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2,335 results · page 80 of 98
Timothy Peterson
No abstract is available for this record.
Argimiro Arratia, Albert X. López-Barrantes
In early 2018 prices peaked at USD 20,000 and, almost two years later, we still continue debating if cryptocurrencies can actually become a currency for the everyday life or not. From the economic point of view, and playing in the field of behavioral finance, this paper analyses the relation between prices and the search interest on Bitcoin since 2014. We questioned the forecasting ability of Google Trends for the behavior of price by performing linear and nonlinear dependency tests, and exploring performance of ARIMA and Neural Network models enhanced with this social sentiment indicator. Our analyses and models are founded upon a set of statistical properties common to financial returns that we establish for Bitcoin, Ethereum, Ripple and Litecoin.
Theodore Panagiotidis, Thanasis Stengos, Orestis Vravosinos
We examine the significance of fourty-one potential covariates of bitcoin returns for the period 2010–2018 (2872 daily observations). The recently introduced principal component-guided sparse regression is employed. We reveal that economic policy uncertainty and stock market volatility are among the most important variables for bitcoin. We also trace strong evidence of bubbly bitcoin behavior in the 2017–2018 period.
Jun Deng, Huifeng Pan, Shuyu Zhang, Bin Zou
No abstract is available for this record.
Zura Kakushadze, Willie Yu
We give an algorithm and source code for a cryptoasset statistical arbitrage alpha based on a mean-reversion effect driven by the leading momentum factor in cryptoasset returns discussed in https://ssrn.com/abstract=3245641. Using empirical data, we identify the cross-section of cryptoassets for which this altcoin-Bitcoin arbitrage alpha is significant and discuss it in the context of liquidity considerations as well as its implications for cryptoasset trading.
Nikolai Zaitsev
Report presents analysis of empirical distribution of future returns of bitcoin (BTC) from BTUSD inverse option prices. Logistic pdf is chosen as underlying distribution to fit option prices. The result is satisfactory and suggests that these prices can be described with just three or even one parameter. Fitted Logistic pdf matches forward price movements upto a scaling factor. Nevertheless, this observation stands alone and does not allow stochastic description of underlying prices with logistic pdf in similar fashion as it is done within Black-Scholes modelling framework. Put-call parity relationship is derived connecting prices of vanilla inverse options and futures.
Guglielmo Maria Caporale, Alex Plastun
Abstract This paper examines whether there exists a momentum effect after one-day abnormal returns in the cryptocurrency market. For this purpose, a number of hypotheses of interest are tested for the Bitcoin, Ethereum and Litecoin exchange rates vis-à-vis the US dollar over the period 01.01.2015–01.09.2019, specifically whether or not: (H1) the intraday behavior of hourly returns is different on abnormal days compared to normal days; (H2) there is a momentum effect on days with abnormal returns, and (H3) after one-day abnormal returns. The methods used for the analysis include various statistical methods as well as a trading simulation approach. The results suggest that hourly returns during the day of positive/negative abnormal returns are significantly higher/lower than those during the average positive/negative day. The presence of abnormal returns can usually be detected before the day ends by estimating specific timing parameters. Prices tend to move in the direction of the abnormal returns till the end of the day when it occurs, which implies the existence of a momentum effect on that day giving rise to exploitable profit opportunities. This effect (together with profit opportunities) is also observed on the following day. In two cases (BTCUSD positive abnormal returns and ETHUSD negative abnormal returns), a contrarian effect is detected instead.
Cho‐Hoi Hui, Chi‐Fai Lo, Po-Hon Chau, Andrew L. Wong
No abstract is available for this record.
Savva Shanaev, Satish Kumar Sharma, Subhakara Valluri, Arina Shuraeva
No abstract is available for this record.
Cathy Yi‐Hsuan Chen, Romeo Despres, Li Guo, Thomas Renault
No abstract is available for this record.
Rick Bohte, Luca Rossini
This paper studies the forecasting ability of cryptocurrency time series. This study is about the four most capitalized cryptocurrencies: Bitcoin, Ethereum, Litecoin and Ripple. Different Bayesian models are compared, including models with constant and time-varying volatility, such as stochastic volatility and GARCH. Moreover, some crypto-predictors are included in the analysis, such as S\&P 500 and Nikkei 225. In this paper the results show that stochastic volatility is significantly outperforming the benchmark of VAR in both point and density forecasting. Using a different type of distribution, for the errors of the stochastic volatility the student-t distribution came out to be outperforming the standard normal approach.
Anthony Ngunyi, Simon Mundia, Cyprian Ondieki Omari
Cryptocurrencies have become increasingly popular in recent years attracting the attention of the media, academia, investors, speculators, regulators, and governments worldwide. This paper focuses on modelling the volatility dynamics of eight most popular cryptocurrencies in terms of their market capitalization for the period starting from 7th August 2015 to 1st August 2018. In particular, we consider the following cryptocurrencies; Bitcoin, Ethereum, Litecoin, Ripple, Moreno, Dash, Stellar and NEM. The GARCH-type models assuming different distributions for the innovations term are fitted to cryptocurrencies data and their adequacy is evaluated using diagnostic tests. The selected optimal GARCH-type models are then used to simulate out-of-sample volatility forecasts which are in turn utilized to estimate the one-day-ahead VaR forecasts. The empirical results demonstrate that the optimal in-sample GARCH-type specifications vary from the selected out-of-sample VaR forecasts models for all cryptocurrencies. Whilst the empirical results do not guarantee a straightforward preference among GARCH-type models, the asymmetric GARCH models with long memory property and heavy-tailed innovations distributions overall perform better for all cryptocurrencies.
Peter Zimmerman
I present a model of cryptocurrency price formation that endogenizes both the financial market for coins and the fee-based market for blockchain space. A cryptocurrency has two distinctive features: a price determined by the extent of its usage as money, and a blockchain structure that restricts settlement capacity. Limited settlement space creates competition between users of the currency, so speculative activity can crowd out monetary usage. This crowding-out undermines the ability of a cryptocurrency to act as a medium of payment, lowering its value. Higher speculative demand can reduce prices, contrary to standard economic models. Crowding-out also raises the riskiness of investing in cryptocurrency, explaining high observed price volatility.
Svetlana Saksonova, Irina Kuzmina-Merlino
This paper considers the development of attractive strategies featuring cryptocurrency assets, considering their costs and potential risks. The object of analysis in this paper is cryptocurrency as an investment instrument. The main hypothesis of the research is that modern portfolio theory can be applied to cryptocurrency investments to design an investment portfolio with appropriate risk and profitability characteristics. The authors of the paper: (i) place cryptocurrencies in the context of modern financial market and financial technology development;
Marko Ogorevc
This paper is motivated by a hypothesis that the long term value of a cryptocurrency is determined by its future use as money. For a cryptocurrency to be used as a medium of payment, it has to fulfill three independent functions: medium of exchange, a unit of account, and store of value. Currently, cryptocurrencies are held for investment purposes rather than being used for transactions and thus as a medium of exchange. For cryptocurrency to become widely adopted as a means of payment, it first needs to go through a very volatile period because speculative traders see long-run future value in the cryptocurrency. In order to soften transition from speculative asset to medium of payment a trading strategy is proposed, which provides liquidity and reduces volatility. Similar to pairs trading strategy, the proposed solution is based on cointegration and performed in three steps. The main difference is that proposed solution does not include shorting, but holding cryptocurrencies, thus increasing the total available cash and adding to the equilibrium price. Results from an ongoing experiment suggest that the proposed trading strategy is appealing for about 40% of cryptocurrency investors, as the struggle against volatility problem is accompanied by significant financial gains.
Varghese S. Jacob, Sailendra Prasanna Mishra, Suresh Radhakrishnan
No abstract is available for this record.
Moinak Maiti, Darko Vuković, Victor Krakovich, Maneesh Kumar Pandey
The present study focuses on five cryptocurrencies co-movements physiognomies both in time and frequency domain. The present study highlighted several interesting facts related to cryptocurrencies co-movements both in time and frequency domain that have high policy and investment implications. Overall wavelet coherence diagrams clearly indicate about the very short and long contagion effect among the cryptocurrency pairs for the whole study period. The contagion effect is different at different time scales. Finally wavelet clustering diagram indicates that by investing only in XBP and BitCoin cryptocurrencies investors are not going to get any benefit from diversification. This predictable co-movements pattern among the cryptocurrencies could be the basic investment strategies to gain maximum profit by diversifying the risk in cryptocurrency investments.
Ryotaro Miura, Lukáš Pichl, Taisei Kaizoji
Realized volatility (RV) is defined as the sum of the squares of logarithmic returns on high-frequency sampling grid and aggregated over a certain time interval, typically a trading day in finance. It is not a priori clear what the aggregation period should be in case of continuously traded cryptocurrencies at online exchanges. In this work, we aggregate RV values using minute-sampled Bitcoin returns over 3-h intervals. Next, using the RV time series, we predict the future values based on the past samples using a plethora of machine learning methods, ANN (MLP, GRU, LSTM), SVM, and Ridge Regression, which are compared to the Heterogeneous Auto-Regressive Realized Volatility (HARRV) model with optimized lag parameters. It is shown that Ridge Regression performs the best, which supports the auto-regressive dynamics postulated by HARRV model. Mean Squared Error values by the neural-network based methods closely follow, whereas the SVM shows the worst performance. The present benchmarks can be used for dynamic risk hedging in algorithmic trading at cryptocurrency markets.
Luisanna Cocco, Roberto Tonelli, Michele Marchesi
The objective of this paper is to simulate the trading of the currency pair BTC/USD, investigating through the theory of the genetic algorithms the best sets of trading strategies, simulating through a realistic order book the bitcoin price formation, and reproducing a bitcoin price series that exhibits some stylized facts found in real-time price series. In this artificial market model two kinds of agents, Chartists and Random traders, perform trading. Chartists trade through the application of trading rules. Specifically, a part of Chartists trades applying the best sets of trading rules selected by a genetic algorithm that simulates a trading system, based on four technical analysis indicators, searching for parameters of each indicator that guarantee the highest profits in the training period; the remaining part trades applying trading rules choosing their parameters in a random way. On the contrary random trader's trade without applying any trading strategy, issuing in a random way sell or buy orders. Results show that the best sets of rules found to guarantee the highest profits both in the training and in the testing periods, and perform well also in the artificial market model where the Chartists who adopt the best sets of trading rules are able to achieve higher profits.
Klaus Grobys
A total of 1.1 million bitcoins were stolen in the 2013–2017 period. Noting that the average price for a Bitcoin in 2018 was $7572 the corresponding monetary equivalent of losses is $8.9 billion highlighting the societal impact of this criminal activity. Investigating the response of the uncertainty of Bitcoin returns when hacking incidents occur, the results of this study point toward two different responses. After experiencing a contemporaneous effect at day t=0, the volatility increases significantly again at day t+5. Hacking incidents that occur in the Bitcoin market also affect the uncertainty in the Ethereum market with a time delay of five days. Notably, neither Bitcoin nor Ethereum appear to exhibit asymmetric responses to negative innovations.
Julián Andrada Félix, Adrián Fernández-Pérez, Simón Sosvilla‐Rivero
No abstract is available for this record.
Saketh Aleti, Bruce Mizrach
Abstract We study Bitcoin (BTC) trading at the Chicago Mercantile Exchange (CME) and four settlement spot exchanges that transact $146 million per day in the BTC/USD pair. Spot market median trade sizes are under $1,300 but exceed $18,000 on the CME. Bid‐ask spreads average 0.0298%. Trade sizes of over $1 million move markets by less than 1%. 2.5% of trades and 15.5% of cancellations on Coinbase take place within 50 ms. Bid‐ask spreads exceed 0.8% for only 226 s. Most executions trade‐through better quotes, with estimated losses of $36 million. The CME leads price discovery. BTC leads Ethereum price adjustment.
Sana Guizani, Ines Kahloul Nafti
The emergence of Bitcoin (BTC) has triggered intense discussions. Despite the particular interest of the public, the theoretical understanding of the value of this crypto currency is limited. This is why current research is trying to find better leads to evaluate a complex phenomenon: the BTC price. The volatility of its price presents a certain specificity compared to the traditional currencies. In order to understand the reasons for this volatility, we try to identify and to analyze the main determinants of the BTC price and to estimate their influence. We apply time series to daily data for the period from 19/12/2011 to 06/02/2018. We used several approaches, including the Auto Regressive Distributed Lag ARDL model, the cointegration test at Pesaran et al. (2001) and the Granger causality test in the sense of Toda and Yamamoto (1995). Our estimated results suggest that the number of addresses, the attractiveness indicator and the mining difficulty have a significant impact on the BTC price with variations over time. On the other hand, the transaction volume, the stock, the EUR/USD exchange rate and the macroeconomic and financial development do not determine the price of the BTC in the short term as well as in the long term.
Silvia Bartolucci, Fabio Caccioli, Pierpaolo Vivo
The Lightning Network is a so-called second-layer technology built on top of the Bitcoin blockchain to provide "off-chain" fast payment channels between users, which means that not all transactions are settled and stored on the main blockchain. In this paper, we model the emergence of the Lightning Network as a (bond) percolation process and we explore how the distributional properties of the volume and size of transactions per user may impact its feasibility. The agents are all able to reciprocally transfer Bitcoins using the main blockchain and also - if economically convenient - to open a channel on the Lightning Network and transact "off chain". We base our approach on fitness-dependent network models: as in real life, a Lightning channel is opened with a probability that depends on the "fitness" of the concurring nodes, which in turn depends on wealth and volume of transactions. The emergence of a connected component is studied numerically and analytically as a function of the parameters, and the phase transition separating regions in the phase space where the Lightning Network is sustainable or not is elucidated. We characterize the phase diagram determining the minimal volume of transactions that would make the Lightning Network sustainable for a given level of fees or, alternatively, the maximal cost the Lightning ecosystem may impose for a given average volume of transactions. The model includes parameters that could be in principle estimated from publicly available data once the evolution of the Lighting Network will have reached a stationary operable state, and is fairly robust against different choices of the distributions of parameters and fitness kernels.