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.
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.
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.
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.
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.
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.
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.
Yuanyuan Zhang, Stephen Chan, Jeffrey Chu, Shou–hsing Shih
Decentralized finance, or ‘De-Fi’, is an emerging sector and movement in finance and the cryptocurrency space that aims to extend the idea of digital currencies to a global decentralized financial system. In many cases, customized ‘coins’ or ‘tokens’ are used for applications such as borrowing or lending, providing liquidity, and even voting. Built on the same foundations of traditional cryptocurrencies (e.g. Bitcoin), these tokens possess monetary value and can be traded using fiat currencies on specialized decentralized exchanges. We provide the first analysis investigating the market efficiency of the decentralized finance market through DeFi tokens. Our findings from applying the adaptive market hypothesis (AMH) revealed that the efficiency of the markets varies over time, with the majority of the DeFi token returns exhibit very short days of inefficiency and predictability in their price every year. This is consistent with the AMH, but perhaps unexpected when considering the link between emerging financial markets and market efficiency. We conclude that the majority of investors and practitioners purchase these DeFi tokens for their utility value rather than for investment purposes, hence making the DeFi market more efficient. Further robustness checks on other comparable products in the blockchain ecosystem such as NFTs also reveal similar results.
Abdul Mannan Kanji, Ishita Chaudhary, Rithika Lakshmi Shankar, Gowri Srinivasa
Bitcoin is a decentralized digital currency that was intro- duced in 2009 and since then, has become increasingly popular as one of the most known and highly valued currencies. Contributing factors to its rise include crypto Twitter influencers. An engaged audience on Twitter seems to have an influence on the cryptocurrency market. In this paper, we analyze the impact that tweets have on the price of Bitcoin. Using word-clouds and candlestick plots, we gain insight into the factors that affect Bitcoin prices. We also use various machine learning techniques to automatically classify the sentiment in Tweets related to cryptocurrencies. We incorporate these and other relevant features to build and compare the performance of multiple machine learning models to predict the direction (increase or decrease) of the price of Bitcoin.
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.
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.
This chapter attempts to account for the rising value of cryptocurrencies using basic concepts of monetary theory. A positive value of fiat money is itself problematic inasmuch as that value apparently depends entirely on its expected resale value. A current value entirely dependent on expected future resale value seems inconsistent with backward induction. While fiat money can avoid the backward-induction problem if it is made acceptable in payment of taxes, acceptability for tax payments is unavailable to cryptocurrencies. Is the rising value of bitcoin and other cryptocurrencies a bubble? The paper argues that network effects may be an alternative mechanism for avoiding the logic of backward induction. Because users of any good subject to substantial network effects incur costs by switching to an incompatible alternative to the good currently used, users of a bitcoin for certain transactions may be locked into continued use of bitcoin despite an expectation that its future value will eventually go to zero. Thus, even if bitcoin and other cryptocurrencies are bubble phenomena, network effects may lock existing users of bitcoin into continued use of bitcoin for those transactions for which bitcoins provide superior transactional services to those provided by conventional currencies. Nevertheless, the prospects for bitcoin's expansion beyond its current niche uses are dim, because its architecture implies that a significant expansion in the demand for its transactional services would lead to rapid appreciation that is incompatible with service as a medium of exchange.
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.
This paper analyses the effects known as the day of the week and the month of the year in the cryptocurrency markets. The closing values of eleven cryptocurrencies have been considered. The study employs dummy variable regression techniques, ANOVA and Friedman tests for assessing two calendar anomalies, the day-of-week and month-of-year effects. To test these calendar effects, we have applied both full sample and rolling-regression techniques for two lengths of the rolling sample intervals. Furthermore, we have examined the existence of long memory in day-of-the- week and month-of-the-year cryptocurrency returns. The results provide evidence about the existence of day-of-the-week and month-of-the-year effects in cryptocurrency returns, in particular, on Thursdays and in November. In addition, it should be added that the general results of the current study show that the calendar effect in the cryptocurrency market is dynamic rather than static, which indicates that the calendar effect is a phenomenon that varies over time.
Emmanuel Joel Aikins Abakah, Aviral Kumar Tiwari, Chi‐Chuan Lee, Matthew Ntow‐Gyamfi
Abstract This research explores the distributional and directional predictabilities among Fintech, Bitcoin, and artificial intelligence stocks from March 2018 to January 2021 using nonparametric causality‐in‐quantile and crossquantilogram approaches. We also examine connectedness across the assets using a quantile VAR approach. The results indicate the existence of bidirectional causality‐in‐variance between the variables in a normal market. We also find that directional predictability among the assets is oscillatory over time lags. Finally, we observe a strong price connectedness for highly positive and negative changes. These results further document the diversification potential and safe‐haven properties of technology‐related assets for portfolio investors.
In May 2021, Chinese government issued a series of policies to strengthen the supervision of cryptocurrency market. Based on the principle of event study and combing of Chinese and American cryptocurrency policies, this paper selects May 18 as the event announcement day and constant mean return models are constructed to measure the abnormal returns of top ten market value coins. It is found that the strengthening of regulatory policies had an evidently negative influence on the market. Combined with the analysis of the features of typical cryptocurrencies, this paper figures out that on the whole there is a strong correlation between the abnormal returns of currencies in the cryptocurrency market. At the same time, by analyzing the performance of daily abnormal returns of Bitcoin and Ether in the event window, this study draws a conclusion that investors in that market have patent speculativeness. In addition, complimentary survey on stable coin is also done to verify the overall policy impact, which indicates that the market value fluctuation of stable coin is consistent with the abnormal return changes of the whole market. On this basis, through information query and literature reference, this paper also forecasts the evolution trend of regulatory policies in various countries and the development of the overall cryptocurrency market by period.
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.
In today's world we can see the trend of cryptocurrency is constantly increasing every day. In the financial sector, cryptocurrency has become a huge topic and the right prediction has become more important to gain profits. For determining the right prediction with good accuracy, we performed deep analysis on dataset to understand the market behavior by using different machine learning algorithms like Linear Regression, Random Forest Regressor, Gradient Boosting Regressor, and XGBoost to predict the daily price behavior of top 4 cryptocurrencies like Bitcoin, XRP, Ethereum, and Stellar using these machine learning algorithms. Our experimental result reaches to 95–97 percent validation accuracy.