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.
Purpose This study aims to investigate the possibilities of cryptocurrencies as hedges and diversifiers in the Indian stock market before and during financial crisis due to the pandemic and the Russia–Ukraine war. Design/methodology/approach Researchers have used daily data on cryptocurrencies and Indian stock prices from March 10, 2015 to August 26, 2022. The researchers have used the dynamic conditional correlations (DCC)-GARCH model to determine the volatility spillover and dynamic correlation between stocks and digital currencies. Further, researchers have explored hedge ratio, portfolio weight and hedging effectiveness using the estimates of the DCC-GARCH model. Findings The findings indicate a negative conditional correlation between equities and cryptocurrencies before the crisis and a positive conditional correlation except for Tether during the crisis. Which implies that cryptocurrencies serve as a hedging asset in the stock market before a crisis but are not more than a diversifier during the crisis, except for Tether. Notably, Tether serves as a safe haven during times of crisis. Finally, the study suggests that Bitcoin, Ethereum, Binance Coin and Ripple are the most effective diversifiers for Indian stocks during the crisis. Originality/value This study makes several contributions to the existing literature. First, it compares the hedge and diversification roles of cryptocurrencies in the Indian stock market before and during crisis. Second, the study findings provide insights on risk hedging and can serve as a guide for investors. Third, it may help rational investors avoid underestimating risk while constructing portfolios, particularly in times of financial turmoil.
The paper presents a novel approach to developing a pair trading strategy for cryptocurrencies by employing a customized Rainbow DQN and an image encoding technique. This method transforms candlestick features of Bitcoin (BTC) and Ethereum (ETH) into images, which are then input into a Convolutional Neural Network (CNN) for feature extraction before being fed into the Rainbow DQN model for making trading decisions. Although the Rainbow DQN strategy outperforms RSI and correlation strategies, none of them are profitable after factoring in transaction costs.
The proliferation of cryptocurrencies has shed a whole new light on the way financial markets operate. It is not just the emergence of a new type of financial instrument in payment habits, but much more than that. It fundamentally changes the way we have always thought about money, it changes the way the traditional financial and banking system works, it redefines financial supervision and it gives anonymity to market players. We are talking about a financial system that does not see the need for public economic policy, central banks or any other supervisory body. The value of reform and novelty cannot be questioned here, but this novelty has not been welcomed by all to the same extent. Crypto-assets and crypto-markets have been a rather divisive issue. Some people are confident in the new instruments, while others are distrustful and see them as a sham. A system without supervision and control may, it is true, create some mistrust among economic operators, but cryptocurrencies have spread very rapidly around the world and there are now billions of USD invested in cryptoassets. There are many advantages that attract investors to these assets, even though they are considered to be one of the most volatile assets. Nothing is more proof of this than the fact that governments and central and commercial banks are increasingly turning to cryptocurrencies. The aim of this paper is to provide a literature review of the main reasons for the popularity of cryptocurrencies and to present concrete examples, through primary research, that show that the crypto market is not only accessible to individuals or businesses, but also to governments, banks and investment companies. The study is a novelty compared to previous literature on cryptocurrencies in that it includes the state as a crypto market player and provides illustrative and instructive examples.
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.
This article aims to investigate the presence of herding behavior in artificial intelligence (AI)–themed cryptocurrencies following the launch of ChatGPT. The authors analyze daily data from major AI-themed cryptocurrencies between November 2022 and February 2023. This study finds evidence of irrationality among investors in this market segment who tend to imitate others’ decisions regardless of their own beliefs during down events. The authors connect this finding to the herding theory in financial economics and highlight the implications for investors and policymakers. This article contributes to the literature on the impact of AI on financial markets and suggests the need for further research in this area. Finally, this study provides important policy implications, as it could help investors better understand the risks associated with this emerging asset class.
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.
Decentralized exchanges (DEXs) are an essential component of the nascent decentralized finance (DeFi) ecosystem. The most common DEXs are so-called automated market makers (AMMs): smart contracts that pool liquidity and process trades as atomic swaps of tokens. AMMs price transactions with a deterministic liquidity invariance rule that only uses the AMM’s token deposits as inputs and that has no precedent in traditional finance. Yet, in the context of transparent and open blockchain operations, any liquidity invariance pricing function allows so-called sandwich attacks (akin to front running) that increase the cost of trading and threaten the long-term viability of the DeFi ecosystem. Invariance pricing is also not regret free. Linear pricing rules have similar problems except for uniform pricing, which has regret-free prices and limits sandwich attack profits but which invites excessive order splitting. Comparing trading costs using a model of liquidity provision, constant product pricing is often cheaper except when the variance of the underlying asset is small or when the order is large. This paper was accepted by Will Cong, Special Section of Management Science: Blockchains and Crypto Economics. Funding: A. Park received financial support from the Global Risk Institute and the Social Sciences and Humanities Research Council of Canada [Grant 435-2017-0647]. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2021.02802 .
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.