Abstract Compares ARMA models, boosting, neural network models, HAR_RV models and proposes a new method for predicting one day ahead realized volatility of financial series. HAR_RV models are taken as compared classical volatility prediction models. In addition, the phenomenon of transfer learning for boosting and neural network models is investigated. Bitcoin and E-mini S&P500 are chosen as examples. The realized volatility is calculated based on intraday (intradayâ24 hours) data. The calculation is based on the closing values of the internal five-minute intervals. Comparisons are made both within and between the two intervals. The intervals considered are January 1, 2018âJanuary 1, 2022 and January 1, 2018âApril 2, 2023. Since there were structural changes in the markets during these intervals, the models are estimated in sliding windows of 399 days length. For each time series, we compare three-parameter enumeration boosting, about 10 different neural network architectures, ARMA models, the newly proposed CTCM method, and various training transfer and training sample expansion options. It is shown that ARMA and HAR_RV models are generally inferior to other listed methods and models. The CTCM model and neural networks of CNN architecture are the most suitable for financial time series forecasting and show the best results. Although transfer learning shows no improvement in terms of forecast precision and yields little decline. It requires more extensive and detailed study. The smallest MAPEs for Bitcoin and E-mini S&P500 realized volatility forecasts are achieved by the newly proposed CTCM model and are 21.075%, 25.311% on the first interval and 21.996%, 26.549% on the second interval, respectively.
Purpose: Bitcoin, the most popular form of virtual currency, currently holds the highest market capitalization among cryptocurrencies and serves as a benchmark for the typical cryptocurrency. The main goal of this research is to evaluate Bitcoinâs potential as a distinct asset class. This will be achieved by building upon previous studies and investigating its utility as both a hedging instrument and a tool for portfolio diversification. Methodology: In this study, Bitcoin is compared with other asset classes, such as key stock indices of Indiaâs Nifty-50 and Sensex, and key currency pairs with the Indian Rupee, including the US dollar ($), Euro (âŹ), Pound sterling (ÂŁ), and Japanese Yen („). Gold, as one of the most precise commodities, is analyzed using descriptive statistics to verify and confirm its properties as a distinct asset class. Additionally, the study employs the DCC-GARCH model to ascertain whether Bitcoin qualifies as both a hedging instrument and a tool for portfolio diversification. Findings: The findings of this study indicate that Bitcoins constitute a unique and separate category within alternative assets and investment classes. Various descriptive statistics confirm that Bitcoins exhibit characteristics of an asset class. Additionally, the study reveals and verifies the hedging and portfolio diversification capabilities of Bitcoin based on the results of the DCC-GARCH model. Practical Implications: The findings of this study will prove useful for investors considering cryptocurrency (Bitcoin) as an alternative asset class for diversifying their portfolios and hedging against volatility. Originality/Value: This study contributes to the research paradigm of Bitcoin finance by providing a perspective from a developing nation on Bitcoin as an asset class, which differs from other asset classes such as Nifty-50, Sensex, USDâINR, EURâINR, GBPâINR, JPYâINR, and gold. While previous research has predominantly focused on developed nation contexts, this study underscores the importance of examining Bitcoinâs role in portfolio diversification and hedging strategies. To enhance our understanding, this research presents daily observations of recent economic data spanning from 2011 to 2021. Assessing whether Bitcoin qualifies as an alternative investment and a distinct asset class is crucial, as it could significantly influence investment decisions and serve as a valuable tool for risk management and diversification purposes for investors.
Abstract The main objective of this study is to investigate tail risk connectedness among six major cryptocurrency markets and determine the extent to which investor sentiment, economic conditions, and economic uncertainty can predict tail risk interconnectedness. Combining the Conditional Autoregressive Value-at-Risk (CAViaR) model with the time-varying parameter vector autoregressive (TVP-VAR) approach shows that the transmission of tail risks among cryptocurrencies changes dynamically over time. During crises and significant events, transmission bursts and tail risks change. Based on both in- and out-of-sample forecasts, we find that the information contained in investor sentiment, economic conditions, and uncertainty includes significant predictive content about the tail risk connectedness of cryptocurrencies.
Taicir Mezghani, Mustafa Raza Rabbani, Yousra Trichilli, BoujelbĂšne Abbes
The study investigates the dynamic interconnections and opportunities for hedging among cryptocurrency, commodity, and Islamic stock markets using DCC-GARCH and Spillover connectedness models. Using daily data covering the Russia-Ukraine war and COVID-19 outbreak from December 1, 2019 to April 15, 2022, we document weak and frequently negative correlation between Bitcoin and Islamic stock markets. Thus, Bitcoin could be viewed as a haven from Islamic stock market losses. The results also indicate that Bitcoin's diversification benefits are normally steady and increase considerably during turbulence. Furthermore, the net return spillovers from the Bitcoin market remain above zero during most of the study period. We also find that utilizing Bitcoin as a hedge during the COVID-19 pandemic phase leads to higher expenses. The outcomes of this investigation are expected to carry substantial ramifications for Indonesian investors and portfolio managers who adhere to Shariah law since they will enable them to comprehend the advantages of diversifying portfolios across various periods of stock holding or investment horizons.
Abstract COVID-19 has significantly influenced global financial markets, including Bitcoin. Recent studies have focused on investigating the first wave of the COVID-19 outbreak and accounting for market changes, which were mostly due to the pandemic. This research not only analyzes the contagion effects of COVID-19 but also considers aftermath events beyond the first pandemic wave to examine spillovers of Bitcoin. The study employs Diebold and Yilmazâs method to explore the static and dynamic spillovers of the selected variables and identifies several major global events, including crypto-specific affairs, macroeconomic policies, and geopolitical conflicts, to explain the new market dynamics of Bitcoin using network analysis. The findings identify a few high-contagion periods related to Bitcoin. The paper also found that Bitcoin is more likely to produce extreme returns and is more connected to other markets. Contagion effects âfromâ and âtoâ other markets are asymmetrical in terms of arrival time and market response. Bitcoin is more likely to be affected by other markets in extreme situations and receives spillovers from them sooner than it transmits spillovers to others. In the context of various global events, impacts arising from developed countries are stronger. China still has some impact on cryptocurrency markets, but they are waning. Bitcoin is thus not a safe haven from the shocks of global events, but can sometimes work as a hedge or diversifier. The results offer alternative explanations for Bitcoinâs different market dynamics and enrich our understanding of Bitcoinâs safe haven, hedge, and diversifier properties within a diversified portfolio.
Imran Yousaf, Shoaib Ali, mohamed marei, Mariya Gubareva
This study investigates the static and dynamic interdependence of the Islamic cryptocurrency and metal markets using the TVP-VAR methodology. The empirical findings suggest that Islamic cryptocurrencies are the recipients of both return and volatility spillovers, while most metals act as transmitters of these spillovers. The dynamic spillovers are intensified in the COVID-19 period compared to the pre-COVID-19 period. We find that the return connectedness is short-lived lived whereas the volatility connectedness is a long-term phenomenon. Finally, we also compute the optimal weights, hedge ratios, and hedging effectiveness during COVID-19. The results suggest that investors should add Islamic cryptocurrencies to metals portfolios in order to get maximum risk-adjusted returns during the pandemic crisis. Our findings are helpful for portfolio managers and investors in making decisions regarding diversification, portfolio allocation, forecasting, and hedging.
The purpose of the current paper is to propose new conceptual framework of oil price fluctuation on cryptocurrencies, the study analyze previous literature to evaluate the impact of energy prices and Cryptocurrencies return. The study suggest that analysis should extract the spam to conclude the period before, during and after corona pandemic. While suggestion to conduct weekly analysis to provide highly query and reliability of results. According to huge number of cryptocurrencies exploded onto the scene and has grown at an ever-increasing rate, the study propose to adopt only highest four crypto capital in 2023.Analyzes the daily returns of highly capital cryptocurrencies which are Bitcoin (BTC), Ethereum (ETH), Ripple (XRP), and Litecoin (LTC), and their correlations with crude oil (COR), Brent crude (BCR), and natural gas (N GR). The data is analyzed using cointegration tests, ARDL methodology, bounds test, and E-views software to check the relationships between independent and dependent variables. The study aims to identify evidence of a long-term relationship between the variables and estimate the relationship in the short and long term.
Cryptocurrency is one of the most notable financial innovations in recent years. The present study provides a novel methodology to understand the level of awareness regarding cryptocurrencies among investors under different market regimes. The study used a survey questionnaire for a sample of 352 investors in sample Asian countries (India, Japan, South Korea, Singapore, Saudi Arabia, Bahrain, Oman, and Dubai). The study has developed eight hypotheses that have been tested considering independent (Statement of Responses About cryptocurrency-SORAC) and dependent variables (Market Factors-MF, Social Sentiment Factors-SF, Technological Factors-TF, Stocks-ST, Bank Deposits-BD) by incorporating t-test, regression, and correlation. The results revealed that cryptocurrency has a significant and positive influence on dependent variables among investors under different market regimes. The results from the analytical findings pave the way for the policymakers of these countries to help design robust mechanisms to regulate their cryptocurrencies under different market regimes.
Praise Otito Obanya, Modisane Seitshiro, Carel P. Olivier, Tanja Verster
Cryptocurrencies are widely regarded as volatile and less predictable assets by financial participants. The behaviour and dynamics of Bitcoinâs daily volatility, obtained by fitting GARCH models, are investigated for a period of 8 years using permutation entropy which is represented by the variable H for calculations. The best fitting GARCH models selected are the FIGARCH(1,0.7,1) and SGARCH(1,1) models based on maximum likelihood estimation, Akaike Information Criterion and Bayesian Information Criterion. Simulated volatilities are also obtained from the best fitting GARCH models using their respective parameters, to confirm how well the models fit. The results obtained show that the H values of Bitcoin are generally low and that the dynamics of Bitcoinâs volatility is quite predictable, as Bitcoinâs volatility is most likely to decline over time than increase or have an alternating movement. Also, the simulated volatilities show good agreement with the real-world volatility, confirming the models as good fits.
Amro Saleem Alamaren, Korhan K. GökmenoÄlu, Nigar TaĆpınar
Abstract This study investigates volatility spillovers and network connectedness among four cryptocurrencies (Bitcoin, Ethereum, Tether, and BNB coin), four energy companies (Exxon Mobil, Chevron, ConocoPhillips, and Nextera Energy), and four mega-technology companies (Apple, Microsoft, Alphabet, and Amazon) in the US. We analyze data for the period November 15, 2017âOctober 28, 2022 using methodologies in Diebold and Yilmaz (Int J Forecast 28(1):57â66, 2012) and BarunĂk and KĆehlĂk (J Financ Economet 16(2):271â296 2018). Our analysis shows the COVID-19 pandemic amplified volatility spillovers, thereby intensifying the impact of financial contagion between markets. This finding indicates the impact of the pandemic on the US economy heightened risk transmission across markets. Moreover, we show that Bitcoin, Ethereum, Chevron, ConocoPhilips, Apple, and Microsoft are net volatility transmitters, while Tether, BNB, Exxon Mobil, Nextera Energy, Alphabet, and Amazon are net receivers Our results suggest that short-term volatility spillovers outweigh medium- and long-term spillovers, and that investors should be more concerned about short-term repercussions because they do not have enough time to act quickly to protect themselves from market risks when the US market is affected. Furthermore, in contrast to short-term dynamics, longer term patterns display superior hedging efficiency. The net-pairwise directional spillovers show that Alphabet and Amazon are the highest shock transmitters to other companies. The findings in this study have implications for both investors and policymakers.
This paper analyses the high-frequency intraday Bitcoin dataset from 2019 to 2022. During this time frame, the Bitcoin market index exhibited two distinct periods, 2019-20 and 2021-22, characterized by an abrupt change in volatility. The Bitcoin price returns for both periods can be described by an anomalous diffusion process, transitioning from subdiffusion for short intervals to weak superdiffusion over longer time intervals. The characteristic features related to this anomalous behavior studied in the present paper include heavy tails, which can be described using a $q$-Gaussian distribution and correlations. When we sample the autocorrelation of absolute returns, we observe a power-law relationship, indicating time dependence in both periods initially. The ensemble autocorrelation of the returns decays rapidly. We fitted the autocorrelation with a power law to capture the decay and found that the second period experienced a slightly higher decay rate. The further study involves the analysis of endogenous effects within the Bitcoin time series, which are examined through detrending analysis. We found that both periods are multifractal and present self-similarity in the detrended probability density function (PDF). The Hurst exponent over short time intervals shifts from less than 0.5 ($\sim$ 0.42) in Period 1 to closer to 0.5 in Period 2 ($\sim$ 0.49), indicating that the market has gained efficiency over time.
Digital currencies and coins are methods of computer-generated currency which uses cryptography for safety and operate individually of a dominant authority, like governments and economic institution. They are spread out and usually utilize blockchain technology to note transactions strongly. Cryptocurrencies such as Bitcoins, Ethereum, and some others have grown popularity in the latest eons for their latent to deliver borderless, secure, and fast transactions. Yet, they also arisen with threats like security concerns, regulatory uncertainty, and price volatility. It is vital for operators to conduct detailed research and comprehend the threats included before capitalizing or utilizing cryptocurrencies. Even though cryptocurrencies and coins have grown famous and are being gradually used for countless transactions, it is significant to remind that they still have not passed the traditional banking systems. Traditional banking sectors still perform an important part in the worldwide economic system, offering services like payment processing, savings accounts, and lending. Conversely, the growth of cryptocurrencies has directed to conferences about the possible influence on the financing sector and the necessity for traditional banking systems to acclimate to the varying setting of digital economics. It is important to observe these growths closely to comprehend the evolving association among the crypto-currencies and system of traditional banking. Hence, the current study gives the deep knowledge in the usage of crypto currencies, digital coins and their role in financial sectors that is dominating the traditional banking sector. And analyzed the impact of crypto currencies and digital coins on investors and economy of the nation and also regarding the easy accessibility of finance.
Muneer Shaik, Mustafa Raza Rabbani, Mohd Atif, Ahmet Faruk Aysan · 6 authors
We investigate the dynamic volatility connectedness of geopolitical risk, stocks, bonds, bitcoin, gold, and oil from January 2018 to April 2022 in this study. We look at connectivity during the Pre-COVID, COVID, and Russian-Ukraine war subsamples. During the COVID-19 and Russian-Ukraine war periods, we find that conventional, Islamic, and sustainable stock indices are net volatility transmitters, whereas gold, US bonds, GPR, oil, and bitcoin are net volatility receivers. During the Russian-Ukraine war, the commodity index (DJCI) shifted from being a net recipient of volatility to a net transmitter of volatility. Furthermore, we discover that bilateral intercorrelations are strong within stock indices (DJWI, DJIM, and DJSI) but weak across all other financial assets. Our study has important implications for policymakers, regulators, investors, and financial market participants who want to improve their existing strategies for avoiding financial losses.
Abstract The main objective of this paper is to forecast the realized volatility (RV) of Bitcoin futures (BTCF) market. To serve our purpose, we propose an augmented heterogenous autoregressive (HAR) model to consider the information on time-varying jumps observed in BTCF returns. Specifically, we estimate the jump-induced volatility using the GARCH-jump process and then consider this information in the HAR model. Both the in-sample and out-of-sample analyses show that jumps offer added information which is not provided by the existing HAR models. In addition, a novel finding is that the jump-induced volatility offers incremental information relative to the Bitcoin implied volatility index. In sum, our results indicate that the HAR-RV process comprising the leverage effects and jump volatility would predict the RV more precisely compared to the standard HAR-type models. These findings have important implications to cryptocurrency investors.
Purpose We investigated the association of COVID-19 indicators and economic uncertainty indices on payment-based system cryptocurrency (i.e. Bitcoin, Ripple and Dogecoin) returns. Design/methodology/approach We used an autoregressive distributed lag (ARDL) model for panel data and performed robustness checks by utilizing a random effect model (REM) and generalized method of moments (GMM). There are 25 most adopted cryptocurrencyâs countries and the data spans from 22 March 2021 to 6 May 2022. Findings This research discovered four findings: (1) the index of COVID-19 vaccine confidence (VCI) recovers the economic and Bitcoin has become more attractive, causing investors to shift their investment from Dogecoin to Bitcoin. However, the VCI was revealed to be insignificant to Ripple; (2) during uncertain times, Bitcoin could perform as a diversifier, while Ripple could behave as a diversifier, safe haven or hedge. Meanwhile, the movement of Dogecoin prices tended to be influenced by public figuresâ actions; (3) public opinion on Twitter and government policy changes regarding COVID-19 and economy had a crucial role in investment decision making; and (4) the COVID-19 variants revealed insignificant results to payment-based system cryptocurrency returns. Originality/value This study contributed to verifying the vaccine confidence index effect on payment-based system cryptocurrency returns. Also, we further investigated the uncertainty indicators impacting on cryptocurrency returns during the COVID-19 pandemic. Lastly, we utilized the COVID-19 variants as a cryptocurrency returnsâ new determinant.