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 security’s 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 day’s price does not play significant role in today’s 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.
This paper uses the wavelet coherence approach and the wavelet-based Granger causality test, to investigate the effect of the five waves of the COVID-19 pandemic on Bitcoin, Ethereum, BNB, Cardano, Ripple, Dogecoin, TRON, Litecoin, Stellar, and Bitcoin Cash in a time-frequency framework from 22 January 2020 to 22 February 2022. The results show the presence of correlation between the COVID-19 pandemic and cryptocurrencies in the short-medium term, and a positive impact on Bitcoin only during the first wave of the pandemic in the medium term. However, Cardano failed to act as a risk diversifier. In the long-term, our analysis shows that Ethereum, BNB, Ripple, Dogecoin, TRON, Litecoin, Stellar, and Bitcoin Cash proved their ability as strong safe haven assets, even during different periods of the COVID-19 crisis. Our results can provide helpful information for policymakers, and cryptocurrency market main and hedge funds managers during periods of uncertainty.
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.
The Covid-19 pandemic affected financial markets in several ways, influencing the dynamics of the relationships between asset classes. We investigate the connectedness between cryptocurrencies and international energy markets from 2018 to 2021 using the time-varying parameter vector autoregression approach. Net total directional connectedness suggests that the cryptocurrency and energy indexes had heterogeneous roles. Bitcoin and Ripple coin were the net receivers of shocks, while Ethereum switched from receiver to transmitter. The US energy market was a persistent net transmitter of shocks, while Asian energy markets were consistent net shock receivers. Pairwise connectedness reveals that cryptocurrencies can explain the volatility of the energy markets during the difficult period of the pandemic at the beginning of 2020. We provide insights for portfolio optimization and policy implications.
This study will investigate the liquidity spillover effects of five cryptocurrencies: Bitcoin, Ether, Binance-coin, Ripple, and Tether. Firstly, the researcher utilizes the Amihud illiquidity ratio to quantify the liquidity performance of the five currencies, which we treat as weekly for the purposes of our study due to data collecting constraints. Secondly, to quantify the liquidity spillover effect in the cryptocurrency market over the period of 2017-2022, the researcher employs Diebold and Yilmaz's spillover index. The results identify the senders and receivers of liquidity spillovers on an individual and pairwise basis for the five major currencies and demonstrate the presence of time variation. Additionally, this paper evaluates the news report-based cryptocurrency uncertainty index (UCRY). This includes the price of cryptocurrencies (UCRY price) and the uncertainty surrounding cryptocurrency policy (UCRY policy). Considering the constructed index follows the same path as the largest cryptocurrency, Bitcoin, it is therefore recommended that the Bitcoin price can be used to forecast the cryptocurrency uncertainty index. Overall, this study has filled a gap in the literature by conducting research on liquidity spillovers in cryptocurrency markets, and it presents some preliminary conclusions. However, in order to verify the validity of our findings and to provide more meaningful results, additional research is required over a longer time horizon and with additional cryptocurrency types.
Abstract This article examines the relationship between Bitcoin volume and term deposit investments in Mexico, Indonesia, Nigeria, and Turkey (MINT) from 2016 to 2021. We run cointegration and error-correction econometric models for each country, analyzing both the long-term and short-term interactions between Bitcoin volume and time deposits. Our findings indicate a negative association between Bitcoin volume and term deposits in all the MINT countries, except Mexico. This suggests that individual investors in economically and financially unstable nations are increasingly turning to Bitcoin as an alternative investment option. The observed effects, while currently modest, highlight the potential threats posed by decentralized cryptocurrencies to the monetary systems of emerging economies, impacting the stability of the banking industry and overall economic growth.
The objective of this paper is to select the appropriate GARCH model fit for analysing the volatility dynamics of the Tunisian sectoral stock market indices and Bitcoin during the COVID-19 outbreak period as well as to examine the Bitcoin diversification benefits. On using four models (EGARCH, FIGARCH, FIEGARCH, and TGARCH) and mean-variance spanning test, our findings prove that following the COVID-19 outbreak, the consumer service, financial and distribution, industrial, basic materials and banking sectors' return volatilities tend to have a relatively high positive and significant asymmetric effect, as compared to the pre-COVID period. Similarly, the results reveal that the Bitcoin proves to bring about significant diversification benefits once incorporated into a well-diversified benchmark portfolio, predominantly throughout the COVID-19 outbreak. Overall, our results could be of great benefit to investors seeking to account for any future volatility and implement special hedging strategies under COVID-19 crisis.
This paper examines the impact of Bitcoin futures introduction on the crash risk of spot Bitcoin prices. Using both time-series regression with a time dummy and a difference-in-differences (DID) framework, we find that crash risk, proxied by the negative conditional skewness (NCSKEW) and down-to-up volatility (DUVOL) of 5-minute intraday Bitcoin returns, declines significantly after the launch of Bitcoin futures. Robustness checks confirm that the findings are robust to changes in control variables, control cryptocurrencies, the sampling frequency for high-frequency returns, and an extended post-introduction period. Furthermore, we explore the moderating roles of market liquidity and investor attention. The crash-mitigating effect of Bitcoin futures is significantly more pronounced in periods of low liquidity and limited investor attention, suggesting that futures markets play a stronger role in enhancing information efficiency under such conditions.HighlightsThis paper examines whether Bitcoin futures introduction increases or decreases Bitcoin price crash risk.The price crash risk of Bitcoin, measured by NCSKEW and DUVOL from high-frequency intraday data, decreases significantly after futures introduction.The main findings are robust to changes in control variables, control cryptocurrencies, the sampling frequency for high-frequency returns, and an extended post-introduction period.The crash-mitigating effect is more pronounced in periods of low liquidity and limited investor attention.
Bitcoin and Ethereum are the top two cryptocurrencies in the first and the second places respectively. This study looks to examine the inter and intra dynamics and relationship between Bitcoin price (BTCP), Ethereum price (ETHP), Bitcoin volume (BTCV) and Ethereum volume (ETHV). This study utilizes the Johansen Cointegration Test as well as the Vector Error Correction Model (VECM) to determine the long-run relationship between Bitcoin price (BTCP), Ethereum price (ETHP), Bitcoin volume (BTCV) and Ethereum volume (ETHV) before and during the COVID-19 pandemic and to determine whether the pandemic has any effect on the changes in prices and volumes of these cryptocurrencies. The study also utilizes daily data extracted from coinmarketcap.com for Bitcoin price and volume as well as for Ethereum price and volume from August 8, 2015 up to February 28, 2021 extracted on March 1, 2021. We find that the COVID-19 pandemic has no effect on the long run relationship between Bitcoin price (BTCP), Ethereum price (ETHP), Bitcoin volume (BTCV) and Ethereum volume (ETHV) for all the specifications. We also find that the pandemic has no effect on the prices of Bitcoin and Ethereum but has an effect on their trading volumes in the short run. We find that the price of Bitcoin is positively related with the Bitcoin trading volume and positively related with the trading volume of Ethereum whereas the Ethereum price is negatively related with the Bitcoin trading volume and positively related with the trading volume of Ethereum. We also find that the price of Bitcoin is positively related with the trading volume of Bitcoin and negatively related with the trading volume of Ethereum. On the other hand, the price of Ethereum is positively related with trading volume of Bitcoin and positively related with the trading volume of Ethereum.
This study focuses to analyse the association between bitcoin and three prominent energy commodities (Oil, Gasoline and Natural gas) utilising quantile auto-regressive distributed lags (QARDL). The results indicated that energy prices hold a positive influence on the bitcoin market in long-run on extreme market conditions (bearish and bullish market state). In short-terms, the results failed to find the significant role of natural gas and gasoline prices in driving bitcoin returns. The findings of causality-in-quantile reported the presence of the uni-directional causal relationship from energy commodities to bitcoin. Interestingly, the findings reported that in bullish market, the causal link between crude oil and bitcoin exhibits feedback effect indicating that when the market is generating higher returns, both the critical variables drive the returns of each other. The outcomes have implication for investors regarding the relevance of energy commodities with bitcoin and their contribution in its valuation to support efficient risk management.
Shinta Amalina Hazrati Havidz, Ni Putu Indah Rahmadani, Priscilla Laura Aditya Tori
This research was conducted to determine whether gold and cryptocurrency (i.e., Bitcoin) can be used as safe haven assets for oil, wheat, stock index (SI), government bond (GB), Islamic stock (IS), and Islamic bond (IB) during the Russia-Ukraine war. We used panel quantile regression by utilizing extreme lower quantiles (i.e., 1%, 2.5%, 5%). It will only be recognized as a safe haven asset if it is negatively correlated with another asset during extreme adverse shocks. The data spans from 23 February 2021 – 25 July 2022 which covered the five largest economies in Europe and Asia (i.e., Germany, France, the UK, China, and Japan). The findings indicate that gold only acted as a safe haven asset for wheat, SI, and IS during the Russia-Ukraine war. Additionally, Bitcoin only serves as a safe haven asset for oil, wheat, SI, and GB during the Russia-Ukraine war.
In this paper, we compare the predictive power of Auto Regressive Integrated Moving Averages (ARIMA) and Multi-Layer Perceptron Artificial Neural Networks (MLP ANN) model to short-term forecast the monthly returns of Bitcoin cryptocurrency. We evaluate the performance of two models using time series with monthly data from January 2018 to December 2021. The key parameters for the final assessment of prognostic models are the values of Root Mean Square Error-RMSE and Forecast Error-FE. The results of the short-term BTC return forecast showed better properties of composite compared to univariate time series forecasting models, i.e., higher prognostic power of the MLP ANN model compared to the selected ARIMA (1,1,3) model (lower RMSE and FE). The results point to further comparative research of prognostic models and the possibility of forming more complex and hybrid structures of neural network models in order to predict economic phenomena as accurately as possible.