This paper studies two cryptocurrencies and finds that their prices can be estimated or forecasted better than their returns because returns being ratios of prices, do not always exhibit the economic relationship that may exist between two price series. However, average returns use multiple prices in their ratios that capture the economic behavior of the price series. Further, the forecasting performance of traditional preceding return models are compared with those of preceding average return models and the latter are found to generally give better results in terms of Root Mean Square Error (RMSE) and average return on investments (ARoIs).
ABSTRACT Recent literature explores the profitability of various cryptocurrency momentum trading strategies and proposes cryptocurrency momentum as a pricing factor (Liu et al.). How risky is this factorâbased investment strategy for cryptoâinvestments? We answer this question by examining the distributional characteristics (hence, riskiness) of six cryptocurrency momentum trading strategies. The empirical evidence suggests that the realised variances of cryptocurrency momentum strategies are governed by power laws. The statistical tests derived from block bootstraps indicate that the population mean and variance of the momentum factor realised variances are statistically not defined. Contrary to the belief that cryptocurrency momentum trading strategies produce generous payoffs, our results imply that, in real life, we might not be able to realise these risk premiums. We conclude that the performance metrics evaluating the profitability of cryptocurrency momentum strategies, using variance as an input, are not informative. We also find crossâsectional dependence amongst the tail risk of momentum strategies based on different formation periods.
Abstract Despite recent studies focused on comparing the dynamics of market efficiency between Bitcoin and other traditional assets, there is a lack of knowledge about whether Bitcoin and emerging markets efficiency behave similarly. This paper aims to compare the market efficiency dynamics between Bitcoin and the emerging stock markets. In particular, this study indicates whether the dynamics of Bitcoin market efficiency mimic those of emerging stock markets. Thus, the paper's contribution emerges from the combination of Bitcoin and emerging markets in the field of dynamics of market efficiency. The dynamics of market efficiency are measured using the Hurst exponent in the rolling window. The study uses daily data for the MSCI Emerging Markets Index and the Bitcoin market over the period 2011â2022. Our results show that there is at most a moderate correlation between the dynamics of Bitcoin and emerging stock marketsâ efficiency over the entire study period. The strongest correlations occur mainly in periods of high economic policy uncertainty in the largest Bitcoin mining countries. Therefore, the association between Bitcoin market efficiency and emerging stock marketsâ efficiency may strengthen with an increase in economic policy uncertainty. These findings may be useful for investors and portfolio managers in constructing better investment strategies.
This paper investigates the profitability of Bitcoin and US equity. More concretely, we inspect the performances of the S&P 500 index and Bitcoin by comparing their returns and volatilities. As a result, we obtain the following significant findings. First, our regression analysis clarifies that for the period after the sudden appearance of COVID-19, there was a weak nexus between the S&P 500 index and Bitcoin returns. In addition, our return and return spread analysis evidences that for this period, on average, Bitcoin returns were much higher than the S&P 500 index returns. Moreover, our volatility and volatility spread analysis reveals that for this period, on average, the volatilities of Bitcoin returns were much higher than those of the S&P 500 index returns.
Virtual assets and currency sector are becoming increasingly intertwined.According to new IMF research, the correlation of crypto assets with traditional holdings like equities has increased dramatically as usage has grown, limiting their risk perception investment opportunities, and raising the danger of spillover across financial markets.Theoretical and empirical findings concerning cryptocurrencies and stock market behaviour have been misleading thereby putting policy makers at a crossroads.This paper therefore examines the response of stock market to investment in cryptocurrencies in the US stock market.Monthly data covering the period between February 2016 to February 2022 was used.The answer was achieved using novel dynamic autoregressive-distributed lag (ARDL) simulation techniques along with the Breitung and Candelon causality test.Findings revealed that cryptocurrencies impacted positively on the US stock market.Secondly, investment in Bitcoin and Ethereum is a good predictor of stock market while no evidence of causality between investment in ripple and stock market indices in the US stock market.Thirdly, a long-run relationship exists between investment in cryptocurrencies and behaviour of stock market indices in the United State, and that investment in cryptocurrencies has a significant long-run increasing effect on stock prices in United State.
Bitcoin was presented at the end of 2008 but the question still remains whether it is a form of money or something entirely different. Bitcoin was not designed with the aim to create money in a strict sense but primarily with the intention to make the transfer of value as effective as possible. Yet, Bitcoin has a capacity to take on the role of money, and that capacity was recognized in court cases. In this regard, the paper presents the results of the primarily empirical but also theoretical research conducted previously on the volatile but still very deflationary nature of Bitcoin and its effect on monetary obligations. The idea that cryptocurrencies can be also used as a hedging instrument to prevent the negative effects of domestic currency depreciation might be controversial for a number of reasons, one of which is certainly the volatile nature of bitcoin "price". We stress that periodic depreciation of its value does not mean that bitcoin is inflationary. On the contrary, bitcoin is deflationary by nature, which is evident in different in-built mechanisms and new ways of application. In this paper, the author uses different analytical method techniques to single out and describe various deflatory mechanisms, both preprogramed and factual ones. The author also applies the synthetical method and its techniques, primarily abstraction and generalization, to sum up the data confirming the main hypothesis that bitcoin is by nature deflationary despite its volatility and, therefore, it can be used as a hedging mechanism.
Ta-Cheng Chang, Wei-Ying Nie, Hsuan-Ling Chang, KuangâChieh Yen
We examine how economic policy uncertainty (EPU) influences realized variance dependency and tail-risk synchronization across major cryptocurrencies. Using 5-min high-frequency returns to construct realized variance and signed jump variance measures, we document that global and Western EPU (the US, UK, France) significantly strengthen both variance dependency (VD) and signed jump variance dependency (SJVD) among the top 15 cryptocurrencies, whereas Asian EPUs exhibit weaker and less consistent effects. The sensitivity of SJVD is particularly pronounced, reflecting the asymmetric transmission of tail risk during uncertainty shocks. These findings remain robust after controlling for Bitcoinâs realized volatility and hold in post-COVID subsample analysis. Our results suggest that cryptocurrency markets exhibit greater systemic interconnectedness and heightened tail-risk co-movements during periods of elevated policy uncertainty, with important implications for risk management and financial stability monitoring.
Abstract The high returns of cryptocurrencies have attracted many investors in recent years. At the same time the evolution of cryptocurrencies is characterized by extreme volatility. For investors, it is therefore key to gauge the risks related to an investment in cryptocurrencies. We provide a comparison of several GARCH and stochastic volatility models for forecasting the risk of cryptocurrencies over the out-of-sample period from 28.09.2018 to 28.02.2023. It turns out that the widely used GARCH(1,1) does not provide accurate risk predictions. In contrast, adding t -distributed innovations or allowing for regime changes improves the accuracy in both model classes. Finally, we consider a Bayesian decision-guided approach with discount learning to combine the different models and provide robust evidence that combining the model predictions leads to accurate combined risk predictions.
As an investor, volatility plays an important role in decision making. It is defined as the rate at which a securityâs price increases or decreases, i.e., shows pricing behavior during a definite span of time. A high volatility will lead to high risk. Thus, it becomes critical to determine the volatility and the risk-return trade-off among investments. This paper tries to document the volatility and risk-return trade-off of four prominent crypto-currencies (Bitcoin, Ethereum, Binance and Ripple), based on market-capitalization. For analysis, closing prices of cryptocurrencies has been accumulated through secondary method for 365 days, starting from 1st March 2022 and ending on 28th February 2023. Standard Deviation and Kurtosis, used together for volatility and risk assessment, documented that Bitcoin has the highest volatility and risk associated with expected returns. Regression, for assessing the impact of volatility in BTC price on others, derived that ETH has a strong, but not very strong, bivariate relationship with BTC, among all the pairs. Durbin Watson (DW) test concluded that there was no auto-correlation in the prices of crypto-currencies, i.e., previous dayâs price does not play significant role in todayâs price. For risk-return trade-off, Coefficient of Variation (CoV) has been applied. It determined that Ethereum has the highest ratio indicating its non-suitability to a conservative investor because of having the lowest returns as compared to risks involved; while Binance has the lowest Coefficient of Variation (CoV) depicting lower risk and maximum return among all.
Industry 4.0 is the current and developing environment which has led to the evergrowing use of disruptive technology in all areas of life, including finance and investment.Cryptocurrency appeared on the surface of capital markets in 2008, as one of the greatest innovations of our century.The study shows that cryptocurrencies have their own niche in payment systems; they are highly competitive and dependable financial instruments.The growth dynamics of cryptocurrency market capitalization in the world makes Bitcoin the most successful example of the use of virtual currency in the information economy.Our country's economy should follow the path of innovation in finding solutions to a number of technical, economic and legal issues concerning the development of the cryptocurrency market in India through involving the experience of the leading countries.The study also assesses how the financial industry uses Cryptocurrency to enhance the efficiency and wealth of investors as the alternative for the traditional investment avenues.Cryptocurrency has an enormous propensity to improve an investor's risk-yield profile.The paper substantiates opportunities and perspectives for the development of the future of Indian cryptocurrency market.
The question of how to benefit from an organic combination of gold and bitcoin has become a prominent topic in the contemporary society. Hence, we've built the time series forecasting models and target planning models of gold and bitcoin, providing the best gold and bitcoin rotation investing strategy based on our methodology. We consider the connection between gold and bitcoin price fluctuations by creating the SVM-GARCH Combination Model, and at the same time, data-based nonlinear feature extraction and heteroscedasticity processing give a more accurate and dependable foundation for investment decision making.In terms of investment planning, We first utilized VaR to clarify our quantitative investment risk indicators, and then built a VaRY Model to organically integrate and balance investment returns and risks. At the same time, we include Risk Adjustment Parameters into the planning model so that, by dynamic weight adjustment, our target planning model can match the wealth utility propensity of investors with diverse risk preferences, therefore improving the model's application and flexibility. Finally, in view of the differences in trading restrictions between Trading Days and Non-trading Days, we formulate different dynamic weights - Multi-objective Programming Models for trading and non trading periods, so that our best investment decision can be more comprehensive and targeted.We present proof for the brilliance of our investment strategy in four dimensions by merging and assessing the forecasting model and the planning model: Accuracy, Rationality, Flexibility, and High Return.
This paper tests the safe-haven property of Bitcoin for South African stocks using Full and Diagonal BEKK-GARCH models. The study uses the Johannesburg stock exchange Top40 index, and bitcoin returns data before COVID-19 (August 2018 to December 2019) and during COVID-19 (January 2020 to June 2021). The results show that bitcoin cannot be considered as safe-haven for stocks in South Africa since it is weakly correlated with stock and had a high volatility during the Pandemic. Therefore, the safe-haven hypothesis of bitcoin on South African stocks is not true for the period under study. The policy implication is that bitcoin is not an appropriate safe-haven asset on South African stocks because it lacks store of value properties.
Abstract Price discovery studies of a single asset traded in multiple markets have traditionally focused on assessing the relative price discovery contribution of each market. However, in this paper, we demonstrate that the overall price discovery across all markets can undergo changes even when the relative price discovery of each market remains constant. We propose that this overall change in price discovery can be effectively captured by the fractional parameter in the fractionally cointegrated vector autoregressive (FCVAR) model. In contrast, the widely used cointegrated vector autoregressive (CVAR) model fails to account for this dynamic in overall price discovery. Through a combination of simulation exercises and empirical applications, we show that the FCVAR approach outperforms the CVAR model not only in evaluating the relative price discovery contributions but also, more importantly, in providing a comprehensive measurement of overall price discovery.