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2,964 results ¡ page 117 of 124
Mohammad Jafarinejad, Hamid Sakaki
No abstract is available for this record.
Thanos Andrikopoulos, Robert Hudson, Saeed Akbar, Darius Saftoiu
No abstract is available for this record.
Anton Kajtazi, Andrea Moro
No abstract is available for this record.
Min Luo, Vasileios E. Kontosakos, Athanasios A. Pantelous, Jian Zhou
No abstract is available for this record.
Takahiro Hattori, Ryo Ishida
No abstract is available for this record.
Gloria Yang Yu, Jinyuan Zhang
No abstract is available for this record.
Stefano Colucci
No abstract is available for this record.
Masafumi Nakano, Akihiko Takahashi, Soichiro Takahashi
No abstract is available for this record.
Michael S. Pagano, John Sedunov
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.
Alla Petukhina, Simon Trimborn, Wolfgang Karl Härdle, Hermann Elendner
No abstract is available for this record.
Hanlin Yang
No abstract is available for this record.
Tatja Kärkkäinen
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.
Nashirah Abu Bakar, Sofian Rosbi
The purpose of this study is to develop robust estimation of association between two types of crypto-currencies namely Bitcoin and Ethereum. Daily data of crypto-currencies are collected from https://coinmarketcap.com. The period for data analysis is started from January 2017 until October 2018. The value of mean return for Bitcoin is 13.18 %. Meanwhile, the value of mean return for Ethereum is 27.85 %. The standard deviation for Bitcoin is 30.27 % and Ethereum is 64.24 %. Then, this study performed Person product moment coefficient analysis to evaluate the correlation between these two crypto-currencies. Result indicates the association coefficient value is 0.50. The correlation shows there is strong positive correlation between Bitcoin return and Ethereum return. As conclusion, there is significant relationship between Bitcoin and Ethereum return data with strong positive correlation (r = 0.503, n = 21, p =0.020).The significant of this study is to help investors to make better decision in selecting appropriate investment portfolio for their investment fund that contributes better return and lower risk.
Lawrence J. Trautman, Taft Dorman
No abstract is available for this record.
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).
Robert Ĺlepaczuk, Maryna Zenkova
Abstract This study investigates the profitability of an algorithmic trading strategy based on training SVM model to identify cryptocurrencies with high or low predicted returns. A tail set is defined to be a group of coins whose volatility-adjusted returns are in the highest or the lowest quintile. Each cryptocurrency is represented by a set of six technical features. SVM is trained on historical tail sets and tested on the current data. The classifier is chosen to be a nonlinear support vector machine. The portfolio is formed by ranking coins using the SVM output. The highest ranked coins are used for long positions to be included in the portfolio for one reallocation period. The following metrics were used to estimate the portfolio profitability: %ARC (the annualized rate of change), %ASD (the annualized standard deviation of daily returns), MDD (the maximum drawdown coefficient), IR1, IR2 (the information ratio coefficients). The performance of the SVM portfolio is compared to the performance of the four benchmark strategies based on the values of the information ratio coefficient IR1, which quantifies the risk-weighted gain. The question of how sensitive the portfolio performance is to the parameters set in the SVM model is also addressed in this study.
Marius Kinderis, Marija Bezbradica, Martin Crane
Predicting currency prices remains a difficult endeavour. Investors are continually seeking new ways to extract \nmeaningful information about the future direction of price changes. Recently, cryptocurrencies have attracted \nhuge attention due to their unique way of transferring value as well as its value as a hedge. A method proposed \nin this project involves using data mining techniques: mining text documents such as news articles and tweets \ntry to infer the relationship between information contained in such items and cryptocurrency price direction. \nThe Long Short-Term Memory Recurrent Neural Network (LSTM RNN) assists in creating a hybrid model \nwhich comprises of sentiment analysis techniques, as well as a predictive machine learning model. The success \nof the model was evaluated within the context of predicting the direction of Bitcoin price changes. Findings \nreported here reveal that our system yields more accurate and real-time predictions of Bitcoin price fluctuations \nwhen compared to other existing models in the market.
Wee Seng Wong, Dennis Saerbeck, Dante Delgado Silva
No abstract is available for this record.
Shaen Corbet, Charles Larkin, Brian M. Lucey, Andrew Meegan ¡ 5 authors
No abstract is available for this record.
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.
Zhenghui Li, Hao Dong, Zhehao Huang, Pierre Failler
The rapid development of VFAs allows investors to diversify their choices of investment products. In this paper, we measure the return risk of VFAs based on GARCH-type model. By establishing a Markov regime-switching Regression (MSR) Model, we explore the asymmetric effects of speculation, investor attention, and market interoperability on return risks in different risk regimes of VFAs. The results show that the influences of speculation and investor attention on the risks of VFAs are significantly positive at all regimes, while market interoperability only admits a positive impact on risk under high risk regime. All of the three factors exert asymmetric effects on risks in different regimes. Further study presents that the risk regime-switching also shows asymmetric characteristic but the medium risk regime is more stable than any others. Therefore, transactions of investors and arbitrageurs are monitored by certain policies, such as limiting the number of transactions or restricting the trading amount at high risk regime. However, when return risk is low, it will return to a medium level if we encourage investors to access.
Guglielmo Maria Caporale, Alex Plastun, Viktor Oliinyk
This paper investigates the role of the frequency of price overreactions in the cryptocurrency market in the case of BitCoin over the period 2013â2018. Specifically, it uses a static approach to detect overreactions and then carries out hypothesis testing by means of a variety of statistical methods (both parametric and non-parametric) including ADF tests, Granger causality tests, correlation analysis, regression analysis with dummy variables, ARIMA and ARMAX models, neural net models, and VAR models. Specifically, the hypotheses tested are whether or not the frequency of overreactions (i) is informative about Bitcoin price movements (H1) and (ii) exhibits no seasonality (H2). On the whole, the results suggest that it can provide useful information to predict price dynamics in the cryptocurrency market and for designing trading strategies (H1 cannot be rejected), whilst there is no evidence of seasonality (H2 cannot be rejected).
Valerio Celeste, Shaen Corbet, Constantin Gurdgiev
No abstract is available for this record.