We explore whether investors would receive excess profits by round-turn trading (hereafter referred to as trading) Bitcoin futures based on Bollinger Bands trading strategy (BBTS). Since investors are suggested to first buy (then sell) Bitcoin futures as oversold (overbought) signals emitted by the BBTS (i.e. penetrating lower (upper) Bollinger Bands regarded as a buying (selling) signal), we aim to explore whether investors would have better returns by trading such futures according to the BBTS. Results show that the average holding period return (AHPR) is over 20% for trading Bitcoin futures following the BBTS. Furthermore, after we adjust the 60-day moving average (MA) instead of the 20-day MA for the BBTS, the AHPR is above 50%. It is noted that if the margin could be deemed as an investment amount, its rate of return would be much higher than the 50% for trading Bitcoin futures.
Mamoona Zahid, Farhat Iqbal, Abdul Raziq, Naveed Sheikh
Using the high-frequency data of Bitcoin, this study aims to model the time-varying volatility identified in the residuals of the heterogeneous autoregressive (HAR) model of realized volatility using the symmetric, asymmetric and long-memory generalized autoregressive conditional heteroscedastic models (GARCH) models. We further extended these models by incorporating jumps and continuous components in the realized volatility estimators and investigating the impact of the inverse leverage effect. The Diebold Mariano and model confidence set test confirm that the forecasting performance of HAR-type models can be effectively improved by these innovations. The long memory HAR-GARCH model with jumps and continuous components provided better forecasting accuracy for Bitcoin volatility as compared to other realized volatility models. The findings of this study may benefit individual investors and risk managers who wish to minimize risks and diversify their portfolios to maximize profits in Bitcoin’s investment.
Since its inception in 2009, Bitcoin has increasingly gained main stream attention from the general population to institutional investors. Several models, from GARCH type to jump-diffusion type, have been developed to dynamically capture the price movement of this highly volatile asset. While fitting the Gaussian and the Generalized Hyperbolic and the Normal Inverse Gaussian (NIG) distributions to log-returns of Bitcoin, NIG distribution appears to provide the best fit. The time-varying Hurst parameter for Bitcoin price reveals periods of randomness and mean-reverting type of behaviour, motivating the study in this paper through fractional Ornstein–Uhlenbeck driven by a Normal Inverse Gaussian Lévy process. Features such as long-range memory are jump diffusion processes that are well captured with this model. The results present a 95% prediction for the price of Bitcoin for some specific dates. This study contributes to the literature of Bitcoin price forecasts that are useful for Bitcoin options traders.
Purpose The purpose of this paper is to study the interlinkages between the cryptocurrency and stock market by characterizing their connectedness starting from January 1, 2018 to December 31, 2021. Design/methodology/approach The author employs a time-varying parameter vector autoregression (TVP-VAR) in combination with an extended joint connectedness approach. Findings The pandemic shocks appear to have influences on the system-wide dynamic connectedness, which reaches a peak during the COVID-19 pandemic. Net total directional connectedness suggests that each cryptocurrency and stock have a heterogeneous role, conditional on their internal characteristics and external shocks. In particular, Bitcoin and Binance Coin are reported as the net receiver of shocks, while the role of Ethereum shifts from receivers to transmitters. As for the stock market, the US stock market stays persistent as net transmitters of shocks, while the Asian stock market (including Hong Kong and Shanghai) are the two consistent net receivers. During the COVID-19 pandemic shock, pairwise connectedness reveals that cryptocurrencies can explain the volatility of the stock markets with the impact most severe at the beginning of 2020. Practical implications Insightful knowledge about key antecedents of contagion among these markets also help policymakers design adequate policies to reduce these markets' vulnerabilities and minimize the spread of risk or uncertainty across these markets. Originality/value The author is the first to investigate the interlinkages between the cryptocurrency and the stock market and assess the influences of uncertain events like the COVID-19 health crisis on the dynamic interlinkages among these two markets. The author employs the TVP-VAR combined with an extended joint connectedness approach.
The main purpose of this study is to reveal whether seasonal/time-oriented/calendar anomalies affect the price and transaction volume of Bitcoin. Day of the week and month of the year anomalies are examined in this context. The data for the years 2013-2021 are handled in 3 different sampling periods, consisting of the whole of this time period and each of its divided parts. The existence of these anomalies is analyzed with EGARCH models created separately. The most important conclusion reached in this study is that the analyzed anomalies differ according to the sampling periods. The common findings reached as a result of the analyzes for all three time intervals are as follows: It has been determined that Monday has positive effects in terms of both Bitcoin return and transaction volume, while Saturday has negative effects only regarding transaction volume. Mondays, Tuesdays, and Wednesdays create volatility-increasing effects concerning returns, Friday, Saturday and Sunday reduce volatility. In terms of trading volume, Monday and Tuesday reduce volatility, while Thursday and Friday increase volatility. Whereas March has a positive effect on return volatility, it has a negative effect on trading volume volatility, and September has only a negative effect on return volatility.
The objectives of this research are 1) to study the relationship of people interested in investing in cryptocurrencies; 2) to identify people interested in cryptocurrencies. This research study is quantitative research by survey research using an online questionnaire data collection method of 402 respondents. The research results found that Most of them are Generation Z people interested in investing in cryptocurrencies and low to middle-income groups, most of whom are interested in BTC and ETH, the well-known crypto-currency groups. The analysis was then divided by K-Means Clustering analysis into three groups, namely "Not interested in cryptocurrencies", "Moderate-interest" and "Risk-takers" that interested in investing in cryptocurrencies. The last group has a selection to invest in a variety of cryptocurrencies, especially SOL, BNB and ADA, which are smart contract coins.
We tracked the impact of the Covid-19 crisis on the liquidity of10 crypto currencies for the period from July 31, 2019 (before the crisis) to December 31, 2020 (in the Covid -19 era). We applied the vector error correction model to each crypto currency. The results show that in the short term, the COVID-19 crisis has no influence on the liquidity of cryptocurrencies except for Cardano. Similarly, in the long term, it has no impact on the liquidity of cryptocurrencies with the exception of Binance coin, Tezos and Cardano. The assumption of having a common liquidity factor implies that, in a shock of liquidity, the entire market will be affected. The cryptocurrency market, however, has proven to be different.
In this paper, we investigate the role of Bitcoin as a safe haven against the stock market losses during the spread of COVID-19. The performed analysis was based on a regression model with dummy variables defined around some crucial dates of the pandemic and on the dynamic conditional correlations. To try to model the real dynamics of the markets, we studied the safe-haven properties of Bitcoin against thirteen of the major stock market indexes losses using daily data spanning from 1 July 2019 until 20 February 2021. A similar analysis was also performed for Ether. Results show that this pandemic impacts on the Bitcoin status as safe haven, but we are still far from being able to define Bitcoin as a safe haven.
Renan Gomes Mendes Diniz, Diogo de Prince, Leandro Maciel
Purpose The aim of this paper is to test the existence of bubbles for the daily prices of cryptocurrencies Bitcoin and Ethereum and verify if there is a relationship between bubbles and volatility regimes. Design/methodology/approach The authors test the presence of bubbles with the generalized supremum augmented Dickey–Fuller (GSADF) test using critical values simulated by the bootstrap procedures of Gutierrez (2011), Harvey et al. (2016) and Pedersen and Schütte (2020). Also, the authors estimate Markov regime switching generalized autoregressive conditional heteroskedasticity model for these cryptocurrencies. Findings The GSADF test result indicates the presence of bubbles for both cryptocurrencies. Simulating critical values by wild-bootstrap, which is robust to non-stationary volatility, leads to the highest number of bubbles in both cryptocurrencies. In addition, based on the estimates of conditional variance models with regime changes, the authors find that the bubbles identified are associated with a regime of low returns volatility, indicating a change in the trade-off between risk and return when the prices of cryptocurrencies differ from their fundamental values. Originality/value To the best of the authors knowledge, there are no studies that test the explosive behavior for cryptocurrencies by the GSADF test using the bootstrap method to simulate critical values from the procedures of Harvey et al. (2016) or Pedersen and Schütte (2020). These bootstrapping procedures are robust to heteroscedasticity and avoid the detection of false bubbles. Further, the advantage of Harvey et al. (2016) procedure is the robustness to non-stationary volatility.
Purpose This study examines the turn-of-the-month (TOM) effect in Bitcoin (BIT), Ethereum (ETH) and Litecoin (LIT) cryptocurrencies from August 2015 to August 2021. Design/methodology/approach Dummy regression model is used to examine the presence of the TOM effect and to test the efficiency of the cryptocurrency market. The characteristics of the returns during TOM days are compared with that of the non-non-TOM trading days. The authors also develop a trading strategy to earn abnormal returns using the TOM effect. Findings The authors show that TOM returns are positive and significantly higher than that of non-TOM returns. Interestingly, the authors empirically show that the TOM effect is not driven by the day-of-the-week (DOW) effect or the January effect. Based on the significant TOM effect, the authors formulate a trading strategy that annually outperforms the buy-and-hold strategy for BIT by 21.77% and for LIT by 47.10%. Finally, the results are robust to using a Generailzed Auto Regressive Conditional Heteroskedasticity (GARCH) (1,1) model and the January 2018 sell-off. Practical implications The results have important implications for both traders and investors. The findings suggest that the investors might be able to earn excess profits by timing their positions in BIT and LIT taking the advantage of the TOM effect. Originality/value First, the authors provide the only study to report the evidence of the TOM effect in three leading cryptocurrencies, viz., BIT, LIT and ETH. Second, the authors control for the DOW effect and the January effect while investigating the TOM effect in cryptocurrency market. Finally, this study develops a trading strategy based on which the investors can time the cryptocurrency markets as indicated by the pattern of the TOM effect during the studied time period.
Uniswap, as with other DEXs, has gained much attention this year because it is a non-custodial and publicly verifiable exchange that allows users to trade digital assets without trusted third parties. However, its simplicity and lack of regulation also make it easy to execute initial coin offering scams by listing non-valuable tokens. This method of performing scams is known as rug pull, a phenomenon that already exists in traditional finance but has become more relevant in DeFi. Various projects have contributed to detecting rug pulls in EVM compatible chains. However, the first longitudinal and academic step to detecting and characterizing scam tokens on Uniswap was made. The authors collected all the transactions related to the Uniswap V2 exchange and proposed a machine learning algorithm to label tokens as scams. However, the algorithm is only valuable for detecting scams accurately after they have been executed. This paper increases their dataset by 20K tokens and proposes a new methodology to label tokens as scams. After manually analyzing the data, we devised a theoretical classification of different malicious maneuvers in the Uniswap protocol. We propose various machine-learning-based algorithms with new, relevant features related to the token propagation and smart contract heuristics to detect potential rug pulls before they occur. In general, the models proposed achieved similar results. The best model obtained accuracy of 0.9936, recall of 0.9540, and precision of 0.9838 in distinguishing non-malicious tokens from scams prior to the malicious maneuver.
In the research, the causal relationships between Bitcoin, gold and oil prices were examined. The data of the research covers the period from 2015 to July 2020 and consists of daily price values. Augmented Dickey-Fuller Unit Root Test was used to see whether the stochastic process changes with time. Bitcoin and gold series do not contain a unit root since the oil series is stationary at the level while the difference is stationary. The reason why the series containing unit roots are not stationary is due to structural breaks or not, was investigated by Bai-Perron Unit Root Test with Multiple Structural Breaks. According to the test, it was determined that the Bitcoin series has one break and two regimes, while the gold series has two structural breaks and three different regimes. Whether the research series are cointegrated or not was investigated with the Gregory and Hansen test. The causality between the series was examined with the Toda-Yamamoto causality test, which is based on the VAR (Vector Autoregression) model and examines the causality in the series regardless of the unit root. A two-way causality relationship was determined between the eight lag-long Gold series and the Bitcoin series. In other cases, a causal relationship has not been established. As a result, we give an evidence that Bitcoin and gold prices series followed a parallel pattern while with oil not. Therefore, investors can add Bitcoin into their portfolios to make balance of the risk and return.
Predicting the future price of the currency has always been considered one of the most challenging issues. In this paper, we utilize different artificial neural networks (ANNs), including Feedforwardnet, Fitnet, and Cascade networks, and predict the future price of Bitcoin. This paper discusses how a combination of technical attributes, like price-related and lagged features, as inputs of the neural networks, are used to raise the prediction capabilities that directly impact into the final profitability. For empirical analysis, this paper uses the data of the Bitcoin price for a period of 9 months (1.1.2018 - 30.9.2018) available on http://www.coindesk.com. Using a ten-fold cross-validation method, this paper finds the optimal number of hidden neurons for different train functions in each ANN based on error measures, including mean squared error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE). Then, the Bitcoin price is predicted, and results are compared based on the amount of R to find out which ANN leads to a better prediction. Finally, this paper concludes that the Fitnet network with trainlm function and 30 hidden neurons outweighs the others. This paper assesses the models’ performance and how specific setups produce principled and stable predictions for beneficial trading.
The last few years have seen a paradigm shift in the financial sector with the development of cryptocurrencies as an alternative mode of payment as well as an investment scheme. The aim of this study is two-fold. The first is to quantify the volatility of cryptocurrencies in terms of the dynamics of tail-end behavior using different approaches and choose the one with the lowest value-at-risk. The second is to investigate the effect of its inclusion in a portfolio with and without gold, to see if Bitcoin is indeed the “digital gold”. This paper uses the generalized simulated annealing optimization technique to compare portfolios for ten countries across the world. The data provide convincing evidence in favor of the inclusion of Bitcoin in the optimized portfolios. Rolling-window analyses (three-year and five-year) confirm the same. However, for some countries, the empirical pattern suggests that instead of replacing gold from the portfolio, both should be comprised. Our results are robust in terms of the inclusion of non-linear constraints.