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2,964 results · page 102 of 124
Sangjin Park, Kwahngsoo Jang, JaeâSuk Yang
No abstract is available for this record.
John W. Goodell, Stéphane Goutte
No abstract is available for this record.
Ambreen Khursheed, Muhammad Abubakr Naeem, Sheraz Ahmed, Faisal Mustafa
This study examines the adaptive market hypothesis (AMH) in relation to time-varying market efficiency by using three tests, namely Generalized Spectral (GS), Dominguez-Lobato (DL) and the automatic portmanteau test (AP) test on four-digital currencies; Bitcoin, Monaro, Litecoin, and Steller over the sample period of 2014â2018. The study applies Jarque-Bera test, ADF test, Ljung-Box statistics and ARCH-LM test for testing normality of returns, stationarity of series, serial correlation and volatility clustering in returns and squared returns of selected cryptocurrencies. Further, the study adopts an extremely important category of martingale difference hypothesis (MDH), which uses non-linear methods of dependencies for identifying changing linear and non-linear dependence in the price movement of currencies. The results indicate that price movements with linear and nonlinear dependences varies over time. Our tests also reveal that Bitcoin, Monaro and Litecoin have the longest efficiency periods. While Steller shows the longest inefficient market period. In view of varying market conditions, the results indicate that different market periods have significant impact on prices fluctuations of cryptocurrencies. Therefore, our findings suggest implementing the adaptive market hypothesis (AMH) as predicting changes in cryptocurrency prices over time must consider the time-varying market conditions for efficient forecasting.
Maria Äuljak, Bojan TomiÄ, SaĆĄa ĆœikoviÄ
No abstract is available for this record.
Tobias Burggraf, Aditya Vyas
No abstract is available for this record.
Yo-Der Song, Tomaso Aste
The Bitcoin network is burning a large amount of energy for mining. In this paper, we estimate the lower bound for the global mining energy cost for a period of ten years from 2010 to 2020, taking into account changes in energy costs, improvements in hashing technologies and hashing activity. We estimate energy cost for Bitcoin mining using two methods: Brent Crude oil prices as a global standard and regional industrial electricity prices weighted by the share of hashing activity. Despite a ten-billion-fold increase in hashing activity and a ten-million-fold increase in total energy consumption, we find the cost relative to the volume of transactions has not increased nor decreased since 2010. This is consistent with the perspective that, in order to keep the Blockchain system secure from double spending attacks, the proof or work must cost a sizable fraction of the value that can be transferred through the network. We estimate that in the Bitcoin network this fraction is of the order of 1%.
Lai T. Hoang, Dirk G. Baur
Abstract This paper studies a large number of bitcoin (BTC) options traded on the options exchange Deribit. We use the trades to calculate implied volatility (IV) and analyze if volatility forecasts can be improved using such information. IV is less accurate than AutoRegressiveâMovingâAverage or Heterogeneous AutoâRegressive model forecasts in predicting shortâterm BTC volatility (1 day ahead), but superior in predicting longâterm volatility (7, 10, 15 days ahead). Furthermore, a combination of IV and modelâbased forecasts provides the highest accuracy for all forecasting horizons revealing that the BTC options market contains unique information.
Rodney Garratt, Maarten R.C. van Oordt
We assess how the cost structure of cryptocurrency mining affects the response of miners to exchange rate fluctuations and the immutability of cryptocurrency ledgers that rely on proof-of-work. We show that the amount of mining power supplied to currencies that rely on specialized hardware, such as Bitcoin, responds less to adverse exchange rate shocks than other currencies respond to such shocks, a fact that is instrumental to avoiding double-spending attacks. The results may change if mining equipment used for one cryptocurrency can be transferred to another. For smaller currencies with low exchange rate correlation, transferability eliminates the protection that fixed costs provide. Our results weaken doomsday predictions for Bitcoin and other cryptocurrencies with declining block rewards. This paper was accepted by Bruno Biais, Special Section of Management Science: Blockchains and Crypto Economics. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2023.4901 .
MarĂa BelĂ©n ArouxĂ©t, Aurelio F. Bariviera, VerĂłnica Pastor, Victoria Vampa
Cryptocurrency history begins in 2008 as a means of payment proposal. However, cryptocurrencies evolved into a complex ecosystem of high yield speculative assets. Contrary to traditional financial instruments, they are not (mostly) traded in organized, law-abiding venues, but on online platforms, where anonymity reigns. This paper examines the long term memory in return and volatility, using high frequency time series of seven important coins. Our study covers the pre-Covid-19 and the subsequent pandemic period. We use a recently developed method, based on the wavelet transform, which provides more robust estimators of the Hurst exponent. We detect that, during the peak of Covid-19 pandemic (around March 2020), the long memory of returns was only mildly affected. However, volatility suffered a temporary impact in its long range correlation structure. Our results could be of interest for both academics and practitioners.
Yuze Li, Shangrong Jiang, Xuerong Li, Shouyang Wang
Abstract In recent years, Bitcoin has received substantial attention as potentially high-earning investment. However, its volatile price movement exhibits great financial risks. Therefore, how to accurately predict and capture changing trends in the Bitcoin market is of substantial importance to investors and policy makers. However, empirical works in the Bitcoin forecasting and trading support systems are at an early stage. To fill this void, this study proposes a novel data decomposition-based hybrid bidirectional deep-learning model in forecasting the daily price change in the Bitcoin market and conducting algorithmic trading on the market. Two primary steps are involved in our methodology framework, namely, data decomposition for inner factors extraction and bidirectional deep learning for forecasting the Bitcoin price. Results demonstrate that the proposed model outperforms other benchmark models, including econometric models, machine-learning models, and deep-learning models. Furthermore, the proposed model achieved higher investment returns than all benchmark models and the buy-and-hold strategy in a trading simulation. The robustness of the model is verified through multiple forecasting periods and testing intervals.
Yiqing Hua
The goal of this paper is to compare the accuracy of bitcoin price in USD prediction based on two different model, Long Short term Memory (LSTM) network and ARIMA model. Real-time price data is collected by Pycurl from Bitfine. LSTM model is implemented by Keras and TensorFlow. ARIMA model used in this paper is mainly to present a classical comparison of time series forecasting, as expected, it could make efficient prediction limited in short-time interval, and the outcome depends on the time period. The LSTM could reach a better performance, with extra, indispensable time for model training, especially via CPU.
Yang Hu, Yang Hou, Les Oxley, Shaen Corbet
The paper presents a novel analysis specifically investigating as to whether stocks associated with leading blockchain patent-developments influence the price volatility of Bitcoin across multiple time frequencies. It is important to further develop our understanding of the inter-dynamics between this relatively youthful financial product and pricing sensitivities associated with corporate technological advancement. Several interesting results are presented. First, Bitcoin is identified as a volatility receiver instead of a transmitter across all of the time frequencies considered during periods of patent development. Secondly, Microsoft, Mastercard, Intel and Visa contribute the largest volatility spillovers to the Bitcoin market due to patent development. Finally, for most of the companies considered, the calculated spillover effects towards Bitcoin markets are found to increase from the short-term to the long-term. These results suggest the existence of an avenue through which large corporations can influence cryptocurrency prices through their announcements of future technological intentions. The inherent risks incorporated with blockchain and cryptocurrency patent-development should be studied in detail, with particular warnings presented to those companies with no evidence of prior exposure and market knowledge.
Pierangelo De Pace, Jayant Rao
No abstract is available for this record.
Tobias Burggraf, Markus Rudolf
No abstract is available for this record.
Peterson Owusu, Anokye M. Adam, George Tweneboah
This paper explores the symmetric and asymmetric dependency structure of decomposed return series of Gold and eight cryptocurrencies to establish the hedging and diversification potentials of these asset classes. Daily data spanning 30 April 2013 to 18 April 2019 are employed within the Ensemble Empirical Mode Decomposition and Quantile-in-Quantile regression techniques. Our empirical results provide evidence that cryptocurrencies and Gold can both hedge and diversify for each other at different conditional distributions of their returns. We also find that cryptocurrencies are not purely speculative but can be driven by medium- and long-term fundamentals. In addition, both Gold and cryptocurrencies can be hedge and diversifiers for other traditional asset classes such as crude oil, fiat currencies, and other commodities.
Burcu Kapar, José Olmo
Abstract This paper proposes an empirical model for analysing the dynamics of Bitcoin prices. To do this, we consider a vector error correction model over two overlapping periods: 2010â17 and 2010â19. Price discovery is achieved through the GonzaloâGranger permanentâtransitory decomposition. The pricing factors are endogenous linear combinations of the S&P 500 index, gold price, a Google search variable associated to Bitcoin and a fear index proxied by the FED Financial Stress Index. Our empirical analysis shows that during the first period, a linear combination of four pricing factors describes the efficient Bitcoin price. The S&P 500 index and Google searches have a positive effect whereas gold prices and the fear index have a negative effect. In contrast, during the second period, the efficient price behaves idiosyncratically and can be only rationalised by individuals' search for information on the cryptocurrency. These findings provide empirical evidence on the presence of a correction in Bitcoin prices during the period 2018â19 uncorrelated to market fundamentals. We also show that standard empirical asset pricing models perform poorly for explaining Bitcoin prices.
Dean Fantazzini, Nikita Kolodin
This paper investigates the relationship between the bitcoin price and the hashrate by disentangling the effects of the energy efficiency of the bitcoin mining equipment, bitcoin halving, and of structural breaks on the price dynamics. For this purpose, we propose a methodology based on exponential smoothing to model the dynamics of the Bitcoin network energy efficiency. We consider either directly the hashrate or the bitcoin cost-of-production model (CPM) as a proxy for the hashrate, to take any nonlinearity into account. In the first examined subsample (01/08/2016â04/12/2017), the hashrate and the CPMs were never significant, while a significant cointegration relationship was found in the second subsample (11/12/2017â24/02/2020). The empirical evidence shows that it is better to consider the hashrate directly rather than its proxy represented by the CPM when modeling its relationship with the bitcoin price. Moreover, the causality is always unidirectional going from the bitcoin price to the hashrate (or its proxies), with lags ranging from one week up to six weeks later. These findings are consistent with a large literature in energy economics, which showed that oil and gas returns affect the purchase of the drilling rigs with a delay of up to three months, whereas the impact of changes in the rig count on oil and gas returns is limited or not significant.
Fabian Woebbeking
Abstract By computing a volatility index (CVX) from cryptocurrency option prices, we analyze this marketâs expectation of future volatility. Our method addresses the challenging liquidity environment of this young asset class and allows us to extract stable market implied volatilities. Two alternative methods are considered to compute volatilities from granular intra-day cryptocurrency options data, which spans over the COVID-19 pandemic period. CVX data therefore capture ânormalâ market dynamics as well as distress and recovery periods. The methods yield two cointegrated index series, where the corresponding error correction model can be used as an indicator for market implied tail-risk. Comparing our CVX to existing volatility benchmarks for traditional asset classes, such as VIX (equity) or GVX (gold), confirms that cryptocurrency volatility dynamics are often disconnected from traditional markets, yet, share common shocks.
Damian S. Damianov, Ahmed H. Elsayed
No abstract is available for this record.
Nicola Uras, Lodovica Marchesi, Michele Marchesi, Roberto Tonelli
In this article we forecast daily closing price series of Bitcoin, Litecoin and Ethereum cryptocurrencies, using data on prices and volumes of prior days. Cryptocurrencies price behaviour is still largely unexplored, presenting new opportunities for researchers and economists to highlight similarities and differences with standard financial prices. We compared our results with various benchmarks: one recent work on Bitcoin prices forecasting that follows different approaches, a well-known paper that uses Intel, National Bank shares and Microsoft daily NASDAQ closing prices spanning a 3-year interval and another, more recent paper which gives quantitative results on stock market index predictions. We followed different approaches in parallel, implementing both statistical techniques and machine learning algorithms: the Simple Linear Regression (SLR) model for uni-variate series forecast using only closing prices, and the Multiple Linear Regression (MLR) model for multivariate series using both price and volume data. We used two artificial neural networks as well: Multilayer Perceptron (MLP) and Long short-term memory (LSTM). While the entire time series resulted to be indistinguishable from a random walk, the partitioning of datasets into shorter sequences, representing different price "regimes", allows to obtain precise forecast as evaluated in terms of Mean Absolute Percentage Error(MAPE) and relative Root Mean Square Error (relativeRMSE). In this case the best results are obtained using more than one previous price, thus confirming the existence of time regimes different from random walks. Our models perform well also in terms of time complexity, and provide overall results better than those obtained in the benchmark studies, improving the state-of-the-art.
Hakan Pabuçcu, Serdar Ongan, AyĆe Ongan
Cryptocurrencies, such as Bitcoin, are one of the most controversial and complex technological innovations in today's financial system. This study aims to forecast the movements of Bitcoin prices at a high degree of accuracy. To this aim, four different Machine Learning (ML) algorithms are applied, namely, the Support Vector Machines (<i>SVM</i>), the Artificial Neural Network (<i>ANN</i>), the NaĂŻ ve Bayes (<i>NB)</i> and the Random Forest (<i>RF</i>) besides the logistic regression (LR) as a benchmark model. In order to test these algorithms, besides existing continuous dataset, discrete dataset was also created and used. For the evaluations of algorithm performances, the <i>F</i> statistic, accuracy statistic, the Mean Absolute Error (MAE), the Root Mean Square Error (RMSE) and the Root Absolute Error (RAE) metrics were used. The <i>t</i> test was used to compare the performances of the SVM, ANN, NB and RF with the performance of the LR. Empirical findings reveal that, while the <i>RF</i> has the highest forecasting performance in the continuous dataset, the <i>NB</i> has the lowest. On the other hand, while the <i>ANN</i> has the highest and the <i>NB</i> the lowest performance in the discrete dataset. Furthermore, the discrete dataset improves the overall forecasting performance in all algorithms (models) estimated.
Wanshan Wu, Aviral Kumar Tiwari, Giray Gözgör, Leping Huang
No abstract is available for this record.
Elie Bouri, Rangan Gupta, Xuan Vinh Vo
Are price discontinuities in cryptocurrencies jointly related to large swings in geopolitical risk? This is a relevant question to answer given recent news from the press that Bitcoinâs price jumps are driven by jumps in the level of geopolitical risk index. To answer this question, we examine first the jump incidence of daily returns for Bitcoin and other leading cryptocurrencies and then study the co-jumps between cryptocurrencies and the geopolitical risk index using logistic regressions. Our dataset is at the daily frequency and covers the period 30 April 2013 to 31 October 2019. The results show that the price behaviour of all cryptocurrencies under study is jumpy but only Bitcoin jumps are dependent on jumps in the geopolitical risk index. This revealed evidence of significant co-jumps for the case of Bitcoin only nicely complements previous studies arguing that Bitcoin is a hedge against geopolitical risk.
Rocco Caferra, David Vidal-TomĂĄs
No abstract is available for this record.