Due to the transparency, simplicity, and blockchain system, cryptocurrencies gained popularity in the modern world. This led to more use of cryptocurrencies for speculation and investment rather than a medium of exchange. It is crucial to analyse the nature of the crypto market before investing in such currencies. With this intention, the paper tried to know the extent of following (Followness) of altcoins to the bitcoin in the different dominance phases like High Dominance, Low Dominance, and Moderate Dominance. For this purpose, daily closing prices of the Bitcoin and five major altcoins (Ethereum, Litecoin, Namecoin, Doge, and Ripple) are collected for the last five years and analyse the relationship between bitcoin and altcoins. Pearson's correlation coefficient test is used to know the direction of the relationship, and Vector Error Correction Model is used to see the extent of the relation. In general, the empirical result of the study showed cointegration between bitcoin and Altcoin. It also depicted that Altcoin showed a high level of followness in the moderate dominance phase and low followness in the low dominance phase. The study developed a price estimation equation to predict the price of altcoins depending upon the price of bitcoin and its dominance in the crypto market. This paper concludes that the dominance of Bitcoin also has a significant role in the price movement of altcoins.
Managing extreme price fluctuations in cryptocurrency markets are of central importance for investors in this market segment. Using a sample of highly liquid cryptocurrencies from January 2017 to June 2021, this paper proposes a dynamic investment strategy that selects cryptocurrencies based on their historical volatility and is complemented by a simple stop-loss rule. Our results reveal that investing in highly concentrated low volatility cryptocurrency portfolios with six to twelve months volatility look-back and holding period generate statistically significant excess returns. By including a simple stop-loss rule, the downside risk of cryptocurrency portfolios is reduced markedly, and the Sharpe ratios are improved significantly.
Abstract The recently developed Bitcoin futures and options contracts in cryptocurrency derivatives exchanges mark the beginning of a new era in Bitcoin price risk hedging. The need for these tools dates back to the market crash of 1987, when investors needed better ways to protect their portfolios through option insurance. These tools provide greater flexibility to trade and hedge volatile swings in Bitcoin prices effectively. The violation of constant volatility and the log-normality assumption of the Black–Scholes option pricing model led to the discovery of the volatility smile, smirk, or skew in options markets. These stylized facts; that is, the volatility smile and implied volatilities implied by the option prices, are well documented in the option literature for almost all financial markets. These are expected to be true for Bitcoin options as well. The data sets for the study are based on short-dated Bitcoin options (14-day maturity) of two time periods traded on Deribit Bitcoin Futures and Options Exchange, a Netherlands-based cryptocurrency derivative exchange. The estimated results are compared with benchmark Black–Scholes implied volatility values for accuracy and efficiency analysis. This study has two aims: (1) to provide insights into the volatility smile in Bitcoin options and (2) to estimate the implied volatility of Bitcoin options through numerical approximation techniques, specifically the Newton Raphson and Bisection methods. The experimental results show that Bitcoin options belong to the commodity class of assets based on the presence of a volatility forward skew in Bitcoin option data. Moreover, the Newton Raphson and Bisection methods are effective in estimating the implied volatility of Bitcoin options. However, the Newton Raphson forecasting technique converges faster than does the Bisection method.
This paper initially presents a nontechnical overview of cryptocurrency, its history, and the technicalities of its usage as a means of exchange. Bitcoin’s working methodology and mathematical baseline is further presented in more depth. For the remaining majority of the paper, recent cryptocurrency price data of Bitcoin, Ethereum, Tether, Dogecoin, and Binance coin was used to train a machine learning model of Feed Forward Neural Networks to predict future prices for each of the datasets. Further and in conclusion, the results are discussed, and the efficiency and accuracy of these models are evaluated.
Cryptocurrency and blockchain are one of the most beautiful digital transformations occurring around the world. They have changed the orthodox meaning and working of currency as we know it. It is interesting to note how it excites and worries some. The main reason for the popularity of cryptocurrencies is tremendous returns in very little time. Social media platforms like twitter, provide a safe-place where individuals’ can share their thoughts as well as mindsets, which then can be heard and be reciprocated by others. This paper aims to draw a correlation between the hyped tweets and the prices of cryptocurrencies like Bitcoin - The Crypto King and Dogecoin - The Memecoin during those times. We also aim to predict the future price values of Bitcoin using its past values. By using cryptocurrencies’ financial data, twitter data, RAPIDS and cuml, a fine line can be drawn between the amount of impact tweets have on people as well as on the market. The tweets on cryptocurrency were segregated and price forecasting was done using augmented dickey fuller test and ARIMA models, 10 future values of bitcoin were predicted with 96% accuracy and 0.0395 average error.Besides, from the investigations above of the authentic cost of BTC, it is perfectly clear that there have been way more steep falls in the history of Cryptocurrencies even before Elon started tweeting about it. Thus, it can clearly be stated that no one person can control the utter volatile world of cryptocurrencies! And the decentralized system ledger of cryptocurrency remains unharmed.
Bitcoin has attracted attention from different market participants due to unpredictable price patterns. Sometimes, the price has exhibited big jumps. Bitcoin prices have also had extreme, unexpected crashes. We test the predictive power of a wide range of determinants on bitcoins’ price direction under the continuous transfer entropy approach as a feature selection criterion. Accordingly, the statistically significant assets in the sense of permutation test on the nearest neighbour estimation of local transfer entropy are used as features or explanatory variables in a deep learning classification model to predict the price direction of bitcoin. The proposed variable selection do not find significative the explanatory power of NASDAQ and Tesla. Under different scenarios and metrics, the best results are obtained using the significant drivers during the pandemic as validation. In the test, the accuracy increased in the post-pandemic scenario of July 2020 to January 2021 without drivers. In other words, our results indicate that in times of high volatility, Bitcoin seems to self-regulate and does not need additional drivers to improve the accuracy of the price direction.
Abstract Subject and purpose of work: Cryptocurrencies are a phenomenon that has been strengthening its place in the world of finance for over ten years and which is becoming a frequent investment tool. The aim of this study is to compare the level of risk measures of investments in the cryptocurrency market with investments in global capital markets in 2011-2020. Materials and methods: The study used the quotations of the analysed instruments. The level of risk was estimated using standard deviation and semi-standard deviation of daily logarithmic rates of return. Results: Investment in cryptocurrencies is more risky than in shares of the largest international companies. The level of risk decreases with the duration of the cryptocurrency presence on the market. Conclusions: Achieving extraordinary rates of return generates an increased demand and volatility of cryptocurrencies’ quotations. The level of risk of investing in cryptocurrencies is much higher than in the indexes of global capital exchanges.
Abstract The increasing attention on Bitcoin since 2013 prompts the issue of possible evidence for a causal relationship between the Bitcoin market and internet attention. Taking the Google search volume index as the measure of internet attention, time-varying Granger causality between the global Bitcoin market and internet attention is examined. Empirical results show a strong Granger causal relationship between internet attention and trading volume. Moreover, they indicate, beginning in early 2018, an even stronger impact of trading volume on internet attention, which is consistent with the rapid increase in Bitcoin users following the 2017 Bitcoin bubble. Although Bitcoin returns are found to strongly affect internet attention, internet attention only occasionally affects Bitcoin returns. Further investigation reveals that interactions between internet attention and returns can be amplified by extreme changes in prices, and internet attention is more likely to lead to returns during Bitcoin bubbles. These empirical findings shed light on cryptocurrency investor attention theory and imply trading strategy in Bitcoin markets.
AbdulQuddoos AbdulBasith, Mohammed Elgammal, Bana Abuzayed
Cryptocurrency (CCY) as a new key player in the currency system that has drawn the attention of scholars to examine its influence, relations and the opportunities that it may provide. However, a financial theoretical framework to connect CCY with financial theory is missing. This paper fills this gap by providing a review for the theoretical framework introduced in the literature to position CCY in investment and finance theories. This is done by studying the CCY literature and providing a critical feedback on the overall contributions in the area and possible venues for improvement. We report a need for a long-term analysis for CCY as this asset class is fairly new and sufficient data may not be available. Moreover, a better connection and linking with finance theories is required as it is significantly deficient. The promising potential of blockchain/ CCY stresses the need for interdisciplinary research including business, legal and information technology disciplines. In addition, the Covid-19 pandemic opens the door for further research to investigate the role of CCY as a hedge in the times of crises. Keywords: digital ledger technology, cryptocurrency bitcoin, finance theory, investment, fintech
Since its launch in 2009, bitcoin has thrived, attracting the attention of investors, regulators, academia, and the public in general. Its price dynamics, characterized by extreme volatility, severe jumps, and impressive long-term appreciation, suggest that bitcoin is a new digital asset. This study presents a comprehensive overview of the fractality of bitcoin in a high-frequency framework, namely by applying Multifractal Detrended Fluctuation Analysis (MF-DFA) and a Multifractal Regime Detecting Method (MRDM) to Bitstamp 1 min bitcoin returns from January 2013 to July 2020. The results suggest that bitcoin is multifractal, with smaller and larger fluctuations being persistent and anti-persistent, respectively. Multifractality comes from significant long-range correlations, which cast some doubts on the informational efficiency at this frequency, but mainly comes from fat-tails, which highlights the significant risks undertaken by investors in this market. Our most important result is that the degree and richness of multifractality is time-varying and increased after 2017, when volumes and prices experienced an explosive behaviour. This complexity puts into perspective the duality of bitcoin: while it is characterized by long-run attractiveness and increasing valuation, it also has a high short-run instability. Hence, this study provides some empirical evidence supporting the relationship between these two observable features.
Abstract Economic theory predicts the price dynamics of an unbacked asset to be inherently unforecastable. The same applies to exchange rates of unbacked currencies. Albeit, empirically investors are found to be driven by online and offline news media. This study analyzes the Bitcoin cryptocurrency price series and web search queries with regard to their mutual predictability and cause‐effect delay structure. Chinese Baidu engine searches and compounded Baidu–Google search statistics predict Bitcoin price dynamics at relatively high frequencies ranging from 2 to 5 months. In the other direction, Granger‐causality runs from the cryptocurrency price to queries statistics across nearly all frequencies. In both directions, the reaction time computed from a phase delay measure for the relevant frequency bands with significant causality ranges from about 1 to 4 months. For either direction, out‐of‐sample forecasts are more accurate than forecasts of a benchmark stochastic process. Bivariate models including the Baidu Search Index slightly outperform competing models that include a Baidu–Google composite index. Predictive power seems less diluted if the September 2017 trade regulations by the Chinese government are controlled for.
Karl Oton Rudolf, Samer Zein, Nicola Jackman Lansdowne
Volatility and investor sentiment have been factors for the slow adoption rate of Bitcoin (BTC) that was first recognized in 2008 as a potential store of value, investment vehicle and a hedge alternative to gold during a recession. The purpose of this applied mathematics study will use a multivariate DCC GARCH model. Bitcoin holds its ground in volatility. This study examines Bitcoin as an investment and hedge alternative to gold as well as the major stock index. To perform the research to explore the viability of Bitcoin as an investment and hedge alternative to gold, the authors conducted a DCC GARCH model analysis. The findings of this research paper confirm Bitcoin’s cyclical performance between volatility and adoption. The findings give a strong ground for Bitcoin as the new digital currency, store of value, medium of exchange, and a unit of account and incentivize further research by theorists, scholars and examiners. The significance of this applied mathematics research and analysis will allow an unstoppable, incorruptible, and uncontrollable store of value, and investment vehicle, without governmental or institutional intervention. This study contributes by comparing and contrasting volatility stability based on the return levels of each Bitcoin on major indexes traded with BTC (based on fiat currencies) and gold.
We present a systematic approach to detect fake transactions on cryptocurrency exchanges by exploiting robust statistical and behavioral regularities associated with authentic trading. Our sample consists of 29 centralized exchanges, among which the regulated ones feature transaction patterns consistently observed in financial markets and nature. In contrast, unregulated exchanges display abnormal first-significant-digit distributions, size rounding, and transaction tail distributions, indicating widespread manipulation unlikely driven by specific trading strategy or exchange heterogeneity. We then quantify the wash trading on each unregulated exchange, which averaged over 70% of the reported volume. We further document how these fabricated volumes (trillions of dollars annually) improve exchange ranking, temporarily distort prices, and relate to exchange characteristics (e.g., age and user base), market conditions, and regulation. Overall, our study cautions against potential market manipulations on centralized crypto exchanges with concentrated power and limited disclosure requirements, and highlights the importance of FinTech regulation.
In this paper, the cross-correlations of cryptocurrency returns are analysed. The paper examines one years worth of data for 146 cryptocurrencies from the period January 1 2019 to December 31 2019. The cross-correlations of these returns are firstly analysed by comparing eigenvalues and eigenvector components of the cross-correlation matrix C with Random Matrix Theory (RMT) assumptions. Results show that C deviates from these assumptions indicating that C contains genuine information about the correlations between the different cryptocurrencies. From here, Louvain community detection method is applied as a clustering mechanism and 15 community groupings are detected. Finally, PCA is completed on the standardised returns of each of these clusters to create a portfolio of cryptocurrencies for investment. This method selects a portfolio which contains a number of high value coins when compared back against their market ranking in the same year. In the interest of assessing continuity of the initial results, the method is also applied to a smaller dataset of the top 50 cryptocurrencies across three time periods of T = 125 days, which produces similar results. The results obtained in this paper show that these methods could be useful for constructing a portfolio of optimally performing cryptocurrencies.
In this paper we analyze the existence of cointegrating relationships between Bitcoin, S&P 500, and the quantity of money M2. We perform our analysis with and without applying time warping pre-processing. In all cases we find strong evidence that, in the period 2016-2021 the three time series show two cointegrating relationships and therefore share a common stochastic trend. In addition, a low correlation between Bitcoin and S&P 500 is detected. These finding justify the increased interest of investors in Bitcoin as an alternative asset class. The economic interpretation is that the stock valuation is primarily determined by financial phenomena, in particular the availability of large quantity of money. Money supporting investment is due both to the actions of Quantitative Easing and to the exchange of creditor/debtor role that took place between households and firms. The price of both Bitcoin and stocks is increasingly influenced by the amount of money in circulation and follows the same stochastic trend.
Pairs trading that is built on ’Relative-Value Arbitrage Rule’ is a popular short-term speculation strategy enabling traders to make profits from temporary mispricing of close substitutes. This paper aims at investigating the profit potentials of pairs trading in a new finance area – on cryptocurrencies market. The empirical design builds upon four well-known approaches to implement pairs trading, namely: correlation analysis, distance approach, stochastic return differential approach, and cointegration analysis, that use monthly closing prices of leading cryptocoins over the period January 1, 2018, – December 31, 2019. Additionally, the paper executes a simulation exercise that compares long-short strategy with long-only portfolio strategy in terms of payoffs and risks. The study finds an inverse relationship between the correlation coefficient and distance between different pairs of cryptocurrencies, which is a prerequisite to determine the potentially market-neutral profits through pairs trading. In addition, pairs trading simulations produce quite substantive evidence on the continuing profitability of pairs trading. In other words, long-short portfolio strategies, producing positive cumulative returns in most subsample periods, consistently outperform conservative long-only portfolio strategies in the cryptocurrency market. The profitability of pairs trading thus adds empirical challenge to the market efficiency of the cryptocurrency market. However, other aspects like spectral correlations and implied volatility might also be significant in determining the profit potentials of pairs trading.
Daniel Traian Pele, Niels Wesselhöfft, Wolfgang Karl Härdle, Michalis Kolossiatis · 5 authors
This research provides insights for the separation of cryptocurrencies from other assets. Using dimensionality reduction techniques, we show that most of the variation among cryptocurrencies, stocks, exchange rates, commodities, bonds, and real estate indexes can be explained by the tail, memory and moment factors of their log-returns. By applying various classification methods, cryptocurrencies are categorized as a separate asset class, mainly due to the tail factor. The main result is the complete separation of cryptocurrencies from the other asset types, using the Maximum Variance Components Split method. Additionally, we show that cryptocurrencies tend to exhibit similar characteristics over time and become more distinguished from other asset classes (synchronic evolution).
Liquid markets are driven by information asymmetries and the injection of new information in trades into market prices. Where market matching uses an electronic limit order book (LOB), limit orders traders may make suboptimal price and trade decisions based on new but incomplete information arriving with market orders. This paper measures the information asymmetries in Bitcoin trading limit order books on the Kraken platform, and compares these to prior studies on equities LOB markets. In limit order book markets, traders have the option of waiting to supply liquidity through limit orders, or immediately demanding liquidity through market orders or aggressively priced limit orders. In my multivariate analysis, I control for volatility, trading volume, trading intensity and order imbalance to isolate the effect of trade informativeness on book liquidity. The current research offers the first empirical study of Glosten (1994) to yield a positive, and credibly large transaction cost parameter. Trade and LOB datasets in this study were several orders of magnitude larger than any of the prior studies. Given the poor small sample properties of GMM, it is likely that this substantial increase in size of datasets is essential for validating the model. The research strongly supports Glosten's seminal theoretical model of limit order book markets, showing that these are valid models of Bitcoin markets. This research empirically tested and confirmed trade informativeness as a prime driver of market liquidity in the Bitcoin market.
Cryptocurrency is an advanced digital currency that is gotten by cryptography, numerous digital currencies are decentralized organizations dependent on blockchain innovation an appropriated record authorized by a different organization of computers. And Many present-day technologies are driving the transformative impact in the global financial system, in that impact cryptocurrency stands on first position in the list. Cryptocurrency offer several potential benefits, including better speed and efficiency in processing payments and transfers notably across borders and ultimately boosting financial inclusion. The intension of this paper is to summaries the difference between the normal or traditional method of currency transaction and crypto currency transaction. And why all are providing more interest towards crypto methods nowadays and what different they feel while choosing a crypto method over a normal or traditional currency methods. And how crypto currency is dragging current world attention towards its pocket and why many are developing interest towards following the crypto trend.
This work presents an application of self-attention networks for cryptocurrency trading. Cryptocurrencies are extremely volatile and unpredictable. Thus, cryptocurrency trading is challenging and involves higher risks than trading traditional financial assets such as stocks. To overcome the aforementioned problems, we propose a deep reinforcement learning (DRL) approach for cryptocurrency trading. The proposed trading system contains a self-attention network trained using an actor-critic DRL algorithm. Cryptocurrency markets contain hundreds of assets, allowing greater investment diversification, which can be accomplished if all the assets are analyzed against one another. Self-attention networks are suitable for dealing with the problem because the attention mechanism can process long sequences of data and focus on the most relevant parts of the inputs. Transaction fees are also considered in formulating the studied problem. Systems that perform trades in high frequencies cannot overlook this issue, since, after many trades, small fees can add up to significant expenses. To validate the proposed approach, a DRL environment is built using data from an important cryptocurrency market. We test our method against a state-of-the-art baseline in two different experiments. The experimental results show the proposed approach can obtain higher daily profits and has several advantages over existing methods.
Abstract We study the problem of the intraday short-term volume forecasting in cryptocurrency multi-markets. The predictions are built by using transaction and order book data from different markets where the exchange takes place. Methodologically, we propose a temporal mixture ensemble, capable of adaptively exploiting, for the forecasting, different sources of data and providing a volume point estimate, as well as its uncertainty. We provide evidence of the clear outperformance of our model with respect to econometric models. Moreover our model performs slightly better than Gradient Boosting Machine while having a much clearer interpretability of the results. Finally, we show that the above results are robust also when restricting the prediction analysis to each volume quartile.