Shimon Kogan, Igor Makarov, Marina Niessner, Antoinette Schoar
Trading in cryptocurrencies has grown rapidly over the last decade, primarily dominated by retail investors.Using a dataset of 200,000 retail traders from eToro, we show that they have a different model of the underlying price dynamics in cryptocurrencies relative to other assets.Retail traders in our sample are contrarian in stocks and gold, yet the same traders follow a momentum-like strategy in cryptocurrencies.Individual characteristics do not explain the differences in how people trade cryptocurrencies versus stocks, suggesting that our results are orthogonal to differences in investor composition or clientele effects.Furthermore, our findings are not explained by inattention, differences in fees, or preference for lotterylike stocks.We conjecture that retail investors hold a model of cryptocurrency prices, where price changes imply a change in the likelihood of future widespread adoption, which in turn pushes asset prices further in the same direction.
Bitcoin has had a volatile journey since it was launched in 2009, the current main impressions of Bitcoin are mostly negative, resource-consuming, endangering financial security, and even associated with crimes, such as fraud, money laundering, and so on. However, this paper analyzes the origin of bitcoin and with the creative combination of existing computer technology, the construction of a complete transaction system was founded by Bitcoin, which has caused a huge impact in the fields of finance, technology and the environment. We have to acknowledge its shortcomings and deficiencies in some aspects, On the other hand, realize that Bitcoin has brought great progress and reflection in the fields of finance, technology, environment, etc.
In this paper, we analyse the long memory process in the cryptocurrencies Bitcoin (BTC), Cardano (ADA), Binance Coin (BNB), Dogecoin (DOGE), Ethereum (ETH) and Ripple (XRP) from January 1st, 2018, to November 10th, 2022, which includes the 2020 and 2022 events. The results demonstrate that the daily returns are leptokurtic, and the distributions are non-Gaussian. We also observe non-linearity, implying autocorrelation or conditional heteroscedasticity in digital currencies. The DFA exponents reveal that throughout the Tranquil period, digital currencies with current values higher than 0.5 exhibited long memory in their returns. The BNB digital currency has an exponent of 0.5, indicating that the series were unpredictable throughout this period. As can be shown, all cryptocurrencies offer values of the DFA exponent greater than 0.5 in the Stress subperiod, implying that the higher the DFA exponent and closer to 1, the higher the persistence, as well as the autocorrelation between observations and stronger predictive ability. The findings support the evidence examined by the BDS test, namely that price movements are not i.i.d. (independent and identically distributed) and that investors have a high possibility of achieving above-average returns through arbitrage.
The purpose of this study is to explore the co-movement between COVID-19 cases and eight cryptocurrencies. Cryptocurrencies (Bitcoin, Ethereum, Tether, Binance Coin, Dogecoin, Ripple, USD Coin and Bitcoin Cash) are selected based on their market capitalisations. Daily data is considered from 30 January 2020 to 19 May 2021. The continuous wavelet transform (wavelet coherence) is used to determine the time-varying co-movement between COVID-19 instances and cryptocurrencies in this research. COVID-19 and cryptocurrency prices are interlinked, as found using the wavelet method. Similar results were discovered for Tether, Binance Coin, and Ripple. Although this seems to be the case, Dogecoin appears to be an alternative investment during COVID-19. The research is unique and adds to the existing body of knowledge, even though some of the results address the function of cryptocurrencies in times of crisis. The research findings indicate that investors and crypto enthusiasts should keep an eye out in the scenario of COVID-19 scenarios when making investments in cryptocurrency marketplaces.
The crypto market is growing rapidly in the post pandemic era. It is expected to grow at a rate 12% compounding per annum in the near future. There is a shift in investors’ interest towards crypto currencies has gained greater significance, Young India investors are keen in exploring newer investment avenues such as Bitcoin, Ethereum, Polygon, etc., which can provide them diversified returns. In India more than 15 million retail investors are currently trading with these digital currencies. The present study aims at examining the volatility in the crypto currencies market with the help of GARCH family models. The most powerful currency the Bitcoin and other currencies like Ethereum and Cardano were considered as samples to understand the volatility in the markets.
When compared to traditional financial markets, cryptocurrencies were seen as assets with minimal correlations. However, because this continually expanding financial market is marked by substantial volatility and strong price movements over a short period, developing an accurate and reliable forecasting model is deemed crucial for portfolio management and optimization. Given the relevance of cryptocurrencies in the global economy, it is important to determine if Bitcoin (BTC) becomes more predictable as investors adopt more aggressive trading positions. We examine BTC over the period from May 15th, 2021, to April 14th, 2022 (8676-time data), using intraday (hourly) time scales. The results reveal that the random walk hypothesis is rejected at lags of 3 to 16 days, while we see that the BTC market tends toward efficiency (see the evolution between lags of 16 and 2). These findings reveal that, given the uncertainty in the global economy in 2022, namely the Russian invasion of Ukraine, the BTC market shows values of the variance ratios close to unity, implying that it is, apparently, not predictable and that the residuals are not autocorrelated in time. In addition, the results of the Detrended Fluctuation Analysis (DFA) exponent show that this market does not exhibit characteristics of (in) efficiency in its weak form. In other words, this market does not have persistent and mean-reverting properties, thus validating the results of Wright’s Rankings and Signs variance test.
The growth of Cryptocurrency has been considered as a future legitimate tender of currency with great possibilities, and it has contributed to lots of different fronts like investments and forms of trading, on the contrary, has caused several troubles.As virtual currencies are developing rapidly, people should comprehend basic concepts and their global influences of them.Our research paper has included the histories and functions with rules and regulations comprehensively.Our goal is to make sure that the audience understands cryptocurrency by the details and examples given and explore further diversification of critical thinking on the topic.We have retrieved lots of resources from articles, websites, and statistical data and discussed insightful analysis to make sure the accuracy is guaranteed.Our study would be beneficial to people with zero understanding of the concept of cryptocurrency.
This article examines the impact of technological changes to cryptocurrency—known as “forking” that triggers blockchain splits—on market conditions. Despite the explicit distinction in log return distributions between the two splitting blockchains, adopting new technology does not result in a disparity in market conditions: no significant difference exists in market efficiency and long‐term market equilibrium between the two splitting blockchains. Technological changes accompanying market separation do not impede the underlying uniformity in market conditions. The findings suggest that mutual information flows linked to market liquidity explain the results between the new and old forks.
This paper investigates the interaction of public information arrivals and volatility in the cryptocurrency market from the perspective of intellectual capital, specifically, relational capital. The empirical analysis was conducted using Kapetanios' unit root test, various scaling (Hurst exponent) tests, a fractionally integrated generalised autoregressive conditionally heteroskedastic model, and Markov regime-switching regression for different series, including the logarithmic returns and abnormal returns, of price and volume series. Following modelling volatility and the derivation of conditional variance, Twitter posts were employed as an independent variable over each series. The results indicate that, while public information arrivals have a positive impact on the volatility of Ripple returns, they cannot divert away the variability of the volume.
Cryptocurrency has become a popular asset in global financial markets, meaning that not only individual investors but also asset management companies around the world are considering this new investment class. The main contribution of this research is to address an intra-day forecasting problem with hourly granularity by comparing deep network architectures, including ones with and attention mechanisms for the Ethereum intrinsic cryptocurrency (ETH). The results showed that the TCN outperformed other architectures considered for a short-term forecast period in terms of processing time and it is amongst the most accurate models using an ARIMA model as a baseline.
The emergency of cryptocurrency has caused a shift in the financial markets. Although it was created as a currency for exchange, cryptocurrency has been shown to be an asset, with investors seeking to profit from it rather than using it as a medium of exchange. Despite being a financial asset, cryptocurrency has distinct, stylised facts like any other asset. Studying these stylised facts allows the creation of better-suited models to assist investors in making better data-driven decisions. The data used in this thesis was of three leading cryptocurrencies: Bitcoin, Ethereum, and Dogecoin and the Johannesburg Stock Exchange (JSE) data as a guide for comparison. The sample period was from 18 September 2017 to 27 May 2021. The goal was to research the stylised facts of cryptocurrencies and then create models that capture these stylised facts. The study developed risk-quantifying models for cryptocurrencies. The main findings were that cryptocurrency exhibits stylised facts that are well-known in financial data. However, the magnitude and frequency of these stylised facts tend to differ. For example, cryptocurrency is more volatile than stock returns. The volatility also tends to be more persistent than in stocks. The study also finds that cryptocurrency has a reverse leverage effect as opposed to the normal one, where past negative returns increase volatility more than past positive returns. The study also developed a hybrid GARCH model using the extreme value theorem for quantifying cryptocurrency risk. The results showed that the GJR-GARCH with GDP innovations could be used as an alternative model to calculate the VaR. The volatile nature of cryptocurrency was also compared with that of the JSE while accounting for structural breaks and while not accounting for them. The results showed that the cryptocurrencies’ volatility patterns are similar but differ from those of the JSE. The cryptocurrency was also found to be an inefficient market. This finding means that some investors can take advantage of this inefficiency. The study also revealed that structural breaks affect volatility persistence. However, this persistence measure differs depending on the model used. Markov switching GARCH models were used to strengthen the structural break findings. The results showed that two-regime models outperform single-regime models. The VAR and DCC-GARCH models were also used to test the spillovers amongst the assets used. The results showed short-run spillovers from Bitcoin to Ethereum and long-run spillovers based on the DCC-GARCH. Lastly, factors affecting cryptocurrency adoption were discussed. The main reasons affecting mass adoption are the complexity that comes with the use of cryptocurrency and its high volatility. This study was critical as it gives investors an understanding of the nature and behaviour of cryptocurrency so that they know when and how to invest. It also helps policymakers and financial institutions decide how to treat or use cryptocurrency within the economy.
<abstract><p>The Bitcoin futures market is growing and, as such, becoming more sophisticated. A small change in price may therefore have a large impact on the market. This paper investigates the propensity of 18 different competing GARCH family models and error distributions to model and forecast the volatility of Bitcoin futures returns. The study employs two different time periods (from January 2, 2018 to June 14, 2021; and March 11, 2020 to June 14, 2021). From the results, iGARCH(1, 1)-Students't-distribution (STD) is selected as the best performing model among the constructed models for the first period. By fitting the best three models from the first period to the second period, the iGARCH(1, 1)-STD is again selected as the optimal model. However, the iGARCH(1, 1)-normal inverse Gaussian (NIG) provides a significant variance forecast when used for in-sample and out-of-sample forecasts before the financial crisis and during the financial crisis, respectively. Our results indicate the impacts of past squared shocks on squared returns of Bitcoin futures and the ability of iGARCH(1, 1)-STD to capture such innovations and the propensity of iGARCH(1, 1)-NIG to optimally forecast the variance of Bitcoin futures returns.</p></abstract>