The narrative of a Bitcoin is a bubble is very common. We employ statistical techniques to empirically evaluate such claim. A branch of literature links the existence of a bubble in some financial asset’s price to strict local martingales — a finitely lived asset has a bubble if, and only if, it is a strict local martingale under the equivalent risk-neutral measure. A diffusion process is a strict local martingale if its volatility increases faster than linearly as its level grows. We apply a nonparametric method to estimate the volatility function of Bitcoin daily and high frequency prices, as well as of more traditional financial assets. We then estimate the stochastic volatility model of Andersen and Piterbarg (2007), whose parameter space has a specific subset under which the asset’s price is a strict local martingale. Results suggest the existence of a bubble in Bitcoin prices from early 2013 to mid 2014, but, interestingly, not in late 2017.
Bitcoin is the world’s leading cryptocurrency, with a market capitalization briefly exceeding $300 billion. This hints at Bitcoin’s \namorphous nature: is this a monetary or a corporate measure? Hard values become explicit in the processing of transactions and \nthe digital mining of Bitcoins. Electricity is a primary input cost. Bitcoins earned are often used to circumvent local currency \ncontrols and acquire US dollars. For the period August 2010 to February 2018, we examine the components of Bitcoin mining \nrevenues, their statistical contribution to daily changes, and to its variance. We provide evidence that Bitcoin transaction processing \nis capacity constrained.
Rubaiyat Islam, Yoshi Fujiwara, Shinya Kawata, Hiwon Yoon
In a closed economic system like blockchain, the total amount of generated cryptocurrency called bitcoin is conserved and the transaction patterns demonstrate an insight of money flow inside the blockchain. For the last 2 years, bitcoin market has grabbed an immense attention from the investors, technology entrepreneurs and currency enthusiasts. In this paper, we have come up with some findings in our investigation about the bitcoin time-series transaction patterns. We have graphically represented bitcoin’s weekly patterns as a real economic currency that has been minted, stored and exchanged inside the bitcoin blockchain network. We identified outliers’ activities with the help of descriptive statistical analysis. We also demonstrated transaction pattern behavioral change. The main implication of these findings is to understand some stylized facts of the time-series transaction of cryptocurrency-based fully digital financial system. Besides in our analysis, we have shown that the behavioral change of the transaction pattern is capable of explaining the system development events or major historical events that have a network impact.
This paper is deeply motivated by the need to explore the impressive Bitcoin price development by addressing Bitcoin as money in its essential attribute as a medium of exchange. We adopt a monetary economics viewpoint and resort to a representative agent modelling strategy within a money-in-the-utility function (MIUF) framework. First, we show that the impressive Bitcoin price development observed since its inception can be interpreted as a hyperdeflation when we focus on Bitcoin role as a medium of exchange. Second, we show that specific monetary features of Bitcoin, its asymptotical fixed nominal stock and divisibility down to eight decimal places, account for a strong possibility of speculative hyperdeflationary paths. It is shown that those paths are fully consistent with the medium of exchange monetary role of Bitcoin and the representative agent optimizing behavior.
We propose in this article to study the behavior of investors in the bitcoin market in order to test whether investors' overconfidence is a driver of excess volatility, often associated with the aforementioned market. This paper presents an attempt to deepen the previously published studies by adopting a new ARMA(p,q)-FIEGARCH(1,d,k,1) parametrization capable of capturing the overconfidence element as well as simultaneously accounting for possible long memory effect. The data used in this study consists of daily closing prices along with daily exchange volume of Bitcoin, spanning the period ranging from 01/01/2012 up to 31/05/2018. The results and conclusions drafted in this research paper could help to understand the formation of volatility in the Bitcoin market. Therefore, this kind of studies will enable investors to better predict bubbles and irrational exuberances. The main contribution of the present article is drawn from the broadening of previous studies by adopting a newly constructed model, which combines capturing asymmetric response, long memory along with the overconfidence element.
Cryptocurrency is a rapid developing financial technology innovation which has attracted a large number of people around the world. The high-speed evolution, radical price fluctuations of cryptocurrency, and the inconsistent attitudes of monetary authorities in different countries have triggered panic and chain reactions towards the application and adoption of cryptocurrency and have caused public security related events. So far, a lot of researches and analyses have focused on just one or only a few number of cryptocurrencies, a comprehensive analysis of the whole cryptocurrency market and its systemic risk is still lacking. In this paper, we analyze the dynamics and systemic risk of the cryptocurrency market based on the public available price history. We first validated that the correlation matrix and asset tree are good tools to analyze the risk and stability of the cryptocurrency market. Furthermore, consistent with public perception, our quantitative analysis reveals that the cryptocurrency market is relatively fragile and unstable. Our work is the first to investigate the systemic risk of the whole cryptocurrency market and may shed some light on cryptocurrency related investment decision, regulation, and legislation.
In this article, we attempt to delineate the relationship between bitcoin prices and global factors such as stock index, economic policy uncertainty, gold spot prices, implied volatility and crude oil prices in a time-frequency domain. We resort to wavelet-based analysis to capture the multiscale interactive behavior of bitcoin with global factors. We primarily show that bitcoin is insulated from global factors in the short run. However, the existence of a significant relationship of bitcoin with global factors cannot be denied in the medium to long run, which could be attributed to the endogenous and intertwined economic system. Among the global factors considered in the study, we find the impact of economic policy uncertainty and crude oil prices to be more prominent on bitcoin. Our study offers some interesting insights on multiscale sensitivity of bitcoin to global factors, which may be useful for investors for taking informed decisions
We review the literature and examine the effects of shocks on bitcoin returns. We assess the effects of factors such as stock market returns, exchange rates, gold and oil returns, FED's and ECB's rates and internet trends on bitcoin returns. Alternative VAR and FAVAR models are employed and generalized as well as local impulse response functions are produced. Our results reveal (i) a significant interaction between bitcoin and traditional stock markets, (ii) a weaker interaction with FX markets and the macroeconomy and (iii) an anemic importance of popularity measures. Lastly, we reveal the increased impact of Asian markets on bitcoin compared to other geographically-defined markets, which however appears to have waned in the last two years after the Chinese regulatory interventions and the sudden contraction of CNY's share in bitcoin trading volume.
Bitcoin has recently been labelled as a “dangerous speculative bubble†by Nobel Prize-winning economists Joseph Stiglitz and Robert Shiller, as the Bitcoin's market value now exceeds the GDP of over 130 countries. In this study, the multifractality and efficiency of the Bitcoin price index are tested, using a nonlinear data analysis technique called the multifractal detrended fluctuation analysis (MF-DFA). In addition, we assess the time-variations in the market efficiency level through using a rolling-window framework. Our evidence shows that the efficiency of the Bitcoin market changes over time and this market seems to be more efficient during downward than upward periods. We also find that Bitcoin is marked by a persistent long memory phenomenon in its short- term components, which could be interpreted as a possible speculation by investors.
We study the efficiency of cryptocurrencies by measuring the price’s reaction time to unexpected relevant information. We find the average price delay to significantly decrease during the last three years. For the cross-section of 75 cryptocurrencies we find delays to be highly correlated with liquidity.
In the present paper, we investigate connectedness within cryptocurrency markets as well as across the Bitcoin index (hereafter, BPI) and widely traded asset classes such as traditional currencies, stock market indices and commodities, such as gold and Brent oil. A spill over index approach with the spectral representation of variance decomposition networks, is employed to measure connectedness. Results show no significant spillover effects between the nascent market of cryptocurrencies and other financial markets. We suggest that cryptocurrencies are real independent financial instruments that pose no danger to financial system stability. Concerning the connectedness within the cryptocurrency markets, we report a time–frequency–dynamics connectedness nature. Moreover, the decomposition of the total spill over index is mostly dominated by a short frequency component (2–4 days) leading to the conclusion that this nascent market is highly speculative at present. These findings provide insights for regulators and potential international investors.
In this paper we extend the known methodology for fitting stable\ndistributions to the multivariate case and apply the suggested method to the\nmodelling of daily cryptocurrency-return data. The investigated time period is\ncut into 10 non-overlapping sections, thus the changes can also be observed. We\napply bootstrap tests for checking the models and compare our approach to the\nmore traditional extreme-value and copula models.\n
Frode Kjærland, Aras Khazal, Erlend Aune Krogstad, Frans B. Gyllenhammar Nordstrøm · 5 authors
This paper aims to enhance the understanding of which factors affect the price development of Bitcoin in order for investors to make sound investment decisions. Previous literature has covered only a small extent of the highly volatile period during the last months of 2017 and the beginning of 2018. To examine the potential price drivers, we use the Autoregressive Distributed Lag and Generalized Autoregressive Conditional Heteroscedasticity approach. Our study identifies the technological factor Hashrate as irrelevant for modeling Bitcoin price dynamics. This irrelevance is due to the underlying code that makes the supply of Bitcoins deterministic, and it stands in contrast to previous literature that has included Hashrate as a crucial independent variable. Moreover, the empirical findings indicate that the price of Bitcoin is affected by returns on the S&P 500 and Google searches, showing consistency with results from previous literature. In contrast to previous literature, we find the CBOE volatility index (VIX), oil, gold, and Bitcoin transaction volume to be insignificant.
The paper analyzes the relationship between the most popular cryptocurrencies and a range of selected fiat currencies, in order to identify any pattern and/or causality between the series. Cryptocurrencies are a hot topic in Finance due to their strict relationship with the Blockchain system they originate from and therefore are normally considered as part of the ongoing, world-wide financial revolution. This innovative study investigates this relationship for the first time by thoroughly investigating the data, their features, and the way they are interconnected. Results show very interesting results in terms of how concentrated the causality effect on some specific cryptocurrencies and fiat currencies is. The outcome is a clear and possibly explainable relationship between cryptocurrencies and Asian markets, while envisioning some kind of Asian effect.
The cryptocurrency market is unique on many levels: Very volatile, frequently changing market structure, emerging and vanishing of cryptocurrencies on a daily level. Following its development became a difficult task with the success of cryptocurrencies (CCs) other than Bitcoin. For fiat currency markets , the IMF offers the index SDR and, prior to the EUR, the ECU existed, which was an index representing the development of European currencies. Index providers decide on a fixed number of index constituents which will represent the market segment. It is a challenge to fix a number and develop rules for the constituents in view of the market changes. In the frequently changing CC market, this challenge is even more severe. A method relying on the AIC is proposed to quickly react to market changes and therefore enable us to create an index, referred to as CRIX, for the cryptocurrency market. CRIX is chosen by model selection such that it represents the market well to enable each interested party studying economic questions in this market and to invest into the market. The diversified nature of the CC market makes the inclusion of altcoins in the index product critical to improve tracking performance. We have shown that assigning optimal weights to altcoins helps to reduce the tracking errors of a CC portfolio, despite the fact that their market cap is much smaller relative to Bitcoin. The codes used here are available via www.quantlet.de .