Bitcoin is one of the cryptocurrencies that had a high rate of return since its appearance in 2009. However, the exchange rate of Bitcoin against any foreign currency is considered to have high volatility making it difficult to determine the real value of Bitcoin. The main purpose of this research is to find the value of Bitcoin, especially US Dollar and Rupiah currencies. The test is carried out using the weak market efficiency hypothesis and the semi-form market coefficient hypothesis. The data processing methods are used the stationary test (ADF, KPSS, and ERS) to test the efficiency of the weak form market and the cointegration test (Johansen Cointegration) with the VECM model to check the efficiency of the semi-strong market. The results show that the Bitcoin exchange rate does not have a unit root so it is inefficient in a weak form and has a negative effect on the USD / IDR exchange rate so that it is not efficient in semi-strong form as well as on the US Dollar and Rupiah exchange rates. This happens because Bitcoin transactions as a medium of exchange in Indonesia are still illegal. So that the Bitcoin exchange rate against the US Dollar and Rupiah exchange rates is biased because it does not reflect the available information, both historical information and public information. KeywordsâBitcoin Exchange Rate; Market Efficiency; Unit Root; Cointegration
Bitcoin has gained a tremendous amount of attention lately because of the innate nature of entering cryptographic technologies and money-related units in the fields of banking, cybersecurity, and software engineering. This chapter investigates the effect of Bayesian neural structures or networks (BNNs) with the aid of manipulating the Bitcoin process's timetable. The authors also choose the maximum extensive highlights from Blockchain records that are carefully applied to Bitcoin's marketplace hobby and use it to create templates to enhance the influential display of the new Bitcoin evaluation process. They endorse actual inspection to check and expect the Bitcoin technique, which compares the Bayesian neural network and other clean and non-direct comparison models. The exact tests show that BNN works well for undertaking the Bitcoin price schedule and explain the intense unpredictability of Bitcoin's actual rate.
This paper sets under scrutiny whether the S&P500, oil, and Twitter-based uncertainty about financial markets affect the returns and volatility of three major cryptocurrencies.Estimations are conducted concerning Bitcoin, Bitcoin Cash, and Dogecoin during the first wave of the COVID-19 pandemic.Findings document that Twitter uncertainty exhibits a weaker impact on cryptocurrencies than the S&P500 and crude oil.S&P500 constitutes a positive and significant determinant while impacts of oil are weaker and mixed.The volatility of cryptocurrencies is found to display a non-linear character.Moreover, it is revealed that Dogecoin could be more useful to investors as a speculative tool than Bitcoin and Bitcoin Cash.These outcomes inform the interested reader that traditional investments are influential in a much larger degree towards modern financial assets than investor sentiment when economic conditions are stressed.
Since its launch in 2009, bitcoin has thrived, attracting the attention of investors, regulators, academia, and the public in general. Its price dynamics, characterized by extreme volatility, severe jumps, and impressive long-term appreciation, suggest that bitcoin is a new digital asset. This study presents a comprehensive overview of the fractality of bitcoin in a high-frequency framework, namely by applying Multifractal Detrended Fluctuation Analysis (MF-DFA) and a Multifractal Regime Detecting Method (MRDM) to Bitstamp 1 min bitcoin returns from January 2013 to July 2020. The results suggest that bitcoin is multifractal, with smaller and larger fluctuations being persistent and anti-persistent, respectively. Multifractality comes from significant long-range correlations, which cast some doubts on the informational efficiency at this frequency, but mainly comes from fat-tails, which highlights the significant risks undertaken by investors in this market. Our most important result is that the degree and richness of multifractality is time-varying and increased after 2017, when volumes and prices experienced an explosive behaviour. This complexity puts into perspective the duality of bitcoin: while it is characterized by long-run attractiveness and increasing valuation, it also has a high short-run instability. Hence, this study provides some empirical evidence supporting the relationship between these two observable features.
Introduction of the Exchange Traded Funds (ETFs) in 1989 in the USA added another category to the bunch of asset classes that continues to gain increasing popularity not only with the Asset Management Companies but also the potential investors who had been looking eagerly for innovative ways of diversifying their investment portfolio. Among a host of theme-based ETFs which offer varied investment strategies and returns, Bitcoin ETFs are of relatively recent origin and have caught investorsâ attention as a substitute for investment in cryptocurrency directly or through derivatives. Bitcoin ETFs have appeared on the horizon designing products backed by Bitcoin and Bitcoin backed structured products like Bitcoin futures etc. At the same time, there is a growing class of unregulated Bitcoin ETF, Trusts and other financial products that track the value of Bitcoin and trade on traditional market exchanges rather than cryptocurrency exchanges. Also, there is growing another asset class of Blockchain ETFs, operating many a time as a proxy for Bitcoin/Bitcoin ETFs that invest in companies having direct or indirect exposure to Blockchain. The instant paper attempts to examine the fundamentals of Bitcoin and Blockchain ETFs, Blockchain technology, the factors throwing up Bitcoin ETFs in to prominence and the issues relating to their legal recognition by the regulators. It also attempts to provide a governance model for effective regulation of the two without mutual overlapping either in investorsâ comprehension or in practice.
Abstract Economic theory predicts the price dynamics of an unbacked asset to be inherently unforecastable. The same applies to exchange rates of unbacked currencies. Albeit, empirically investors are found to be driven by online and offline news media. This study analyzes the Bitcoin cryptocurrency price series and web search queries with regard to their mutual predictability and causeâeffect delay structure. Chinese Baidu engine searches and compounded BaiduâGoogle search statistics predict Bitcoin price dynamics at relatively high frequencies ranging from 2 to 5 months. In the other direction, Grangerâcausality runs from the cryptocurrency price to queries statistics across nearly all frequencies. In both directions, the reaction time computed from a phase delay measure for the relevant frequency bands with significant causality ranges from about 1 to 4 months. For either direction, outâofâsample forecasts are more accurate than forecasts of a benchmark stochastic process. Bivariate models including the Baidu Search Index slightly outperform competing models that include a BaiduâGoogle composite index. Predictive power seems less diluted if the September 2017 trade regulations by the Chinese government are controlled for.
The world first decentralized currency - cryptocurrency brings us the new era of a mode of exchange. It has been included many technologies like bitcoin, ethereum, and hyper ledger. Due to its rising popularity and demand, people are often curious to know the future price of these coins to make a good deal with them. The future price of bitcoin would help investors as well as corporate to get an overview of the demand and role in the economy. Many researchers have investigated various solutions that will predict the future price of bitcoin. But, the solutions achieved low accuracy. This paper main aim is to propose a prediction model that will predict the future price of bitcoin. The model is based on the deep learning approaches. The proposed model included four different deep learning models. These models are Long Short-Term Memory (LSTM), Bidirectional LSTM, Gated Recurrent Unit (GRU), and Bidirectional GRU. The performance of the prediction models is computed and it found Bi-GRU gave the best-predicted results.
Karl Oton Rudolf, Samer Zein, Nicola Jackman Lansdowne
Volatility and investor sentiment have been factors for the slow adoption rate of Bitcoin (BTC) that was first recognized in 2008 as a potential store of value, investment vehicle and a hedge alternative to gold during a recession. The purpose of this applied mathematics study will use a multivariate DCC GARCH model. Bitcoin holds its ground in volatility. This study examines Bitcoin as an investment and hedge alternative to gold as well as the major stock index. To perform the research to explore the viability of Bitcoin as an investment and hedge alternative to gold, the authors conducted a DCC GARCH model analysis. The findings of this research paper confirm Bitcoinâs cyclical performance between volatility and adoption. The findings give a strong ground for Bitcoin as the new digital currency, store of value, medium of exchange, and a unit of account and incentivize further research by theorists, scholars and examiners. The significance of this applied mathematics research and analysis will allow an unstoppable, incorruptible, and uncontrollable store of value, and investment vehicle, without governmental or institutional intervention. This study contributes by comparing and contrasting volatility stability based on the return levels of each Bitcoin on major indexes traded with BTC (based on fiat currencies) and gold.
A bibliometric analysis of Bitcoin remains of high importance as it helps to cope with emerging trends. Compared to the existing literature, this paper additionally covers the keywords and analysis of the main topics, provides status-quo information and an insight on the future direction of Bitcoin research. We used VOSviewer to perform a network analysis of the most commonly used keywords in the literature. The results of the network analysis have shown that there are four main cluster groups of keywords in different topics. Also, we used R studio to form a matrix that presented the associations and correlation coefficients between the words most frequently used in combination in abstracts of scientific articles. Bibliography analysis has shown that the main topics dealt with in Bitcoin-related literature in 2020-2021 are volatility, gold, inefficiency, safe-haven, and hedge with a potential future increase of analysis as a safe-haven. By using these results, other researchers could optimize their research by concentrating on a specific topic they are interested in and they can use the relevant keywords we have highlighted for each topic. Furthermore, we have distinguished the most active authors in each field, so that researchers could find potential collaborators in each topic, and we also established the most suitable journals for publishing. Our paper not only optimizes all research processes but also reveals the most relevant topics and identifies future direction.
This paper investigates the hedging effectiveness of Bitcoin (BTC) future contract using daily settlement prices for the period of 1 January 2018 until 26 March 2021. Standard OLS regressions, Error Correction Model (ECM), as well as GARCH and EGARCH models are used to estimate the optimal hedge ratio which is necessary for trading and risk management. The findings indicate that the time varying hedge ratios, if estimated through the Error Correction Model (ECM), are more efficient than the fixed hedge ratios in terms of risk minimization.
In this paper we analyze the existence of cointegrating relationships between Bitcoin, S&P 500, and the quantity of money M2. We perform our analysis with and without applying time warping pre-processing. In all cases we find strong evidence that, in the period 2016-2021 the three time series show two cointegrating relationships and therefore share a common stochastic trend. In addition, a low correlation between Bitcoin and S&P 500 is detected. These finding justify the increased interest of investors in Bitcoin as an alternative asset class. The economic interpretation is that the stock valuation is primarily determined by financial phenomena, in particular the availability of large quantity of money. Money supporting investment is due both to the actions of Quantitative Easing and to the exchange of creditor/debtor role that took place between households and firms. The price of both Bitcoin and stocks is increasingly influenced by the amount of money in circulation and follows the same stochastic trend.
Yuze Li, Shangrong Jiang, Yunjie Wei, Shouyang Wang
Abstract The emergence and growing popularity of Bitcoins have attracted the attention of the financial world. However, few empirical studies have considered the inclusion of the newly emerged commodity asset in the global commodity market. It is of great importance for investors and policymakers to take advantage of this asset and its potential benefits by incorporating it as a part of the broad commodity trading portfolio. In this study, we propose a novel ensemble portfolio optimization (NEPO) framework utilized for broad commodity assets, which integrates a hybrid variational mode decomposition-bidirectional long short-term memory deep learning model for future returns forecast and a reinforcement learning-based model for optimizing the asset weight allocation. Our empirical results indicate that the NEPO framework could effectively improve the prediction accuracy and trend prediction ability across various commodity assets from different sectors. In addition, it could effectively incorporate Bitcoins into the asset pool and achieve better financial performance compared to traditional asset allocation strategies, commodity funds, and indices.
The COVID-19 pandemic has raised great attention to the study of its impacts on Bitcoin. We focus on the impacts of the COVID-19 pandemic on the long memory and efficiency of Bitcoin. There exist a few studies on this topic. These studies all ignore the issues of heavy tails and extreme events during COVID-19, which are obstacles to obtaining the reliable continuous time-varying results of long memory and efficiency. After considering the two issues, we first obtain the reliable continuous time-varying results during COVID-19 via sliding window and estimation of Hurst exponent. The other four markets (Ethereum, Binance Coin, S&P 500, and gold spot) are also analysed for comparison. Bitcoin results show that the Bitcoin market keeps efficient during the pandemic and the heavy tails become weaker after the onset of the pandemic. Results of the comparison study show that Bitcoin has similar efficiency with spot gold and is more efficient than Ethereum, Binance Coin, and S&P 500 during the pandemic. This study contributes to current rare literature on the long memory and efficiency of cryptocurrency during COVID-19.
Daniel Traian Pele, Niels Wesselhöfft, Wolfgang Karl HÀrdle, Michalis Kolossiatis · 5 authors
This research provides insights for the separation of cryptocurrencies from other assets. Using dimensionality reduction techniques, we show that most of the variation among cryptocurrencies, stocks, exchange rates, commodities, bonds, and real estate indexes can be explained by the tail, memory and moment factors of their log-returns. By applying various classification methods, cryptocurrencies are categorized as a separate asset class, mainly due to the tail factor. The main result is the complete separation of cryptocurrencies from the other asset types, using the Maximum Variance Components Split method. Additionally, we show that cryptocurrencies tend to exhibit similar characteristics over time and become more distinguished from other asset classes (synchronic evolution).
Daniel Modenesi de Andrade, Fernando Barros, FĂĄbio Motoki, Matheus Oliveira da Silva
Purpose This paper aims to study the dynamics of bitcoin prices in Brazil, a large emerging economy with an unregulated bitcoin market. Design/methodology/approach First, this study tests if the Law of One Price (LOOP) is valid for bitcoin prices in Brazil, conducting tests with data from three Brazilian exchanges. Next, this study documents bitcoin price dynamics in the short run by studying the price discovery mechanism in these exchanges. This study uses Information Share and Component Share, combining the two measures to obtain an Information Leadership Share (ILS) measure. Findings This study finds a common trend within bitcoin prices among a set of exchanges, with cointegration tests between the price series indicating that LOOP is valid in Brazilian markets in the long run. ILS indicated that, for closing prices, the most liquid exchange (Foxbit) leads discovery, whereas the least liquid (Local Bitcoin) lags, with Mercado Bitcoin in the middle both in terms of discovery and liquidity. Finally, this study provides evidence that the price variation in the market that leads price discovery can be used to construct an arbitrage in another exchange. Originality/value This research brings the first evidence of a price discovery mechanism for exchanges in Brazilian Reais. Although LOOP is valid in the long run, price leadership in bitcoin markets potentially create arbitrage opportunities in the short run. This study contributes to the growing literature of bitcoin prices with novel evidence from a large emerging economy.