To unravel the spillover and asymmetrical repercussions of volatility, we employ various models from the GARCH family, most notably the DCC GARCH and EGARCH models. In addition, Granger causality is harnessed to uncover any causal connections among these digital assets. The findings underscore a noteworthy spillover phenomenon between Bitcoin and Ethereum, the two foremost cryptocurrencies boasting the maximum market capitalization. This spillover effect manifests as symmetric volatility impacts, setting them apart from Litecoin and RIPPLE.
Kripto para piyasasının bilhassa son dönemlerde artan popülaritesi, piyasaların etkinliği ve geleceğe dair fiyat hareketlerinin anlaşılmasına yönelik ilgiyi de tetiklemişti. Bu çalışma, en yüksek işlem hacmine sahip 7 kripto para biriminin (Bitcoin (BTC), Binance Coin (BNB), Cardano (ADA), Dogecoin (DOGE), Ethereum (ETH), Tether (USDT) ve Rippel (XRP)) piyasa üzerindeki etkinliğini Fama'nın (1970) etkin piyasalar hipotezi çerçevesinde incelemekte ve bu kripto paraların birim kök ve durağanlık yapılarına odaklanarak, piyasa üzerindeki etkinlik düzeyini anlamak ve gelecekteki fiyat hareketlerine dair bulgular elde etmektir. Bu sayede kripto para piyasalarında etkinliği daha iyi anlamak ve yatırımcılar için daha güvenilir yatırım stratejileri oluşturmak için sağlam bir temel sunmak mümkün hale gelebilmektedir. Araştırmada incelenen 7 kripto paranın fiyat davranışlarını analiz etmek amacıyla birbirine göre farklı avantajları ve bulunan farklı panel birim kök testlerinden faydalanılarak güvenilir ve sağlam sonuçlara ulaşma ihtimali arttırılmıştır. Analitik tekniklerden elde edilen bulgular araştırmanın anakütlesini oluşturan 7 kripto paranın rassal yürüş sürecine tabi olmamak (durağan bir sürece karşılık gelmek) suretiyle, zayıf formda etkin olmadığını göstermektedir. Kripto para piyasalarındaki etkinliğin anlaşılması, yatırımcıların daha bilinçli kararlar almasına yardımcı olacak ve finansal riskleri daha etkin bir şekilde yönetmelerini sağlayacaktır.
Algorithmic trading enables the execution of orders using a set of rules determined by a computer program. Orders are submitted based on an asset’s expected price in the future, an approach well suited for high-volatility markets, such as those trading in cryptocurrencies. The goal of this study is to find a reliable and profitable model to predict the future direction of a crypto asset’s price based on publicly available historical data. We first develop a novel labeling scheme and map this problem into a Machine Learning classification problem. The model is then validated on three major cryptocurrencies through an extensive backtest over a bull, bear and flat market. Finally, the contribution of each feature to the classification output is analyzed.
This paper investigates whether the price of cryptocurrency is determined by the US dollar index, the price of investment assets such gold and oil, and the implied volatility of the KOSPI. Overall, the returns on cryptocurrencies are best predicted by the trading volume of the cryptocurrency both in-sample and out-of-sample. The estimates of gold and the dollar index are negative in the return prediction, though they are not significant. The dollar index, gold, and the cryptocurrencies seem to share characteristics which hedging instruments have in common. When investors take notice of the imminent market risks, they increase the demand for one of these assets and thereby increase the returns on the asset. The most notable result in the out-of-sample predictability is the predictability of the returns on value-weighted portfolio by gold. The empirical results show that the restricted model fails to encompass the unrestricted model. Therefore, the unrestricted model is significant in improving out-of-sample predictability of the portfolio returns using gold. From the empirical analyses, we can conclude that in-sample predictability cannot guarantee out-of-sample predictability and vice versa. This may shed light on the disparate results between in-sample and out-of-sample predictability in a large body of previous literature.
Using daily closing price observations between November 2017 and February 2023, this paper documents how the shocks of a cryptocurrency ETF resonate with ETFs representing traditional asset classes in terms of price and volatility. We find price transmission from the cryptocurrency ETF into the ETFs of several currencies, small-cap equities, and inflation. Risk propagation from the cryptocurrency ETF flows toward ETFs constituted of equities of various sizes, oil prices, high-yield corporate bonds, and inflation. There is scant evidence of transmission from ETFs with underlying conventional assets into the cryptocurrency ETF. The findings bear implications for low-cost risk management strategies.
Given the volatile nature of cryptocurrencies, accurately forecasting cryptocurrency volatility and understanding its determinants are crucial. This paper applies machine learning (ML) techniques to forecast cryptocurrency volatility using internal determinants (e.g., lagged volatility, previous trading information) and external determinants (e.g., technology, financial, and policy uncertainty factors). Both Random Forest and Long Short-Term Memory (LSTM) networks significantly outperform traditional volatility models such as GARCH. Furthermore, we explore two optimization models—Genetic Algorithm and Artificial Bee Colony—to tune the hyper-parameters of LSTM. Our results indicate that the application of these optimization models substantially improves forecasting performance. Moreover, using SHapley Additive exPlanations, an interpretation method, we find that internal determinants play the most important roles in volatility forecasts. Finally, our results show that models trained with determinants from multiple cryptocurrencies outperform those trained with determinants from a single cryptocurrency, suggesting that considering a broader range of determinants can capture the complex dynamics in the cryptocurrency market.
This paper aims to study the blockchain in the field of financial ecology as the carrier, optimize the consensus mechanism, and use intelligent consulting as an analytical means to provide investors with an objective, low-cost asset allocation portfolio. This article begins with an introduction to the features of blockchain decentralization and tamper-proof execution of algorithms, how proof-of-work works, and how tokens can improve welfare and reduce user base volatility. The paper then introduces how robo-advisors work and how they develop. Finally, this paper reviews existing research models on robo-advisors, from the traditional mean-variance model based on Markowitz to the jump-diffusion, regime-switching model, and the Pi portfolio management model that does not require quantifying risk preference coefficients, which this paper discusses and seeks to explore the advantages and limitations between the different models. Based on the existing research gaps, the directions that digital finance can expand in the future are discussed.
Purpose This review aims to provide an overview of research from different academic disciplines to chart some of the key developments in retail cryptocurrency trading against the backdrop of the wider trading landscape, and how it has evolved in recent years. The purpose of this review is to provide researchers with a broad perspective to highlight the complex range of factors that drive cryptocurrency trading among retail investors. Design/methodology/approach Peer-reviewed literature from the social sciences, economics, marketing and branding disciplines is synthesised to explicate influential factors among retail cryptocurrency investors. Findings Online retail trading communities can create narratives that ascribe value to cryptocurrencies leading to consumer herding behaviours. The principles that underpin emotional branding and Fear of Missing Out can promote trading behaviour driven by heuristic processing and cognitive biases. Concurrently, the tenets of controversial marketing and the anti-establishment nature of Bitcoin and other cryptocurrencies serve to bolster in-group out-group categorisations fostering continued investment and market volatility. Consequently, Bitcoin and cryptocurrency trading more broadly offer a powerful combination of excitement from risk-taking akin to gambling buffered by the sanctity of social inclusion. Originality/value A broader, unique perspective on retail cryptocurrency trading which assists in better understanding the complexities that underpin its appeal to retail investors.
This study explores the stylized facts, volatility clustering, other highly irregular behaviour, and risk measures of cryptocurrencies’ returns. By analysing bitcoin, ripple, and ethereum daily data we establish evidence of strong dependencies among analysed cryptocurrencies. This paper provides new insights about cryptocurrency behaviour and the main measures of risk and detailed comparative analysis with tech-stocks. Comprehensive research on stylized facts confirmed high risk for both cryptocurrencies and tech-stocks with cryptocurrencies being even riskier. Empirical research findings are useful in developing dependence and risk strategies for investment and hedging purposes, especially during more volatile periods in the markets as there was confirmed existence of volatility clusters when high volatility periods are followed by low volatility periods. Sensitivity analysis and measures of Value-at-Risk (VaR) and Expected Shortfall (ES) show the amount of losses investors can expect in the worst case scenario. Our results confirm the existence of predictability, volatility clustering, and possibilities for arbitrage opportunities. Findings could be beneficial for investors and policymakers as well as for scientific purposes as findings give us a better understanding of the behaviour of cryptocurrencies.
As cryptocurrency evolved, new financial instruments, such as lending and borrowing protocols, currency exchanges, fungible and non-fungible tokens (NFT), staking and mining protocols have emerged. A financial ecosystem built on top of a blockchain is supposed to be fair and transparent for each participating actor. Yet, there are sophisticated actors who turn their domain knowledge and market inefficiencies to their strategic advantage; thus extracting value from trades not accessible to others. This situation is further exacerbated by the fact that blockchain-based markets and decentralized finance (DeFi) instruments are mostly unregulated. Though a large body of work has already studied the unfairness of different aspects of DeFi and cryptocurrency trading, the economic intricacies of non-fungible token (NFT) trades necessitate further analysis and academic scrutiny. The trading volume of NFTs has skyrocketed in recent years. A single NFT trade worth over a million US dollars, or marketplaces making billions in revenue is not uncommon nowadays. While previous research indicated the presence of wrongdoings in the NFT market, to our knowledge, we are the first to study predatory trading practices, what we call opportunistic trading, in depth. Opportunistic traders are sophisticated actors who employ automated, high-frequency NFT trading strategies, which, oftentimes, are malicious, deceptive, or, at the very least, unfair. Such attackers weaponize their advanced technical knowledge and superior understanding of DeFi protocols to disrupt trades of unsuspecting users, and collect profits from economic situations that are inaccessible to ordinary users, in a "supposedly" fair market. In this paper, we explore three such broad classes of opportunistic strategies aiming to realize three distinct trading objectives, viz., acquire, instant profit generation, and loss minimization.
This study demonstrates the significant impact of market sentiment, derived from social media, on the daily price prediction of cryptocurrencies in both bull and bear markets. Through the analysis of approximately 567 thousand tweets related to twelve specific cryptocurrencies, we incorporate the sentiment extracted from these tweets along with daily price data into our prediction models. We test various algorithms, including ordinary least squares regression, long short-term memory network and neural hierarchical interpolation for time series forecasting (NHITS). All models show better performance once the sentiment is incorporated into the training data. Beyond merely assessing prediction error, we scrutinise the model performances in a practical setting by applying them to a basic trading algorithm managing three distinct portfolios: established tokens, emerging tokens, and meme tokens. While NHITS emerged as the top-performing model in terms of prediction error, its ability to generate returns is not as compelling.
Esther Cabezas-Rivas, Felipe Sánchez, Isaac Tormo-Xaixo
The aim of this paper is to analyse the Bitcoin in order to shed some light on its nature and behaviour. We select 9 cryptocurrencies that account for almost 75\% of total market capitalisation and compare their evolution with that of a wide variety of traditional assets: commodities with spot and futures contracts, treasury bonds, stock indices, growth and value stocks. Fractal geometry will be applied to carry out a careful statistical analysis of the performance of the Bitcoin returns. As a main conclusion, we have detected a high degree of persistence in its prices, which decreases the efficiency but increases its predictability. Moreover, we observe that the underlying technology influences price dynamics, with fully decentralised cryptocurrencies being the only ones to exhibit self-similarity features at any time scale.
With the rapid development of cryptocurrencies, the volatility characteristics of their yields have received more and more attention. At the same time, many empirical studies show that the GARCH family model is more effective in describing the volatility of financial time series. Firstly, this paper briefly introduces the research background of cryptocurrency and the research method using GARCH model. Next, the daily rate of return is calculated and descriptive statistical analysis is carried out on the collected closing price data of cryptocurrency, and on this basis, the GARCH model is constructed for empirical test to explore the volatility characteristics of its rate of return. Then the corresponding research conclusions and relevant policy recommendations are given.
Abna Ajeesh, Lekshmi Prakash, Mohammad Ali Moni, V. Sreeraj
This study used the CMC 200 Index as a cryptocurrency market benchmark to examine complex volatility patterns of cryptocurrencies. The growing interest in cryptocurrencies and the necessity to analyse their market dynamics, especially in the face of external inputs like news, prompted the study. The study examined market responses and causes to diverse stimuli using rigorous analytical models including GARCH, EGARCH, FIGARCH, and News Impact Curve. The asymmetricvolatility or “leverage effect” showed that negative events or news have a greater impact on market volatility than positive developments of similar magnitude. Symmetric volatility indicated large price shifts regardless of news direction. The left-skewed news effect curve emphasises this asymmetric volatility, demonstrating that negative news has a greater impact on market dynamics. The curve’s leftward skew shows the market’s increased susceptibility to pessimism. This suggests that negative news might undermine investor confidence in the crypto market more than favourable news. Beyond these initial reactions, the research revealed a “long memory” in market volatility, suggesting that prior shocks continue to affect its volatility over time. These studies emphasise the importance of investor sentiment in crypto market. Investors in this volatile market need honest communication and strong risk management due to the leverage impact and prior experience.
Relatively narcissistic people are attracted to cryptocurrencies, though it is unclear whether they are differentially attracted to cryptocurrencies over other investments. Furthermore, theoretically, only narcissistic admiration, rather than narcissistic rivalry, would be expected to associate with attitudes toward cryptocurrencies. Intelligence and financial literacy are also proposed individual difference predictors of attitudes toward investments. Consequently, we administered measures of narcissistic admiration and rivalry, a financial literacy test, and a battery of intelligence tests to a sample of young adults (N = 372). Based on a structural equation model, narcissistic admiration and narcissistic rivalry differentially predicted attitudes toward cryptocurrencies (admiration, positively; rivalry, negatively), but both failed to associate significantly with attitudes toward stocks. Furthermore, financial literacy was a unique, positive predictor of attitudes toward stocks, whereas intelligence was a unique, negative predictor of attitudes toward cryptocurrencies. Our findings support the notion that narcissism is differentially associated with attitudes toward cryptocurrencies, though only narcissistic admiration (positively), consistent with the hypersensitivities to reward theory. Finally, higher levels of intelligence, controlling for financial literacy and narcissism, associated negatively with attitudes toward cryptocurrencies, perhaps due to the influence of scepticism.
Muhammad Mahmudul Karim, Md Hakim Ali, Larisa Yarovaya, Md Hamid Uddin · 5 authors
Implied volatility has consistently demonstrated its reliability as a superior estimator of the expected short-term volatility of underlying assets. In this study, we employ the newly constructed robust model-free implied volatility (MFIV) indices for Bitcoin and Ethereum (BitVol and EthVol) to explore the asymmetric return-volatility relationship of these cryptocurrencies through the lens of behavioral finance theories. Utilizing the asymmetric quantile regression model (QRM) and the Non-linear ARDL (NARDL) approach, our results reveal a notable difference from equities. Both positive and negative return shocks in the cryptocurrency market lead to an increase in volatility. However, during high volatility regimes, positive (negative) return shocks exert a more substantial impact on positive innovations of volatility for Bitcoin (Ethereum) compared to negative (positive) return shocks. The degree of asymmetry steadily intensifies as we progress from medium to uppermost quantiles of the volatility distribution. These observed phenomena can be attributed to behavioral aspects among market participants, including noise trading, behavioral biases, and fear of missing out (FOMO). Our findings hold significant implications for various aspects of cryptocurrency trading, portfolio hedging strategies, volatility derivatives pricing, and risk management.