This paper aims to compare the empirical performance of two approaches in detecting structural breaks and outliers due to the significant frequent price changes seen in cryptocurrencies.The two approaches are indicator saturation (IS) and Bai and Perron (BP).The cryptocurrency data employed in this study are Bitcoin and Ethereum.In comparing the performance of the two approaches, this study performed multiple empirical comparisons using various significant levels, different data frequencies, as well as the original and log series (price).The findings showed that the prices contained structural breaks and outliers and that the IS approach performed significantly better than the BP test in terms of the identified structural breaks as well as outliers across different settings.The contribution of this study is providing empirical comparisons between IS and BP approaches using cryptocurrency data.These findings are important to the potential stakeholders, in particular, for quality control in industries, for setting price targets, and for confirming trading signals to reduce potential losses.
Despite widespread skepticism linked to cryptocurrencies, they are constantly gaining the interest of scholars, investors, media, and regulators. Recognizing the importance that portfolio risk maintains for crypto participants, this study attempts to shed light on this issue. We investigate the risk-return tradeoffs of the most tradable cryptocurrencies based on portfolio diversification techniques. Three different crypto portfolios containing a diverse number of cryptocurrencies were created to analyze the diversification risk from a historical perspective. Data concerning daily prices and their trade volume was collected from the Coin Market Cap database and covered the period from 1 January 2016 to 31 December 2022. The results regarding the risk-reward tradeoff stand in line with the portfolio theory, where higher expected returns offset higher risk. On average, the portfolio composed of 10 cryptocurrencies offers better optimization than the one with five, as it generates the same returns with lower risk. The year 2018 reflects the maximum diversification benefits in the three portfolios, corresponding to the period when cryptocurrencies gained massive popularity. From the managerial perspective, results inform crypto and institutional investors of the possible diversification benefits of the 15 most traded cryptocurrencies.
Disruption and shutdown of exchanges frequently happen in the cryptocurrency market, though its potential impacts are relatively under-investigated due to several empirical challenges. This study employs 20-h of service interruption on October 15th at Upbit , the dominant cryptocurrency exchange in Korea, as an exogenous shock to examine the effect of unexpected service interruption at the exchange on cryptocurrency market. Event study estimation using price data from Binance, the largest cryptocurrency exchange globally, shows the sharp and negative reactions to cryptocurrencies mostly traded at Upbit . Major currencies such as Bitcoin and Ethereum also presented limited reactions, implying that service interruption could be interpreted as vulnerability of overall cryptocurrencies.
Marcin Wątorek, Maria Skupień, Jarosław Kwapień, Stanisław Drożdż
This paper investigates the temporal patterns of activity in the cryptocurrency market with a focus on Bitcoin, Ethereum, Dogecoin, and WINkLink from January 2020 to December 2022. Market activity measures - logarithmic returns, volume, and transaction number, sampled every 10 seconds, were divided into intraday and intraweek periods and then further decomposed into recurring and noise components via correlation matrix formalism. The key findings include the distinctive market behavior from traditional stock markets due to the nonexistence of trade opening and closing. This was manifest in three enhanced-activity phases aligning with Asian, European, and U.S. trading sessions. An intriguing pattern of activity surge in 15-minute intervals, particularly at full hours, was also noticed, implying the potential role of algorithmic trading. Most notably, recurring bursts of activity in bitcoin and ether were identified to coincide with the release times of significant U.S. macroeconomic reports such as Nonfarm payrolls, Consumer Price Index data, and Federal Reserve statements. The most correlated daily patterns of activity occurred in 2022, possibly reflecting the documented correlations with U.S. stock indices in the same period. Factors that are external to the inner market dynamics are found to be responsible for the repeatable components of the market dynamics, while the internal factors appear to be substantially random, which manifests itself in a good agreement between the empirical eigenvalue distributions in their bulk and the random matrix theory predictions expressed by the Marchenko-Pastur distribution. The findings reported support the growing integration of cryptocurrencies into the global financial markets.
This study aims to model the volatility features of Bitcoin, Ethereum, and Ripple, which are the cryptocurrencies with the greatest volumes that have come to the agenda since the global crisis, and to determine the presence and dates of price bubbles.After running the ADF and Ng-Perron unit root tests, the EGARCH model was analyzed as the best for Bitcoin and TGARCH for the Ethereum and Ripple. According to the obtained results, negative coefficients for Bitcoin imply that negative shocks will increase volatility more than positive shocks. This means that a leverage effect is present. No leverage effect was reached for Ethereum or Ripple, and positive shocks are understood to increase volatility for them compared to negative shocks. In addition, continuous speculative bubble pricing occurred for all three cryptocurrencies, with much higher bubble prices being understood to have occurred with Ethereum and Bitcoin compared to Ripple.
Purpose — This study aims to examine Islamic cryptocurrencies and their dependency on foreign exchange markets in vine copula architecture (CD-Vine) and provide a framework for detecting complex dependence structures, risk management implications, and hedging effectiveness. Design/Methodology/Approach — This study used gold-backed cryptocurrencies and three fiat currencies. The vine copula approach was preferred because it applies several distributions and estimates complex dependencies. Hedging effectiveness was measured by constructing simulation-based portfolios optimised with DCC-t-Copula. Benford’s law and realized variance were used to determine the stability of Islamic cryptocurrencies. Findings — According to C-Vine and D-Vine copula models, paper money has a weak tail dependence with gold-backed cryptocurrencies. Only OneGram coin, whose volatility matched the risk of Bitcoin, showed zero irregularities in volume trading. The findings were robust to different estimations based on Minimum Spanning Tree and Dendrogram. Originality/Value — This is the first study to examine Islamic cryptocurrencies’ stability and the significance of hedging effectiveness on gold-backed cryptocurrencies under a copula-based approach. Research Limitations — The study did not apply time-varying vine copula. Practical Implications — The risk management perspective shows insignificant hedge effectiveness in the portfolio of fiat and gold-backed cryptocurrencies.
Abstract Volatility of Bitcoin has a long memory, we modeled such a character using FIGARCH processes, afteward we went on for pricing Futures and Options, the price of Futures depends on many factors in the market, we have proposed a model for futures contracts which links their price to spot price and volatility, after calibrating our model the result was consistent with market values, the pandemic of Covid which started earlier in 2020 after hitting the district of Wuhan just before; had not really an effect on derivatives markets until july 2021 when the market started a downward trend. We price Options using a sample of volatilities that we consider determinstic for a matter of calculous. We finally compare our model for Futures to the same model but with a constant volatility. JEL Classification. G13
Weihao Han, David Newton, Emmanouil Platanakis, Charles Sutcliffe · 5 authors
Abstract Cryptocurrency returns are highly nonnormal, casting doubt on the standard performance metrics. We apply almost stochastic dominance, which does not require any assumption about the return distribution or degree of risk aversion. From 29 long–short cryptocurrency factor portfolios, we find eight that dominate our four benchmarks. Their returns cannot be fully explained by the three‐factor coin model of Liu et al. So we develop a new three‐factor model where momentum is replaced by a mispricing factor based on size and risk‐adjusted momentum, which significantly improves pricing performance.
Nader Naifar, Sohale Altamimi, Fatimah Alshahrani, Mohammed Alhashim
This paper aims to investigate the impact of global financial, economic, and gold price uncertainty indices (VIX, EPU, and GVZ) and investor sentiment based on media coverage news on the returns of Bitcoin and Ethereum during the COVID-19 pandemic. We adopt an asymmetric framework based on the Quantile-on-Quantile approach, which examines the quantiles of the cryptocurrency returns, investor sentiment, and the various uncertainties indicators. The empirical findings suggest that the COVID-19 pandemic has significantly impacted cryptocurrency returns. Specifically, (i) the results demonstrate the predictive power of Economic Policy Uncertainty (EPU) during this period, as evidenced by a strong negative association between EPU and cryptocurrency returns across all quantiles; ( ii ) the correlation between cryptocurrency returns and the VIX index was negative but weak, across various quantile combinations of Ethereum and Bitcoin returns; ( iii ) an increase in COVID-19 news negatively affected Bitcoin returns across all quantiles; ( iv ) Bitcoin and Ethereum cannot be relied upon as effective hedging tools against global financial and economic uncertainty during the COVID-19 pandemic. Studying the behavior of cryptocurrency during uncertainty like pandemics is extremely important because it provides investors with insights on diversifying their portfolios and hedging their risks.
Abstract The paper examines market co‐movement between pairs of financial assets in the time‐frequency domain. Recent finance literature confirms the integration of cryptocurrencies and financial assets, which may bring more investments with the possibility of surplus liquidity in the cryptocurrency segment, leading to financial instability. The novelty of this paper is examining the integration of cryptocurrencies and the indices of equity, sustainability, renewable energy, and crude oil for the daily observations from 2015 to 2021 by using the wavelet coherency method. The empirical results signify no integration in the short‐term scales and grow stronger in the medium‐term scales, especially during the COVID‐19 period, and further exhibit weaker heterogeneous associations in the long‐term scales. However, the sustainability, clean energy indices follow similar dynamics of the equity market and crypto pairs. In contrast, the global crude oil index showcases the minor integration with cryptocurrencies compared with other traditional asset classes. Hence, the cryptocurrency market fails to confirm the safe haven features, especially during the COVID‐19 periods (Medium‐term), which facilitate the domestic and international investors expecting to hedge their price risk in equity markets using cryptocurrencies may have to look for short‐term. The lead–lag heterogeneous effects of the asset‐pairs may pave arbitrage opportunities for investors.
Cryptocurrencies have gained popularity and are increasingly used in the global financial system, despite their volatile nature. They have become an attractive financial instrument for individuals and corporations due to their potentials for high returns, decentralized nature, and exemption from strict government regulations. This study aims to investigate how cryptocurrency volatility affects the performance of companies listed on the Nigerian Exchange Limited (NGX). The study uses an ex post facto research design and the GARCH (1,1) model. Weekly data on Bitcoin and Ethereum were obtained from www.ng.investing.com and used to construct a cryptocurrency composite index with principal component analysis (PCA). The All-Share Index data were extracted from the Security and Exchange Commission (SEC) statistical bulletin between January 2017 and December 2021. The result of the mean equation shows that cryptocurrency trading in Nigeria responds more to positive sentiment and good news than bad news, while the variance equation reveals that current conditional volatility of cryptocurrencies and companies' performance is influenced by their previous shocks and past volatility conditions. The study also found evidence of volatility clustering in companies’ performance on the NGX. Therefore, investors are advised to exercise caution in an expanding cryptocurrency market, while regulators and policymakers should use relevant indicators to avoid contagion risk that could spread to the stock market. This paper is significant and relevant to achieving the Nigerian government's plan to introduce an official virtual currency.
The Non-Fungible-Token (NFT) market has experienced explosive growth in recent years. According to DappRadar, the total transaction volume on OpenSea, the largest NFT marketplace, reached 34.7 billion dollars in February 2023. However, the NFT market is mostly unregulated and there are significant concerns about money laundering, fraud and wash trading. The lack of industry-wide regulations, and the fact that amateur traders and retail investors comprise a significant fraction of the NFT market, make this market particularly vulnerable to fraudulent activities. Therefore it is essential to investigate and highlight the relevant risks involved in NFT trading. In this paper, we attempted to uncover common fraudulent behaviors such as wash trading that could mislead other traders. Using market data, we designed quantitative features from the network, monetary, and temporal perspectives that were fed into K-means clustering unsupervised learning algorithm to sort traders into groups. Lastly, we discussed the clustering results' significance and how regulations can reduce undesired behaviors. Our work can potentially help regulators narrow down their search space for bad actors in the market as well as provide insights for amateur traders to protect themselves from unforeseen frauds.
This study explores whether Islamic equities offer portfolio diversification benefits to cryptocurrency investors. It employs the Continuous Wavelet Transform model to examine the nature of coherence between major cryptocurrency asset classes and major Asian Islamic equity markets on different investment horizons. We consider a range of Islamic equity indices for multiple countries and a basket of three prominent cryptocurrencies: Bitcoin, Ethereum and Ripple. Findings suggest that Asian Islamic equities offer portfolio diversification opportunities. Our findings also imply that Asian Islamic equities are not efficient and are prone to short-term speculative activities.
Price feeds of securities is a critical component for many financial services, allowing for collateral liquidation, margin trading, derivative pricing and more. With the advent of blockchain technology, value in reporting accurate prices without a third party has become apparent. There have been many attempts at trying to calculate prices without a third party, in which each of these attempts have resulted in being exploited by an exploiter artificially inflating the price. The industry has then shifted to a more centralized design, fetching price data from multiple centralized sources and then applying statistical methods to reach a consensus price. Even though this strategy is secure compared to reading from a single source, enough number of sources need to report to be able to apply statistical methods. As more sources participate in reporting the price, the feed gets more secure with the slowest feed becoming the bottleneck for query response time, introducing a tradeoff between security and speed. This paper provides the design and implementation details of a novel method to algorithmically compute security prices in a way that artificially inflating targeted pools has no effect on the reported price of the queried asset. We hypothesize that the proposed algorithm can report accurate prices given a set of possibly dishonest sources.
Yeguang Chi, Wenyan Hao, Jiangdong Hu, Zhenkai Ran
Abstract We investigate the cross‐section asset‐pricing patterns of major cryptocurrencies from 2017 to 2021. We show that the basis, momentum, and basis–momentum factors earn statistically significant excess returns, a result consistent with the findings reported in the commodity futures literature. The basis is the strongest signal predicting cross‐sectional differences in cryptocurrency futures returns; the momentum‐induced risk premium is not statistically powerful, whereas the basis momentum‐induced risk premium disappears when accounting for the basis‐induced risk premium. Daily factor returns are statistically much stronger than weekly factor returns. Monthly factor returns are nonsignificant.
Recently the disposition of the number of financial companies to encompass cryptocurrencies in their portfolios has speeded up. Cryptocurrencies are the first pure digital assets to be included by asset directors. Although they share many things in common with more traditional assets, they have their peculiar characters and their behavior as an asset is still in the continuous phase of being understood. It is therefore significant to sum up the available research publication and finding on cryptocurrency marketing, consisting trading avenues, trading impulses, trading techniques, research and management of risk. This publication highlights an intensive survey of cryptocurrency trading research, by consulting 146 research publications on numerous aspects of the cryptocurrency business (e.g., cryptocurrency trading systems, bubble, and extreme condition, prediction of volatility, and return, crypto-assets portfolio construction, and crypto-assets, technical trading and others). This publication also investigates datasets, research inclination and dissemination among the objects of research (contents/properties) and technologies, finalizing with some promising opportunities that are open and transparent in the cryptocurrency market.
Purpose As cryptocurrencies continue to gain viability as an asset class, institutional investors and publicly traded firms have started taking investment positions in digital currencies. What firms may not be considering, however, is the effect these assets may have on their risk profiles. This study aims to (1) measure the effect of cryptocurrencies on the risk and return characteristics of publicly traded companies; (2) decipher the motives behind holding cryptocurrencies as an asset class; and (3) determine whether one reason for holding is more effective than another. To conduct this research, the four largest publicly traded holders of cryptocurrency as well as four of the most prominent cryptocurrencies are explored. Design/methodology/approach The cross-sectional analysis approach has been used to analyze the daily returns, volatility, betas and Sharpe Ratios of firms during periods without cryptocurrency strategies and during periods with cryptocurrency strategies. Findings The impact of the cryptocurrency asset class on common stock performance and corporate disclosures are documented. The importance of risk disclosures on cryptocurrency holdings is emphasized: Firms must better inform their stakeholders through comprehensive disclosures in financial statements. Firms utilize cryptocurrencies for various reasons such as treasury management tools or as direct sources of income. Consequently, the impact on returns and risks varies substantially. Originality/value To the best of the authors’ knowledge, this is one of the first studies on cryptocurrency investments in the treasury departments of publicly traded companies. The study contributes to the literature by extracting relevant information regarding company risk reporting and cryptocurrency risk at firms. The conclusions also promote firm transparency with detailed reporting of cryptocurrency holding risks.
Abstract This paper examines the impact of market related events and investor base on the spread of Bitcoin prices between two exchange platforms, Coinbase and Binance. Based on high‐frequency data samples collected from 2019 to 2021, we show how investors from different bases react differently to market related events, which create the price spreads between exchange platforms. We also identify the arbitrage opportunities these spreads create and establish arbitrage strategies for all identified events to exploit the variations in Bitcoin prices traded on both platforms. Findings indicate arbitrage offers profits that are higher overall than holding Bitcoin on either platform.
Abstract This study introduces a novel pairs trading strategy based on copulas for cointegrated pairs of cryptocurrencies. To identify the most suitable pairs and generate trading signals formulated from a reference asset for analyzing the mispricing index, the study employs linear and nonlinear cointegration tests, a correlation coefficient measure, and fits different copula families, respectively. The strategy’s performance is then evaluated by conducting back-testing for various triggers of opening positions, assessing its returns and risks. The findings indicate that the proposed method outperforms previously examined trading strategies of pairs based on cointegration or copulas in terms of profitability and risk-adjusted returns.
Stanisław Drożdż, Jarosław Kwapień, Marcin Wątorek
In relation to the traditional financial markets, the cryptocurrency market is a recent invention and the trading dynamics of all its components are readily recorded and stored. This fact opens up a unique opportunity to follow the multidimensional trajectory of its development since inception up to the present time. Several main characteristics commonly recognized as financial stylized facts of mature markets were quantitatively studied here. In particular, it is shown that the return distributions, volatility clustering effects, and even temporal multifractal correlations for a few highest-capitalization cryptocurrencies largely follow those of the well-established financial markets. The smaller cryptocurrencies are somewhat deficient in this regard, however. They are also not as highly cross-correlated among themselves and with other financial markets as the large cryptocurrencies. Quite generally, the volume V impact on price changes R appears to be much stronger on the cryptocurrency market than in the mature stock markets, and scales as $R(V) \sim V^α$ with $α\gtrsim 1$.
Abstract Novel technologies allow cryptocurrency exchanges to offer innovative services that set them apart from other exchanges. In this paper we study the distinct features of cryptocurrency fee schedules and the implications for optimal trade execution. We formulate an optimal execution strategy that minimizes the trading fees charged by the exchange. We further provide a proof for the existence of an optimal execution strategy for this type of fee schedule. In fact, the optimal strategy involves both market and limit orders on various price levels. The optimal order distribution scheme depends on the market conditions expressed in terms of the distribution of limit order execution probabilities and the exchange's specific configuration of the fee schedule. Our results indicate that a strategy kernel with an exponentially decaying allocation of trade volume to price levels further away from the best price provides a superior performance and potential reduction of trade execution cost of more than 60%. The robustness of these results is confirmed in an empirical study. To our knowledge this is the first study of optimal trade execution that takes into consideration the full fee schedule of exchanges in general.
From gold standard currencies to fiat money secured by government credit, to today's cryptocurrencies, the basic form of money and mankind's perception of its value has shifted dramatically. This paper will demonstrate the value and risk assessment of the two cryptocurrencies with the highest market share, i.e., Bitcoin and Ethereum. Although the current technology of cryptocurrencies is not perfect, it will improve over time and their value will increase due to the high demand for them. This aim of the study to give first-time investors an understanding of the valuation and risks of cryptocurrencies, rather than treating them as simple financial assets for investment. According to the analysis, the value and risk of Bitcoin depend deeply on many characteristics that were initially built into it. It also has an impact on the value of other virtual currencies at the same time. On the other hand, Ether is a much more open platform, so its value and risk depend more on the various applications and contracts built into a blockchain than Bitcoin. These results shed the light on guiding the further exploration of solving the safety problem of cryptocurrencies from different perspectives.