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2,335 results · page 88 of 98
Sofia Anyfantaki, Stelios Arvanitis, Nikolas Topaloglou
No abstract is available for this record.
Dominique Guégan, Marius Frunza
The aim of this research is to explore the econometric features of Bitcoin-USD rates. Various non-Gaussian models are fitted to daily returns in order to underline the unique characteristics of Bitcoin when compared to other more traditional currencies. Market efficiency hypothesis is tested further, and the main reasons for breaches in efficiency are discussed. The main goal of the paper is to assess the presence of bubble effects in this market with customized tests able to detect the timing of various bubbles. The results show that the Bitcoin prices had two episodes of rapid inflation in 2013 and 2017.
Efe Akinci, Jing Li
This paper studies Granger Causality relations between Bitcoin and 5 stock market indexes which are Japan, Russia, South Korea, Sweden and the United States. The time-period examined is from 2013 to 2017 and all the tests are conducted based on daily data. We analyze this in three different periods, last 5 years (2013-2017), in 2017 and last 3 months of 2017. To estimate the relationship, we use unit root test and Augmented Dickey-Fuller, Lagrange Multiplier, Johansen Cointegration Test and finally Granger Causality Test. After the tests, countries have a same integrated order that exhibits a long-run relationship. In causality, except for Russia, each country has affected the Bitcoin prices and being affected in a different period, especially in the last 3 months of 2017, the impact and popularity of Bitcoin affect too much the stock market in the short-run. As a result, the causation between Bitcoin and stock market indexes shows impact statistically significant in the 2017 year. The importance of cryptocurrency and popularity not as much as hype like late 2017 in 2018, but we think that cryptocurrencies are one of the major currencies that affect economical world very deeply.
Jamil Civitarese
No abstract is available for this record.
Yasar Kaya
In this paper, the price fluctuations of Bitcoin under speculative environment is studied. It has been seen that the market trend points out an existence of a speculative bubble. Over the course of the period from 2014 to 2018, the trend in price movements of bitcoin has proved to be strongly speculative. In that regard, investors might be curious about what drivers might be instrumental in these speculative price changes. After reviewing of NPV, it was seen that NPV is not applicable to the case of cryptocurrencies due to their nature and lack of free cash flows to base the asset valuation to some fundamental facts. Later, LPPL model is reviewed, however, that also proved to be insufficient since it does not reflect the investor speculations and inform much about price dynamics regarding behavioral finance principles. Then, some papers from the past price fluctuations of bitcoin (for the period from 2010 to 2013) was reviewed and three key variables were determined which might explain price movements. Public interest towards Bitcoin as interest-driven, regulatory and political news about cryptocurrencies as event-driven and VIX as overall investor approach to Bitcoin market have been taken. After running regressions, the only significant variable happened to be public interest and popularity of Bitcoin. Although, for some cases, VIX variable also explain price fluctuations for some intervals, in none of the cases event-driven variable has long- terms effect on price fluctuations under speculative environment. Lastly, a robustness test is also handled considering the âweekend effectâ and it has been seen public interest variable again proved to be a significant price determinant.
Thanos Andrikopoulos, Robert Hudson, Saeed Akbar, Darius Saftoiu
No abstract is available for this record.
Min Luo, Vasileios E. Kontosakos, Athanasios A. Pantelous, Jian Zhou
No abstract is available for this record.
Jamil Civitarese
Metcalfe's Law argues the value of a network is proportional to the square of its users. Bitcoin and other cryptocurrencies can be modeled as such: if Metcalfe's Law is true, then it is possible to forecast prices using the size of the network. I test this assertion by a cointegration test between price and an adjusted number of wallets' connections. It is stated that the series do not cointegrate, rejecting the Metcalfe's Law. A first-differences model is employed to further analyse the relation between returns and variations in the number of wallets. It is stated that Metcalfe's Law consistently predicts the trend in the value of Bitcoin; nevertheless, it is not possible to reject the reverse causation of Bitcoin returns leading to new wallets.
JesĂșs FernĂĄndezâVillaverde
Abstract This article reviews what cryptocurrencies are, and it frames them within the context of historical monetary experiences and contemporary monetary economics. The article argues that, as pure fiduciary private money, cryptocurrencies are a bubble without a fundamental value and they will not provide, in general, optimal amounts of money or deliver price stability. Nevertheless, cryptocurrencies can play a role in improving the current means of payments and in disciplining central banks into providing better governmentârun fiduciary monies.
Kee-Youn Kang, Seungduck Lee
No abstract is available for this record.
Kamwoo Lee, Sinan Ulkuatam, Peter A. Beling, William T. Scherer
In this paper, we present a novel method to predict Bitcoin price movement utilizing inverse reinforcement learning (IRL) and agent-based modeling (ABM). Our approach consists of predicting the price through reproducing synthetic yet realistic behaviors of rational agents in a simulated market, instead of estimating relationships between the price and price-related factors. IRL provides a systematic way to find the behavioral rules of each agent from Blockchain data by framing the trading behavior estimation as a problem of recovering motivations from observed behavior and generating rules consistent with these motivations. Once the rules are recovered, an agent-based model creates hypothetical interactions between the recovered behavioral rules, discovering equilibrium prices as emergent features through matching the supply and demand of Bitcoin. One distinct aspect of our approach with ABM is that while conventional approaches manually design individual rules, our agents' rules are channeled from IRL. Our experimental results show that the proposed method can predict short-term market price while outlining overall market trend.
Michael S. Pagano, John Sedunov
No abstract is available for this record.
Alexander Brauneis, Roland Mestel, Ryan Riordan, Erik Theissen
We study trading of Bitcoin against US dollar (BTCUSD) on exchanges in three continents, Bitfinex, Bitstamp and Coinbase Pro. We use a high frequency dataset that contains transactions and order book information. The BTCUSD market is highly liquid in terms of bid-ask spreads and order book depth. While spreads are even lower than in equity markets, prices are not integrated across exchanges. Persistent differences exist between the three exchanges in terms of trade prices and posted prices often violating no-arbitrage assumptions. The liquidity of the Bitcoin exchanges is predominantly determined by local factors and is essentially independent of liquidity in equity and FX markets. This suggests that despite the virtual nature of Bitcoin, local jurisdictional factors affect the flow of capital between low and high price jurisdictions.
Urban J. Jermann
No abstract is available for this record.
Ăric Ghysels, Giang Nguyen
We examine price discovery and liquidity provision in the secondary market for bitcoinâan asset with a high level of speculative trading. Based on BTC-eâs full limit order book over the 2013â2014 period, we find that order informativeness increases with order aggressiveness within the first 10 tiers, but that this pattern reverses in outer tiers. In a high volatility environment, aggressive orders seem to be more attractive to informed agents, but market liquidity migrates outward in response to the information asymmetry. We also find support to the Markovian learning assumption often made in theoretical models of limit order markets.
Hanlin Yang
No abstract is available for this record.
JĂłn DanıÌelsson
No abstract is available for this record.
Tatja KÀrkkÀinen
Following the popularity of Bitcoin trading in recent years, Bitcoin futures were introduced in December 2017 as an effort to provide institutional and retail investors with additional trading tools for Bitcoin. This study analyses the Bitcoin futures mid-quote data from CBOE, and Bitcoin market index applying VAR and VECM process methodologies, Hasbrouckâs information share and the Gonzalo-Granger component share measurement to examine price discovery in Bitcoin markets. Furthermore, the chapter seeks to assess the Bitcoin market microstructure. The results drawn on the intra-day prices show that the futures are leading the price discovery at different frequencies even with comparably low futures trading volumes. This supports the extant literature of futures-spot market price discovery and the role of informed traders in the futures market.
Feng Dong, Zhiwei Xu, Yu Zhang
There has been a burgeoning Fintech literature in the past years, especially on cryptocurrencies. However, there is lack of research handling cryptocurrencies in a mainstream macroeconomic model. To bridge the gap, we develop a model for Bitcoin-like cryptocurrency as risky and costly bubbles in an infinite-horizon production economy. This model is consistent with the following facts: i) the surging Bitcoin market presents enormous volatility, ii) its price dynamics are significantly sensitive to both market sentiment and policy stances. Entrepreneurial firms choose to hold Bitcoins as liquid assets to buffer idiosyncratic investment distortions. The intrinsically worthless Bitcoins can emerge as rational bubbles when the market sentiment is optimistic enough. On the one hand, bubbly Bitcoins provide market liquidity to facilitate investment in the real sector, while on the other hand, they deteriorate the investment efficiency and crowd out aggregate production. Our quantitative exercise produces various cyclical features of Bitcoin bubbles and find that the collapse of Bitcoin bubbles can improve social welfare by decreasing distortion-driven real investment.
Spencer Wheatley, Didier Sornette, Tobias Huber, Max Reppen · 5 authors
We develop a strong diagnostic for bubbles and crashes in bitcoin, by analyzing the coincidence (and its absence) of fundamental and technical indicators. Using a generalized Metcalfe's law based on network properties, a fundamental value is quantified and shown to be heavily exceeded, on at least four occasions, by bubbles that grow and burst. In these bubbles, we detect a universal super-exponential unsustainable growth. We model this universal pattern with the Log-Periodic Power Law Singularity (LPPLS) model, which parsimoniously captures diverse positive feedback phenomena, such as herding and imitation. The LPPLS model is shown to provide an ex-ante warning of market instabilities, quantifying a high crash hazard and probabilistic bracket of the crash time consistent with the actual corrections; although, as always, the precise time and trigger (which straw breaks the camel's back) being exogenous and unpredictable. Looking forward, our analysis identifies a substantial but not unprecedented overvaluation in the price of bitcoin, suggesting many months of volatile sideways bitcoin prices ahead (from the time of writing, March 2018).
Paraskevi Katsiampa, ÎÏÎœÏÏαΜÏÎŻÎœÎżÏ ÎÎșίλλαÏ, François Longin
No abstract is available for this record.
Wee Seng Wong, Dennis Saerbeck, Dante Delgado Silva
No abstract is available for this record.
Cathy YiâHsuan Chen, Wolfgang Karl HĂ€rdle, Ai Jun Hou, Ning Wang
The CRIX (CRyptocurrency IndeX) has been constructed based on a number of cryptos and provides a high coverage of market liquidity, hu.berlin/crix. The crypto currency market is a new asset market and attracts a lot of investors recently. Surprisingly a market for contingent claims hat not been built up yet. A reason is certainly the lack of pricing tools that are based on solid financial econometric tools. Here a first step towards pricing of derivatives of this new asset class is presented. After a careful econometric pre-analysis we motivate an affine jump diffusion model, i.e., the SVCJ (Stochastic Volatility with Correlated Jumps) model. We calibrate SVCJ by MCMC and obtain interpretable jump processes and then via simulation price options. The jumps present in the cryptocurrency fluctutations are an essential component. Concrete examples are given to establish an OCRIX exchange platform trading options on CRIX.
Wei Zhang, Pengfei Wang, Xiao Li, Dehua Shen
We investigate the crossâcorrelations of returnâvolume relationship of the Bitcoin market. In particular, we select eight exchange rates whose trading volume accounts for more than 98% market shares to synthesize Bitcoin indexes. The empirical results based on multifractal detrended crossâcorrelation analysis (MFâDCCA) reveal that (1) the nonlinear dependencies and powerâlaw crossâcorrelations in returnâvolume relationship are found; (2) all crossâcorrelations are multifractal, and there are antipersistent behaviors of crossâcorrelation for q = 2; (3) the price of small fluctuations is more persistent than that of the volume, while the volume of larger fluctuations is more antipersistent; and (4) the rolling window method shows that the crossâcorrelations of returnâvolume are antipersistent in the entire sample period.