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2,335 results · page 37 of 98
David Iheke Okorie, Boqiang Lin
No abstract is available for this record.
Dora Almeida, Andreia Dionísio, Isabel Vieira, Paulo Ferreira
Cryptocurrency investments are often perceived as uncertain and risky. In this study, we assessed if this is indeed the case, using a sample of seven cryptocurrencies and considered a period that encompassed the first real global shock in the life of these relatively new financial assets, the COVID-19 pandemic. Uncertainty was evaluated using Shannon’s symbolic entropy. To measure risk, we use value-at-risk and conditional value-at-risk. The results indicate that, except for Tether, the analyzed cryptocurrencies’ returns exhibited similar patterns of uncertainty and risk. Levels of uncertainty were close to the maximum values, but high uncertainty is not always associated with high risk. During the pandemic crisis, uncertainty increased while risk decreased, suggesting that the considered assets may have safe haven properties.
Misha Perepelitsa
In this paper we give an elementary analysis of economics of Bitcoin that combines the transaction demand by the consumers and the supply of hashrate by miners. We argue that the decreasing block reward will have no significant effect on the exchange rate (price) of Bitcoin and thus the network will be transitioning to a regime where transaction fees will play a bigger part of miners' revenue. We consider a simple model where consumers demand bitcoins for transactions, but not for hoarding bitcoins, and we analyze market equilibrium where the demand is matched with the hashrate supplied by miners. Our main conclusion is that the exchange rate of Bitcoin cannot be determined from the market equilibrium and so our arguments support the hypothesis that Bitcoin price has no economic fundamentals and is free to fluctuate according to the present demand for hoarding and speculation. We point out that increasing fees bear the risk of Bitcoin being outcompeted by its main rival Ethereum, and that decreasing revenues to miners depreciate the perception of Bitcoin as a medium for store value (hoarding demand) which will have effect its exchange rate.
Geumil Bae, Jang Ho Kim
The cryptocurrency market is understood as being more volatile than traditional asset classes. Therefore, modeling the volatility of cryptocurrencies is important for making investment decisions. However, large swings in the market might be normal for cryptocurrencies due to their inherent volatility. Deviations, along with correlations of asset returns, must be considered for measuring the degree of market anomaly. This paper demonstrates the use of robust Mahalanobis distances based on shrinkage estimators and minimum covariance determinant for observing anomaly scores of cryptocurrencies. Our analysis shows that anomaly scores are a critical complement to volatility measures for understanding the cryptocurrency market. The use of anomaly scores is further demonstrated through portfolio optimization and scenario analysis.
Hemendra Gupta, Rashmi Chaudhary
Cryptocurrencies have gained a lot of attraction across the globe. Most observers of the cryptocurrency market will agree that crypto volatility is in a different league altogether. There has been a growing need to understand the nature of volatility in cryptocurrency. This paper analyzes the performance of four mostly traded, different cryptocurrencies in terms of their risk and return. The relationship between the return and returns volatility among different currencies has been examined considering the daily closing prices from 1 January 2017 to 30 June 2022, using the family of the GARCH model. The study has explored the spillover and asymmetric effect of volatility by using the DCC GARCH model and EGARCH model, respectively. The causal behavior among different cryptocurrencies has also been examined using Granger causality. There has been a strong spillover effect among different cryptocurrencies, Bitcoin and Ether, which are the top two cryptocurrencies with the highest market capitalization which have exhibited an asymmetric impact in their volatility as compared to the other two currencies, which are Litecoin and XRP.
Adedeji Daniel Gbadebo
The creation of distributed ledger technology resulted in the use of secured peer-to-peer interactions that pave way for the invention of Bitcoin and other cryptocurrencies. Since its invention, the price of Bitcoin has exhibited excessive volatility and has attracted increasing attentions. This paper considers the isolated influence of network activities (confirmed payments and users’ adoptions), mining information (network difficulty, Hashrate and transaction fees) and market factors (such as, bitcoin supply and trade volume) as key drivers of Bitcoin price. Using the vector autoregressive model (VECM), the results identified the existence of both long-term equilibrium and short-term dynamic relationship amongst the endogenous system’s variables. The cointegration relation has reversed adjustment effects on the bitcoin return. Accordingly, any deviation from the equilibrium dynamics due to perturbations of network events, market forces and mining data would be minimised. This explains why the Bitcoin price, and by implication its return, continues to experience different massive run-up, spiky protrusions, resistance, reversals, strong supports and consolidations. Based on the finding, the study recommends increased regulatory efforts to curb the excessive fluctuations in Bitcoin price in order to prevent significant loss which could discourage digital investors in the cryptocurrency markets.
Audil Rashid, Walid Bakry, Somar Al-Mohamad
The paper seeks to determine whether Bitcoin behaves differently from forex markets and Gold, and whether it offers any diversification, hedging, or safe-haven potential. A Markov regime-switching regression model is employed to determine the relationship between Bitcoin, the real economic activity, foreign exchange markets, financial markets, Energy, and Gold. The results indicate that, unlike USD/EUR and Gold, besides other variables, Bitcoin exhibits significant deviations in terms of its association with other financial and economic variables. Bitcoin appears to be strikingly positively associated with equity markets in both regimes. This may limit its potential to either act as a hedge or a safe-haven for US Equity markets. Bitcoin also deviates considerably from Gold and USD/EUR as it is not affected by the same set of variables as Gold or USD/EUR are under either regime. Moreover, while Gold appears to offer considerably weak safe-haven properties, particularly against equity, Bitcoin fails to be a safe-haven for any of the assets under study. The results, however, indicate that the properties of Bitcoin may range between a diversifier and a hedge, however, such potential of Bitcoin must be viewed with caution owing to the large volatility exhibited by Bitcoin.
Yongjun Kim, Yung-Cheol Byun
This study uses the API of Upbit, one of Korea’s cryptocurrency exchanges, to predict continuous time series for a limited period and cryptocurrencies using LSTM, a machine learning technique. The trading (buying and selling) point algorithm presented in this study was used to conduct experimental research on efficient profit creation for cryptocurrency investment. Several related studies have shown the results of time series prediction for long-term forecasts, such as a week or several months. Still, they have not attempted to make an ultra-short-term prediction in units of one minute. This paper attempts such a 1 min prediction. This is an experiment to create efficient profits by setting efficient trading (buying and selling) points using machine learning techniques and repeating these operations by an algorithm. Applying it to cryptocurrency shows the possibility of time series prediction.
Zae Young Kim, Jeong-Hyuck Park
Modern technology has brought novel types of wealth. In contrast to hard cash, digital currency does not have a physical form. It exists in electronic forms only. To date, it has not been clear what impacts its ongoing growth will have, if any, on wealth distribution. Here, we propose to identify all forms of contemporary wealth into two classes: ‘distinguishable’ or ‘identical’. Traditional tangible moneys are all distinguishable. Financial assets and cryptocurrencies, such as bank deposits and Bitcoin, are boson-like, while non-fungible tokens are fermion - like. We derived their ownership-based distributions in a unified manner. Each class follows essentially the Poisson or the geometric distribution. We contrast their distinct features such as Gini coefficients. Furthermore, aggregating different kinds of wealth corresponds to a weighted convolution where the number of banks matters and Bitcoin follows Bose–Einstein distribution. Our proposal opens a new avenue to understand the deepened inequality in modern economy, which is based on the statistical physics property of wealth rather than the individual ability of owners. We call for verifications with real data.
Patrick Jaquart, Sven Köpke, Christof Weinhardt
We employ and analyze various machine learning models for daily cryptocurrency market prediction and trading. We train the models to predict binary relative daily market movements of the 100 largest cryptocurrencies. Our results show that all employed models make statistically viable predictions, whereby the average accuracy values calculated on all cryptocurrencies range from 52.9% to 54.1%. These accuracy values increase to a range from 57.5% to 59.5% when calculated on the subset of predictions with the 10% highest model confidences per class and day. We find that a long-short portfolio strategy based on the predictions of the employed LSTM and GRU ensemble models yields an annualized out-of-sample Sharpe ratio after transaction costs of 3.23 and 3.12, respectively. In comparison, the buy-and-hold benchmark market portfolio strategy only yields a Sharpe ratio of 1.33. These results indicate a challenge to weak form cryptocurrency market efficiency, albeit the influence of certain limits to arbitrage cannot be entirely ruled out.
Po−Keng Cheng, Chin‐Ho Lin
In this study, we apply an interactive agent‐based model to investigate fundamentalists and positive-feedback traders behaviours in cryptocurrency markets. Our results suggested that fundamentalists pushed up cryptocurrency prices in some past periods. In addition, the cryptocurrency markets are unstable and agitated.
Müslüm Polat, Oktay Karakaya
Çalışmanın temel amacı; son on yıla damga vuran kripto paralar arasındaki getiri ve risk açısından nedensellik ilişkisini tespit etmektir. Bu amaçla piyasa değeri en yüksek 10 kripto paradan en fazla verisi bulunan Bitcoin, Ethereum, Litecoin, Stellar, Ripple arasında 10 model oluşturulmuş ve her model, Granger nedensellik ve Hafner-Herwatz varyansta nedensellik testleri test edilmiştir. Çalışmada 23 Şubat 2017 ile 18 Haziran 2021 tarihleri arasındaki günlük verilerden oluşan 1577 gözlem kullanılmıştır. Nedensellik analizi sonuçlarına göre seçili kripto paralar arasında ortalamada Ethereum - Litecoin hariç diğer değişkenler arasında Granger nedensellik ilişkisi, varyansta ise Bitcoin - Ethereum ve Bitcoin - Litecoin hariç diğer değişkenler arasında varyansta nedensellik ilişkisi tespit edilmiştir.
Nidhal Mgadmi, Azza Béjaoui, Wajdi Moussa
No abstract is available for this record.
Weike Yang, Zheng Tao
In this paper, we analyze the time-series graphs of Bitcoin price and Twitter-based economic uncertainty index over the past two years and use a wavelet coherence graph to determine their relationship. We found a causal relationship between Bitcoin (BTC) and Twitter-based economic uncertainty (TEU) index in different frequency bands, which would help predict Bitcoin price movements in the future. Our study provides reference to academics and investors.
Di Zhang, Youzhou Zhou
The goal of cryptocurrencies is decentralization. In principle, all currencies have equal status. Unlike traditional stock markets, there is no default currency of denomination (fiat), thus the trading pairs can be set freely. However, it is impractical to set up a trading market between every two currencies. In order to control management costs and ensure sufficient liquidity, we must give priority to covering those large-volume trading pairs and ensure that all coins are reachable. We note that this is an optimization problem. Its particularity lies in: 1) the trading volume between most (>99.5%) possible trading pairs cannot be directly observed. 2) It satisfies the connectivity constraint, that is, all currencies are guaranteed to be tradable. To solve this problem, we use a two-stage process: 1) Fill in missing values based on a regularized, truncated eigenvalue decomposition, where the regularization term is used to control what extent missing values should be limited to zero. 2) Search for the optimal trading pairs, based on a branch and bound process, with heuristic search and pruning strategies. The experimental results show that: 1) If the number of denominated coins is not limited, we will get a more decentralized trading pair settings, which advocates the establishment of trading pairs directly between large currency pairs. 2) There is a certain room for optimization in all exchanges. The setting of inappropriate trading pairs is mainly caused by subjectively setting small coins to quote, or failing to track emerging big coins in time. 3) Too few trading pairs will lead to low coverage; too many trading pairs will need to be adjusted with markets frequently. Exchanges should consider striking an appropriate balance between them.
Forbes Kaseke, Shaun Ramroop, Henry Mwambi
Despite the rapid growth of developing markets, aided by globalization, comparative studies of cryptocurrency and stock market volatility have focused on the developed markets and neglected developing ones. In this regard, this study compares cryptocurrency volatility with that of the Johannesburg Stock Exchange (JSE), a developing market. GARCH-type models are applied to daily log returns of Bitcoin, Ethereum, and the FTSE/JSE 4O in two ways. Firstly, the models are applied directly; secondly, structural breaks are tested and accounted for in the models. The sample period was from September 18, 2017, to May 27, 2021. The results show higher volatility and higher volatility persistence in cryptocurrency than in the JSE market. They also show that persistence is overestimated for cryptocurrencies when structural breaks are not accounted for. The opposite was true for the JSE.Moreover, the two cryptocurrencies were found to have close to identical volatility plots that differ from that of the JSE. High volatility periods of cryptocurrency also did not coincide with that of JSE and those of JSE did not coincide with the cryptocurrency ones. There is also evidence of an inverse leverage effect in cryptocurrency, which opposes the normal leverage effect of the JSE market.
Viktor Manahov
The enormous rise of the cryptocurrencies over the last few years has created one of the largest unregulated markets in the world. In this study, we obtain millisecond data for the five major cryptocurrencies—bitcoin, ethereum, ripple, litecoin and dash—and two cryptocurrency indices—Crypto Index (CRIX) and CCI30 Crypto Currencies Index—to investigate the relationship between cryptocurrency liquidity, herding behaviour and profitability during periods of extreme price movements (EPMs). We demonstrate that cryptocurrency traders (CTs) facilitate EPMs and demand liquidity even during the utmost EPMs. We observe the presence of herding behaviour during up markets across the entire dataset. Our robustness checks indicate that herding behaviour follows a dynamic pattern that varies over time with decreasing magnitude. We also provide novel evidence of CTs’ profitability after transaction costs, and demonstrate their strong profitability-generating record in the future.
Tomáš Šťastný, Jiří Koudelka, Diana Bílková, Luboš Marek
Cryptocurrencies are a new field of investment opportunities that has experienced a significant growth in the last decade. The crypto market was capitalized at more than USD 3000 bn, having grown from USD 10 m over the period 2011–2021. Generating high returns, investments in cryptocurrencies have also shown high levels of price volatility. By comparing the performance of cryptocurrencies (measured by the crypto index) and standard equities (included in the S&P 500 index), we found that the former has outperformed the latter 14 times over the last two years. In the present paper, we analyzed the 2012–2022 global crypto market developments and main constituents. With a focus on the top 30 cryptocurrencies and their prices, as of 9 April 2022, covering data of the two major market stress events—outbreaks of the COVID-19 pandemic (February 2020) and the Russian invasion of Ukraine (February 2022). We applied the dynamic time warping method including barycentre averaging and k-Shape clustering of time series. The use of the dynamic time warping has been essential for the preparation of data for subsequent clustering and forecasting. In addition, we compared performance of cryptocurrencies and equities. Cryptocurrency time series are rather short, sometimes involving high levels of volatility and including multiple data gaps, whereas equity time series are much longer and well-established. Identifying similarities between them allows analysts to predict crypto prices by considering the evolution of similar equity instruments and their responses to historical events and stress periods. Moreover, we tested various forecasting methods on the 30 cryptocurrencies to compare traditional econometric methods with machine learning approaches.
Noé Oswaldo Rodríguez Rodríguez, Octavio Miramontes
Cryptocurrency markets have attracted many interest for global investors because of their novelty, wide on-line availability, increasing capitalization, and potential profits. In the econophysics tradition, we show that many of the most available cryptocurrencies have return statistics that do not follow Gaussian distributions, instead following heavy-tailed distributions. Entropy measures are applied, showing that portfolio diversification is a reasonable practice for decreasing return uncertainty.
Seyram Pearl Kumah, Jones Odei‐Mensah, Richmell Baaba Amanamah
This paper investigates the co-movement between cryptocurrencies and African stock returns to uncover their degree of association and global portfolio diversification benefits implementing the three-dimensional continuous Morlet wavelet transform technique. Data span 10 August 2015 to 10 December 2021 at daily frequency. The results suggest high degrees of co-movement between the asset markets at medium and lower frequencies implying that stock markets in Africa are highly exposed to cryptocurrency market disruptions from the medium term and that international investors seeking to hedge their price risk in African stock markets using cryptocurrencies may have to look at the short term. The phase difference arrow vectors implying lead (lag) effects are time-varying and heterogeneous showing no particular cryptocurrency or stock market as leader or follower. Different markets have the potential to lead or lag other markets at varying scales which may induce arbitrage opportunities for international and local investors. Our findings provide insights for policymakers, regulators and international investors as an economy’s monetary policy can be affected by the connections between the domestic capital market and other markets globally.
Maxim Bichuch, Zachary Feinstein
Within this work we consider an axiomatic framework for Automated Market Makers (AMMs). AMMs are smart contracts that set prices for swaps on a pool of assets. By imposing reasonable axioms on the underlying utility function, we are able to characterize the properties of the swap size of the assets and of the resulting pricing oracle. In providing these general axioms, we define a novel measure of price impacts that can be used to quantify those costs between different AMM constructions. We have analyzed many existing AMMs and shown that the vast majority of them satisfy our axioms. We have also considered the question of fees and divergence loss. In doing so, we have proposed a new fee structure so as to make the AMM indifferent to transaction splitting. Finally, we have proposed a novel AMM that has nice analytical properties and provides a large range over which there is no divergence loss.
Yizhi Wang, Florian Horky, Lennart John Baals, Brian M. Lucey · 5 authors
Amid surging market values and widespread regulatory discussion, NFT and DeFi markets are widely perceived as being simply speculative in nature. This paper detects the existence and dates of price bubbles in the NFT and DeFi markets by applying SADF and GSADF tests. We document that NFT and DeFi markets both exhibit speculative bubbles, with NFT bubbles being more recurrent and having higher average explosive magnitudes than DeFi bubbles. The price bubbles in the NFT and DeFi markets are highly correlated with market hype and with more general cryptocurrency market uncertainty. We do find periods where bubbles are not detected, suggesting that these markets do have some intrinsic value and should not be dismissed as simply bubbles.
Muhammad Sheraz, Silvia Dedu, Vasile Preda
This paper aims to empirically examine long memory and bi-directional information flow between estimated volatilities of highly volatile time series datasets of five cryptocurrencies. We propose the employment of Garman and Klass (GK), Parkinson's, Rogers and Satchell (RS), and Garman and Klass-Yang and Zhang (GK-YZ), and Open-High-Low-Close (OHLC) volatility estimators to estimate cryptocurrencies' volatilities. The study applies methods such as mutual information, transfer entropy (TE), effective transfer entropy (ETE), and Rényi transfer entropy (RTE) to quantify the information flow between estimated volatilities. Additionally, Hurst exponent computations examine the existence of long memory in log returns and OHLC volatilities based on simple R/S, corrected R/S, empirical, corrected empirical, and theoretical methods. Our results confirm the long-run dependence and non-linear behavior of all cryptocurrency's log returns and volatilities. In our analysis, TE and ETE estimates are statistically significant for all OHLC estimates. We report the highest information flow from BTC to LTC volatility (RS). Similarly, BNB and XRP share the most prominent information flow between volatilities estimated by GK, Parkinson's, and GK-YZ. The study presents the practicable addition of OHLC volatility estimators for quantifying the information flow and provides an additional choice to compare with other volatility estimators, such as stochastic volatility models.
Éder Johnson de Area Leão Pereira, Paulo Ferreira, Derick Quintino
Non-fungible tokens (NFTs) are a type of digital record of ownership used in a unique way: ensuring authenticity and uniqueness. Due to these characteristics, NFTs have been used in several markets: games, arts, and sports, among others. In 2020, the volume of negotiations of the NFTs was about USD 200 million. Despite the strong interest of economic agents in operating with NFTs, there are still gaps in the literature, regarding their dynamics and price interrelation with other potentially related assets, which deserve to be studied. In this sense, the main purpose in this paper is to analyze the cross-correlation between NFTs and larger cryptocurrencies. To this end, our methodological approach is based on a Detrended Cross-Correlation Analysis correlation coefficient, with a sliding windows approach. Our main finding is that the cross-correlations are not significant, except for a few cryptocurrencies, with weak significance at some moments of time. We also carried out an analysis of the long-term memory of NFTs, which demonstrated the antipersistence of these assets, with results seemingly corroborating the market inefficiency hypothesis. Our results are particularly important for different classes of investors, due to the analysis on different time scales.