In the market of cryptocurrency the Bitcoins are the first currency which has gain the significant importance. To predict the market price and stability of Bitcoin in Crypto-market, a machine learning based time series analysis has been applied. Time-series analysis can predict the
In recent years, the attention of investors, practitioners and academics has grown in cryptocurrency. Initially, the cryptocurrency was designed as a viable digital currency implementation, and subsequently, numerous derivatives were produced in a range of sectors, including nonmonetary activities, financial transactions, and even capital management. The high volatility of exchange rates is one of the main features of cryptocurrencies. The article presents an interesting way to estimate the probability of cryptocurrency volatility clusters. In this regard, the paper explores exponential hybrid methodologies GARCH (or EGARCH) and through its portrayal as a financial asset, ANN models will provide analytical insight into bitcoin. Meanwhile, more scalable modelling is needed to fit financial variable characteristics such as ANN models because of the dynamic, nonlinear association structure between financial variables. For financial forecasting, BP is contained in the most popular methods of neural network training. The backpropagation method is employed to train the two models to determine which one performs the best in terms of predicting. This architecture consists of one hidden layer and one input layer with N neurons. Recent theoretical work on crypto-asset return behavior and risk management is supported by this research. In comparison with other traditional asset classes, these results give appropriate data on the behavior, allowing them to adopt the suitable investment decision. The study conclusions are based on a comparison between the dynamic features of cryptocurrencies and FOREX Currency’s traditional mass financial asset. Thus, the result illustrates how well the probability clusters show the impact on cryptocurrency and currencies. This research covers the sample period between August 2017 and August 2020, as cryptocurrency became popular around that period. The following methodology was implemented and simulated using Eviews and SPSS software. The performance evaluation of the cryptocurrencies is compared with FOREX currencies for better comparative study respectively.
The paper explores the role, evolution and ruling principles of the concept of “money” in the 21st Century. In this continuously evolving context, cryptocurrencies and Blockchain technology are widely considered the most relevant monetary innovations of the last decades. By means of a macro-founded logical-analytical approach combined with statistical evidence, the paper provides arguments: 1. dismissing the “innovation myth” behind cryptocurrencies because of de facto representing a comeback of the private issue of means of payments and, more problematically, seigniorage at its best; 2. confirming that crypto-tokens do not comply with basic, still ruling monetary principles; 3. suggesting that excess liquidity is already invested in crypto-markets (which are themselves “inflationary”, namely not backed by real value (i.e. GDP). The concrete risk is, once again in economic history, represented by facing a financial bubble.
A model is proposed for Bitcoin prices that takes into account market attention. Market attention, modeled by a mean-reverting Cox-Ingersoll-Ross processes, affects the volatility of Bitcoin returns, with some delay. The model is affine and tractable, with closed formulae for the conditional characteristic functions with respect to both the conventional and a delayed filtration. This leads to semi-closed formulae for European call and put prices. A maximum likelihood estimation procedure is provided, as well as a method for changing to a risk-neutral measure. The model compares very well against classical and attention-based models when tested on real data.
Saiful Izzuan Hussain, Nurulkamal Masseran, Nadiah Ruza, Muhammad Aslam Mohd Safari
Abstract Extreme value theory(EVT) has been used to study the frequency and probability related to extreme situations in finance. This approach focuses on the extreme values and able to provide a better estimation for risk models. In this study, Generalized Pareto Distribution (GPD) is employed to model daily extreme returns in the Bitcoin market from 2017 to 2019. These periods have witnessed three phases of extreme volatility for the cryptocurrency market. The returns level for the Bitcoin range between 17.011 and 18.746. The results demonstrate heavy tail and finite tail distribution characteristics for the tails. The findings provide a better understanding of the tails’ behaviour in the cryptocurrency market and help investors to make a financial decision.
Khalid Khan, Jiluo Sun, Sinem Derindere Köseoğlu, Ashfaq U. Rehman
This study examines the relationship between global economic policy uncertainty (GEPU) and bitcoin prices (BCP) employing the rolling window method. The full sample test shows that there is no causality between GEPU and BCP. However, the full sample causal relationship between the variables can be different when considering structural changes. The finding of the rolling window test indicates that there is causality in different subsamples. It has found both positive and negative bidirectional causalities between GEPU and BCP across various subsamples. The decision makers need to accelerate the development of blockchain technology that can be used for hedging and portfolio diversification. Moreover, enacting laws and regulations on state interventions and prohibitions ensures investor confidence. Information about policy changes should be incorporated into portfolio selection to avoid random market fluctuations. Its unregulated nature makes it more turbulent in the short term and has undergone sudden changes, so investors should be able to obtain comprehensive information about global economic and policy changes. Policy makers should ensure investor confidence by making legal regulations on state interventions and prohibitions.
Recently, the world of cryptocurrencies has experienced an undoubted increase in interest. Since the first cryptocurrency appeared in 2009 in the aftermath of the Great Recession, the popularity of digital currencies has, year by year, risen continuously. As of February 2021, there are more than 8525 cryptocurrencies with a market value of approximately USD 1676 billion. These particular assets can be used to diversify the portfolio as well as for speculative actions. For this reason, investigating the daily volatility and co-volatility of cryptocurrencies is crucial for investors and portfolio managers. In this work, the interdependencies among a panel of the most traded digital currencies are explored and evaluated from statistical and economic points of view. Taking advantage of the monthly Google queries (which appear to be the factors driving the price dynamics) on cryptocurrencies, we adopted a mixed-frequency approach within the Dynamic Conditional Correlation (DCC) model. In particular, we introduced the Double Asymmetric GARCH–MIDAS model in the DCC framework.
In recent years, cryptocurrency or virtual currency is becoming an essential medium of exchange in consumer and domestic trading. Nevertheless, the trading values of cryptocurrency compared to real money are very uncertain and can change dramatically. This article is aimed to assess the uncertainty or volatility of cryptocurrencies, mostly on Bitcoin. In the digital currencies market, Bitcoin is a widely accepted currency. Other digital currencies of the market may influence Bitcoin. For example, Ethereum, Litecoin, Zcash, Monero, Dash and Ripple have a positive impact on Bitcoin. Previous research only focuses on Bitcoin and other markets such as stock markets, energy markets, and exchange rates. However, here we focus on interlinkages and volatility dynamics within cryptocurrency markets by applying some econometrics models. In this article, we have shown that the relationship between Bitcoin and other currencies can be modelled in the ARCH, GARCH, VAR and MGARCH framework. Forecast values of the GARCH (3,3) model are given very close to the original data. VAR stability result shows that the model is stable. Using the CCC, VCC, and DCC of the MGARCH model on daily returns from 1st January 2017 to 15th March 2019, we found significant volatility and strong correlations between the variables.
Dirk Gerritsen, Rick A.C. Lugtigheid, Thomas Walther
Using a hand-collected dataset containing bullish, neutral, and bearish predictions for Bitcoin published by crypto experts, we show that neutral and bearish predictions are followed by negative abnormal returns whereas bullish predictions are not associated with nonzero abnormal returns. Based on all outstanding predictions, we compute prediction revisions relative to (i) the latest issued prediction and (ii) the outstanding consensus prediction. Downward revisions are followed by negative abnormal returns. We conclude that crypto experts are skilled information intermediaries on the Bitcoin market.
This study explores the bubble behavior in the prices of top five cryptocurrencies (i.e., Bitcoin, Ethereum, Ripple, Stellar, and Tether) using daily data of the closing level at the COVID-19 pandemic, covering the period from January 2, 2020 to January 2, 2021. The testing procedure of the bubble behavior in selected cryptocurrencies prices is investigated by two methodologies. Those covers the test statistics originated by the Supremum Augmented Dickey-Fuller (SADF) (Phillips et al., 2011) and Generalized Supremum Augmented Dickey-Fuller (GSADF) (Phillips et al., 2015) to define several bubble periods. The empirical results emphasize that bubble behavior is not a diverse and stable feature of Bitcoin, Ethereum, Ripple, and Stellar prices, except the Tether prices, which point out the emergence of a potential crisis in the digital assets market through an increasing degree of financial instability.
This discussion applies quantitative finance methods and economic arguments to cryptocurrencies in general and bitcoin in particular -- as there are about $10,000$ cryptocurrencies, we focus (unless otherwise specified) on the most discussed crypto of those that claim to hew to the original protocol (Nakamoto 2009) and the one with, by far, the largest market capitalization. In its current version, in spite of the hype, bitcoin failed to satisfy the notion of "currency without government" (it proved to not even be a currency at all), can be neither a short nor long term store of value (its expected value is no higher than $0$), cannot operate as a reliable inflation hedge, and, worst of all, does not constitute, not even remotely, a safe haven for one's investments, a shield against government tyranny, or a tail protection vehicle for catastrophic episodes. Furthermore, bitcoin promoters appear to conflate the success of a payment mechanism (as a decentralized mode of exchange), which so far has failed, with the speculative variations in the price of a zero-sum maximally fragile asset with massive negative externalities. Going through monetary history, we show how a true numeraire must be one of minimum variance with respect to an arbitrary basket of goods and services, how gold and silver lost their inflation hedge status during the Hunt brothers squeeze in the late 1970s and what would be required from a true inflation hedged store of value.
We apply quantitative finance methods and economic arguments to cryptocurrencies in general and bitcoin in particular -- as there are about $10,000$ cryptocurrencies, we focus (unless otherwise specified) on the most discussed crypto of those that claim to hew to the original protocol (Nakamoto, 2009) and the one with, by far, the largest market capitalization.
In its current version, in spite of the hype, bitcoin failed to satisfy the notion of without (it proved to not even be a currency at all), can be neither a short nor long term store of value (its expected value is no higher than $0$), cannot operate as a reliable inflation hedge, and, worst of all, does not constitute, not even remotely, a safe haven for one's investments, a shield against government tyranny, nor a tail protection vehicle for catastrophic episodes.
Furthermore, there appears to be an underlying conflation between the success of a payment mechanism (as a decentralized mode of exchange), which so far has failed, and the speculative variations in the price of a zero-sum asset with massive negative externalities.
Going through monetary history, we also show how a true numeraire must be one of minimum variance with respect to an arbitrary basket of goods and services, how gold and silver lost their inflation hedge status during the Hunt brothers squeeze in the late 1970s and what would be required from a true inflation hedged store of value.
Paulo Rupino da Cunha, Paulo Melo, Hélder Sebastião
We analyze the path from cryptocurrencies to official Central Bank Digital Currencies (CBDCs), to shed some light on the ultimate dematerialization of money. To that end, we made an extensive search that resulted in a review of more than 100 academic and grey literature references, including official positions from central banks. We present and discuss the characteristics of the different CBDC variants being considered—namely, wholesale, retail, and, for the latter, the account-based, and token-based—as well as ongoing pilots, scenarios of interoperability, and open issues. Our contribution enables decision-makers and society at large to understand the potential advantages and risks of introducing CBDCs, and how these vary according to many technical and economic design choices. The practical implication is that a debate becomes possible about the trade-offs that the stakeholders are willing to accept.
This study is an integrated survey of GARCH methodologies applications on 67 empirical papers that focus on cryptocurrencies. More sophisticated GARCH models are found to better explain the fluctuations in the volatility of cryptocurrencies. The main characteristics and the optimal approaches for modeling returns and volatility of cryptocurrencies are under scrutiny. Moreover, emphasis is placed on interconnectedness and hedging and/or diversifying abilities, measurement of profit-making and risk, efficiency and herding behavior. This leads to fruitful results and sheds light on a broad spectrum of aspects. In-depth analysis is provided of the speculative character of digital currencies and the possibility of improvement of the risk–return trade-off in investors’ portfolios. Overall, it is found that the inclusion of Bitcoin in portfolios with conventional assets could significantly improve the risk–return trade-off of investors’ decisions. Results on whether Bitcoin resembles gold are split. The same is true about whether Bitcoins volatility presents larger reactions to positive or negative shocks. Cryptocurrency markets are found not to be efficient. This study provides a roadmap for researchers and investors as well as authorities.
In today's technology-oriented world, electronic devices are becoming increasingly important for everyone. Because of this, the emergence of cryptocurrency seems natural. Thus, there is an urgent need to understand the impact brought by cryptocurrency to the world financial system. As a type of currency, how will the cryptocurrency influence the other types of traditional currency? We use data related to Bitcoin to illustrate the connection between those two types of currency. By applying machine learning to the data, we found out that there is scarcely any correlation between Bitcoin value and conventional currency value, with the exception of the USD. With this result, we hope to contribute to the establishment of a better currency system and therefore, provide the world with a healthier economic environment. At the same time, the economic society may get a clearer understanding of the interrelationship between the new currency---Bitcoin and several typical examples of conventional currency. Meanwhile, throughout the research, we can acknowledge the main difference on investing strategies between conventional currencies and cryptocurrencies, which will therefore, help us make a wiser decision when it comes to the purchases of money power.
Hashem Abdullah AlNemer, Besma Hkiri, Muhammad Asif Khan
This study attempts to investigate the nexus between investor sentiment and cryptocurrencies prices. Our empirical investigation merges bivariate and multivariate wavelet tools to examine the investor sentiment nexus to inter-cryptocurrencies prices. The study outcomes show that the Sentix Investor Confidence index provides significant information in explaining long-term changes in Bitcoin and Litecoin prices. Moreover, the findings generated from the multiple wavelet coherence illustrate the simultaneous contribution of cryptocurrencies and the Sentix Investor Confidence index in explaining the Bitcoin index movement across frequencies and over horizons, especially during bubble burst periods. The study also suggests a time-dependent relationship of Bitcoin prices with alternative cryptocurrencies and the Sentix Investor Confidence index, mostly pronounced during the Bitcoin bubble. We discuss our results using GSV-based investor sentiment. Our findings remain robust and confirm the strong predictive power of investor sentiment in cryptocurrencies price movements over time and across scales.
The volatility of bitcoin (BTC) and time horizon is the center point for investment decisions. However, attention is not often drawn to the relationship between BTC and equity indices. Thus, the purpose of this paper is to investigate the volatility and time frequency domain of BTC with stock markets.
This study examines the interaction of Bitcoin with fiat currencies of three developed (euro, pound sterling and yen) and three emerging (yuan, rupee and ruble) market economies. Empirical investigations are executed through symmetric, asymmetric and non-linear causality tests, and Markov regime-switching regression (MRSR) analysis. Results show that Bitcoin has a causal nexus with Chinese yuan and Indian rupee for price and various return components. The MRSR analysis justifies these findings by demonstrating the presence of interaction in contractionary regimes. Accordingly, it can be stated that when markets display a downward trend, appreciation of the Chinese yuan and Indian rupee positively and strongly affects the value of Bitcoin, possibly due to the market timing. The MRSR analysis also exhibits a transition from a tranquil to a crisis regime in March 2020 because of the pandemic. However, a shorter duration spent in the crisis regime in 2020 indicates the limited and relatively less harmful effect of the pandemic on the cryptocurrency market when compared to the turmoil that occurred in 2018.
Since the creation of Bitcoin, the adequacy of data in the cryptocurrency market has not been widely analysed by scholars. Indeed, the research conducted by Alexander and Dakos (2020) is the only one that has focused on the properties and differences of several data sources, underlining inconsistencies in the time series of prices. In our paper, we contribute to this strand of the literature by examining one of the main features of digital currencies: the cryptocurrency market never sleeps. Given that cryptocurrencies trade on a 24/7 basis, specialised crypto companies offer two kinds of prices (close and weighted prices) to proxy Bitcoin daily prices. However, scholars and practitioners have not considered this issue in their analyses. We show that these prices are statistically different, which affects the financial decisions of investors and the most relevant fields in the cryptocurrency market (efficiency, risk management and volatility forecasting). Therefore, our paper demonstrates that the data processing used by specialised crypto firms is a relevant issue that changes the underlying mechanism of Bitcoin data, affecting the results of investors and scholars.