Abstract The purpose of this research is to identify how effective the determinants of the improved price changes in cryptocurrencies are and if they are predictable. The study addresses several independent variables that are in our consideration which may impact the prices the most. To obtain the results, panel data has been used to run fixed effects models. Then we treated them as time series data to run dynamic trend indicator and first-differencing volatility regression model. Important political shocks and instabilities have been analyzed and interpreted in this paper. In the light of our findings we were able to comment on the complex relation between cryptocurrency prices and socio-political situations throughout the time range. The results address that cryptocurrency price changes are predictable. It is easy to say that major stakeholders (Apple, Amazon, Facebook, Google, Tesla) affect the most prices. Internet search trends seem to have an impact but at the end it has been found that the correlation is strong. We have evaluated all the major cryptocurrency prices with exact accuracy of 95.38% using the volatility regression model effectively. All the cryptocurrencies are evaluated against US dollars in regard of different cryptocurrency like Bitcoin, Ethereum, Litecoin and Ripple digital currency. Cryptocurrencies shouldn’t be seen as a gambling medium and should be taken more seriously like an investment medium. In some specific occasions investing in cryptocurrencies may lead lucrative income.
We conduct a network analysis with centrality measures, using historical daily close prices of top 120 cryptocurrencies between 2013 and 2020, to study and understand the dynamic evolution and characteristics of the cryptocurrency market. Our study has three primary findings: (1) the overall cross-return correlation among the cryptocurrencies is weakening from 2013 to 2016 and then strengthening thereafter; (2) cryptocurrencies that are primarily used for transaction payment, notably BTC, dominate the market until mid-2016, followed by those developed for applications using blockchain as the underlying technology, particularly data storage and recording such as MAID and FCT, between mid-2016 and mid-2017. Since then, ETH, alongside with its strongly correlated cryptocurrencies have replaced BTC to become the benchmark cryptocurrencies. Furthermore, during the outbreak of COVID-19, QTUM and BNB have intermittently replaced ETH to take the leading positions due to their active community engagement during the pandemic; (3) centrality measures are useful features in improving the prediction accuracy of the short-term cryptocurrency price movement.
Oct 26, 2020·CEUR Workshop Proceedings, Vol-2713: Proceedings of the Selected Papers of the Special Edition of International Conference on Monitoring, Modeling & Management of Emergent Economy (M3E2-MLPEED 2020)
Current research has led to a rejection of the hypothesis of a normal distribution of financial assets returns. Under these conditions, portfolio variance cannot serve as a good risk measure. In this paper analyzed the daily returns of the most common cryptocurrencies: Bitcoin, Ethereum, XRP, USDT, Bitcoin Cash, Litecoin. It is shown that the asset returns are not normally distributed, but with good precision follow the Cauchy distribution and Laplace distribution. The analytical expressions for risk measure were obtained using the distribution function and the VaR technique. However, the risk assessment of the return obtained on the basis of the Cauchy distribution is twice as high as the risk assessment obtained on the basis of the Laplace distribution. Therefore, the question arises: what distribution law to use to measurement the cryptocurrency risk? The paper shows that the Laplace distribution is the most adequate basis for measuring of cryptocurrencies risk.
Hanna Danylchuk, Liubov Kibalnyk, Oksana Kovtun, Arnold Kiv · 6 authors
In this article, we present the results of simulation for cryptocurrency market based on fractal and entropy analysis using six cryptocurrencies in the first 20 of the capitalization rating. The application of the selected research methods is based on an analysis of existing methodologies and tools of economic and mathematical modeling of financial markets. It has been shown that individual methods are not relevant because they do not provide an adequate assessment of the given market, so an integrated approach is the most appropriate. Daily values of cryptocurrency pairs from August 2016 to August 2020 selected by the monitoring and modelling database. The application of fractal analysis led to the conclusion that the time series of selected cryptocurrencies were persistent. And the use of the window procedure for calculating the local Hurst coefficient allowed to detail and isolate the persistant and antipersistant gaps. Interdisciplinary methods, namely Tsallis entropy and wavelet entropy, are proposed to complement the results. The results of the research show that Tsallis entropy reveals special (crisis) conditions in the cryptocurrency market, despite the nature of the crises’ origin. Wavelet entropy is a warning indicator of crisis phenomena. It provides additional information on a small scale.
Despite the rise in markets for cryptocurrencies at an outstanding pace, with consistently high trading volume and market capitalization, the increasing volatility of the virtual currencies raise various concerns. One of the major concerns is regarding (in)efficiency, viz. whether there exist opportunities of making excess returns based on out-performing the market or merely a game of chance. In this study, the authors investigate the weak-form efficiency of the top-ten cryptocurrencies using non-parametric and parametric random walk testing methods that are robust to unknown structural breaks and asymmetric effects. The findings do not support the random walk hypothesis, hence validating the weak-form inefficiency for daily cryptocurrencies returns. This can be attributed to the presence of asymmetric volatility clusters. This study has significant implications for portfolio managers, market participants and regulators of leading cryptocurrency markets.
Abstract Investors commonly exhibit the disposition effect—the irrational tendency to sell their winning investments and hold onto their losing ones. While this phenomenon has been observed in many traditional markets, it remains unclear whether it also applies to atypical markets like cryptoassets. This paper investigates the prevalence of the disposition effect in Bitcoin using transactions targeting cryptoasset exchanges as proxies for selling transactions. Our findings suggest that investors in Bitcoin were indeed subject to the disposition effect, with varying intensity. They also show that the disposition effect was not consistently present throughout the observation period. Its prevalence was more evident from the boom and bust year 2017 onwards, as confirmed by various technical indicators. Our study suggests irrational investor behavior is also present in atypical markets like Bitcoin.
This paper examines the relationship of the leading financial assets, Bitcoin, Gold, and S&P 500 with GARCH-Dynamic Conditional Correlation (DCC), Nonlinear Asymmetric GARCH DCC (NA-DCC), Gaussian copula-based GARCH-DCC (GC-DCC), and Gaussian copula-based Nonlinear Asymmetric-DCC (GCNA-DCC). Under the high volatility financial situation such as the COVID-19 pandemic occurrence, there exist a computation difficulty to use the traditional DCC method to the selected cryptocurrencies. To solve this limitation, GC-DCC and GCNA-DCC are applied to investigate the time-varying relationship among Bitcoin, Gold, and S&P 500. In terms of log-likelihood, we show that GC-DCC and GCNA-DCC are better models than DCC and NA-DCC to show relationship of Bitcoin with Gold and S&P 500. We also consider the relationships among time-varying conditional correlation with Bitcoin volatility, and S&P 500 volatility by a Gaussian Copula Marginal Regression (GCMR) model. The empirical findings show that S&P 500 and Gold price are statistically significant to Bitcoin in terms of log-return and volatility.
Inés Jiménez, Andrés Mora‐Valencia, Trino‐Manuel Ñíguez, Javier Perote
The semi-nonparametric (SNP) modeling of the return distribution has been proved to be a flexible and accurate methodology for portfolio risk management that allows two-step estimation of the dynamic conditional correlation (DCC) matrix. For this SNP-DCC model, we propose a stepwise procedure to compute pairwise conditional correlations under bivariate marginal SNP distributions, overcoming the curse of dimensionality. The procedure is compared to the assumption of Dynamic Equicorrelation (DECO), which is a parsimonious model when correlations among the assets are not significantly different but requires joint estimation of the multivariate SNP model. The risk assessment of both methodologies is tested for a portfolio on cryptocurrencies by implementing backtesting techniques and for different risk measures: Value-at-Risk, Expected Shortfall and Median Shortfall. The results support our proposal showing that the SNP-DCC model has better performance for a smaller confidence level than the SNP-DECO model, although both models perform similarly for higher confidence levels.
The emergence of cryptocurrency has introduced a transformative force in the global financial landscape, challenging the conventional structures of traditional financial markets. This paper explores the dynamic relationship between digital currencies and established financial systems, focusing on areas such as investment behavior, regulatory responses, market volatility, and the evolving role of financial institutions. It highlights how cryptocurrencies, particularly Bitcoin and Ethereum, have begun to influence asset allocation strategies, capital flows, and risk perceptions among investors. Furthermore, the paper examines the integration of blockchain technology in financial services and how its decentralized nature poses both opportunities and threats to conventional banking practices. While cryptocurrencies have opened up avenues for innovation and financial inclusion, their unregulated nature raises concerns regarding market stability and security. This study aims to provide a comprehensive understanding of the implications of cryptocurrency growth for traditional financial markets, suggesting the need for adaptive regulatory frameworks and strategic responses from financial institutions.
This paper marries models of stochastic bubbles and the standard model of product diffusion to study the role of price bubbles in cryptocurrency adoption.
This paper is devoted to the problems of the short-term forecasting cryptocurrencies time series using machine learning approach. We applied two the most powerful ensembles methods: Random Forests (RF) and Gradient Boosting Machine (GBM). For testing models we used the daily close prices of three the most capitalized coins: Bitcoin (BTC), Ethereum (ETH) and Ripple (XRP), and as a features were selected the past price information and technical indicators (moving average). To check the efficiency of these models we made out-of-sample forecast for three cryptocurrencies by using one step ahead technique. As the accuracy rate for our models we were selected Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) metrics. According to comparative analysis of the predictive ability of the RF and GBM both models showed the same order of accuracy for the out-of-sample dataset prediction, although boosting also was somewhat more accurate. Computer experiments have confirmed the feasibility of using the machine learning ensembles approaches considered for the short-term forecasting of cryptocurrencies time series. Built models and their ensembles can be used as the basis algorithms for automated Internet trading systems.
We test various volatility models using the Bitcoin spot price series. Our models include HIST, EMA ARCH, GARCH, and EGARCH, models. Both of our in-sample-fit and out-of-sample-forecast results suggest that GARCH and EGARCH models perform much better than other models. Moreover, the EGARCH model's asymmetric term is positive and insignificant, which suggests that Bitcoin prices lack the asymmetric volatility response to past returns. Finally, we formulate an option trading strategy by exploiting the volatility spread between the GARCH volatility forecast and the option's implied volatility. We show that a simple volatility-spread trading strategy with delta-hedging can yield robust profits.
In this paper, we study the cryptocurrency asset pricing via machine learning. Cryptocurrency is based on the blockchain technology. In 2008, a person who published an idea. By 2011, that idea is a market worth over 54 million. After bitcoin began taking off, hundreds of alternative cryptocurrencies formed an in-depth market system. More advanced, alternative forms of blockchains are springing up as autonomous corporations.
Prior studies have shown that social media discussions can be helpful in predicting price movements in financial markets. With the increasingly large amount of social media data, how to effectively distinguish value-relevant information from noise remains an important question. We study this question by investigating the role of network cohesion in the relationship between social media sentiment and price changes in the Bitcoin market. As network cohesion is associated with information correlation within the discussion network, we hypothesize that less cohesive social media discussion networks are better at predicting the next-day returns than more cohesive networks. Both regression analyses and trading simulations based on data collected from Bitcointalk.org confirm our hypothesis. Our findings enrich the literature on the role of social media in financial markets and provide actionable insights for investors to trade based on social media signals.