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2,964 results · page 110 of 124
Yuecheng Jia, Yangru Wu, Shu Yan, Chen-xi Yin
No abstract is available for this record.
Don M. Autore, Nicholas Clarke, Danling Jiang
No abstract is available for this record.
Arzu Tay Bayramoğlu, Çağatay Başarır
No abstract is available for this record.
Craig E. Landry, Dylan Turner, Jeffrey H. Dorfman
No abstract is available for this record.
Maximilian-Benedikt Koehn, Andrejs Cekuls
In 2008 a group of programmers, alias Satoshi Nakamoto, introduced bitcoin. Bitcoin is a cryptocurrency \nor virtual money derived from mathematical cryptography and is conceived as an alternative to government authorised \ncurrency. The founder anticipated, through bitcoin’s construction and his digital mining processes, that bitcoin prices \nwould be relatively stable. However, the recent bitcoin price decline proves that bitcoin is extraordinarily volatile and is \nnot that stable as hoped. Although some scientists have already shown that the fundamental value of bitcoin is zero, the \nprice of bitcoin has reached over 19.000$ in December 2018. Since then, bitcoin prices dropped nearly 70% from their \npeak value and showed in addition to that the typical trends of a speculative bubble. \nHyman Minsky and Charles Kindleberger discussed three different patterns of speculative bubbles. One is when price \nrises in an accelerating way and then crashes very sharply after reaching its peak. Another is when the price rises and is \nfollowed by a more similar decline after reaching its peak. The third is when the price rises to a peak, which is then \nfollowed by a period of gradual decline known as the period of financial distress, to be followed by a much sharper crash \nat some later time. One of the key findings of this study is that all these three patterns occurred during 2017-18 for the \nbitcoin price. \nTherefore, the purpose of this paper is to analyse the historical bitcoin prices in context with the typical five-step \ncharacteristics of a speculative bubble. Furthermore, each phase of a speculative bubble is explained by a behavioural \nfinance approach and answer the price development of this cryptocurrency. The result is frightening, bitcoin can be seen \nas a perfect textbook example of a speculative bubble.
Aleš Zamuda, Vincenzo Crescimanna, Juan C. Burguillo, Joana Dias · 12 authors
This chapter surveys the state-of-the-art in forecasting cryptocurrency value by Sentiment Analysis. Key compounding perspectives of current challenges are addressed, including blockchains, data collection, annotation, and filtering, and sentiment analysis metrics using data streams and cloud platforms. We have explored the domain based on this problem-solving metric perspective, i.e., as technical analysis, forecasting, and estimation using a standardized ledger-based technology. The envisioned tools based on forecasting are then suggested, i.e., ranking Initial Coin Offering (ICO) values for incoming cryptocurrencies, trading strategies employing the new Sentiment Analysis metrics, and risk aversion in cryptocurrencies trading through a multi-objective portfolio selection. Our perspective is rationalized on the perspective on elastic demand of computational resources for cloud infrastructures.
Kofi Agyarko, Albert Buabeng, Joseph Acquah
This study assessed the volatility and the Value at Risk (VaR) of daily returns of Bitcoins by conducting a comparative study in the forecast performance of symmetric and asymmetric GARCH models based on three different error distributions. The models employed are the SGARCH and TGARCH which were validated based on AIC, MAE and MSE measures. The results indicated that the SGARCHGED (1,1) with generalised error distribution term was identified as the best fitted GARCH model. Though, this best fitted model based on information loss (AIC) did not provide the best out-of-sample forecast, the differences was insignificant. Thus, the study clearly demonstrates that it is reliable to use the best fitted model for volatility forecasting. Also, to further validate the performance of the best fitted model, it was subjected to a historical back-test using Value at Risk (VaR). Though, it was evident from the study that no model was superior, it was indicated that an average loss of 1.2% is expected to be exceeded only 1% of the time. Moreover, volatility forecast from the back testing was relatively high during the first quarter of 2018 but begun decreasing steadily with time.
John Abonongo, Anuwoje Ida Logubayom, Raymond Nero
This paper explores the half-life volatility measure of three cryptocurrencies (Bitcoin, Litecoin and Ripple). Two GARCH family models were used (PGARCH (1, 1) and GARCH (1, 1)) with the student-t distribution. It was realised that, the PGARCH (1, 1) was the most appropriate model. Therefore, it was used in determining the half-life of the three returns series. The results revealed that, the half-life was 3 days, 6 days and 4 days for Bitcoin, Litecoin and Ripple respectively. This shows that, the three coins have strong mean reversion and short half-life and that it takes the respective days for volatility in each of coin to return half way back without further volatility.
Junjie Hu, Weiyu Kuo, Wolfgang K. Härdle
Cryptocurrency, the most controversial and simultaneously the most interesting asset, has attracted many investors and speculators in recent years. The visibly significant market capitalization of cryptos also motivates modern financial instruments such as futures and options. Those will depend on the dynamics, volatility, or even the jumps of cryptos. We provide a comprehensive investigation of the risk dynamics of the Bitcoin Market from a realized volatility perspective. The Bitcoin market is extremely risky in the sense of volatility, entangled jumps, and extensive consecutive jumps, which reflect the major incidents worldwide. Empirical study shows that the lagged realized variance increases the future realized variance, while the jumps, especially positive ones, significantly reduce future realized variance. The out-of-sample forecasting model reveals that, in terms of forecasting accuracy and utility gain, investors interested in the long-term realized variance benefit from explicitly modelling the jumps and signed estimators, which is unnecessary for the short-term realized variance forecast.
Sandip Mukherji
This paper studies 60 months of recent returns to examine relationships between bitcoin and 16 exchange- traded funds of currencies, bonds, stocks, commodities, and alternative assets. Bitcoin provides much higher returns, positive skewness, volatility and extreme returns, than all the other assets. Only stocks offer a better risk-return tradeoff than bitcoin. Bitcoin returns have very weak positive correlations with stocks, commodities, and alternatives. Only two funds of stocks and commodities have significant explanatory power of about 3% each for bitcoin returns. The full model of all the 16 funds explains only 15.09% of bitcoin returns. A partial model, with the six funds that are significant in the full model, explains 12.78% of bitcoin returns; 3 stock funds and 1 commodity fund have significant coefficients in this model. These findings indicate that bitcoin is a unique asset which is only weakly related to stocks and commodities. The results also show that small allocations to bitcoin improve the risk-return tradeoffs of stock and bond portfolios.
Parthajit Kayal, Purnima Rohilla
No abstract is available for this record.
Jaehyuk Choi, Heungju Park, Sungbin Sohn
No abstract is available for this record.
Jiatao Liu, Ian W. Marsh, Paolo Mazza, Mikaël Petitjean
No abstract is available for this record.
John Taskinsoy
No abstract is available for this record.
Andrea Podhorsky
No abstract is available for this record.
Mariana Durcheva, Pavel Tsankov
We investigate similarities and differences between stock and cryptocurrency networks obtained from log-return and volatility time series. We constructed correlation and Fast Fourier Transform based graphs and minimum spanning trees from a set of 100 highly capitalized cryptocurrencies and 100 highly capitalized NASDAQ stocks over a time window of fixed length. Our analysis is based on comparison between both economies in terms of network properties. We also examined distributions of node degrees and edge weights. Our results show that cryptocurrencies and companies with high capitalization tend to correspond to central and densely connected nodes. Network topologies for both economies and node degree distributions are rather similar. Nevertheless, the crypto-economy is more correlated and more strongly linked to important nodes, unlike the graphs of NASDAQ stocks, where we observed clusters of nodes having small dissimilarities.
John Taskinsoy
No abstract is available for this record.
Panagiotis Giannakaris, Panagiotis Trakadas, Theodοre Zahariadis, Panagiotis K. Gkonis · 5 authors
The evolution of the energy production and distribution towards innovative decentralized models, dictates the introduction of emerging technologies to transform the conventional energy sector into smart
Robert Hudson, Andrew Urquhart
No abstract is available for this record.
Nader Virk
Integration patterns between five leading conventional currencies after the US dollar and Bitcoin boost the investment potential of the latter relative to its hedging potential. We document that conditional Bitcoin volatility does not influence its dynamic pairwise correlations whereas the change in volatility of conventional currencies do affect the forex market integration patterns.
Zheng Nan, Taisei Kaizoji
This paper proposes a bitcoin-based triangular arbitrage, combining foreign exchanges in the bitcoin market and reverse foreign exchange spot transactions. An FX futures contract is used to reduce exposure to risk as a hedging instrument. The returns of the portfolio are jointly modeled using a bivariate DCC-GARCH model with multivariate standardized student's t disturbances due to the presence of leptokurtosis and fat tails observed. Based on the time-dependent covariance matrix, a dynamic optimal hedge ratio is formed, with a conditional correlation series as a by-product. Empirical results are obtained using Euros and U.S. dollars over the period from 21 April 2014 to 21 September 2018. Multiple rolling one-step-ahead forecasts are generated. The empirical results present bitcoin-based currency strategies dominate bitcoin trading in terms of risk management.
Tijana Šoja, Chamil W. Senarathne
This paper examines whether it is advisable to include some portion of Bitcoin in a portfolio of traditional financial assets. The goal is to explore whether Bitcoin could be a good source of diversification from the perspective of a global investor. Two portfolios have been created for this purpose: a portfolio aimed at minimizing risk and a portfolio designated as "aggressive" that offers higher rates of daily return but also a higher risk. Portfolios were created using Markowitz's optimization theory and included traditional instruments (stocks, bonds, gold) and Bitcoin. In portfolio optimization, high-frequency data (daily data) were used. The analysed period is from the end of July 2010 to the end of June 2019, which is the period of active Bitcoin trading. The results show that Bitcoin could be a good source of diversification for a portfolio that consists of traditional financial instruments, for investors trading daily. It could be a good source of diversification for the risk-averse investor and those investors who have a higher risk appetite. Considering the high volatility of Bitcoin, the investors should be very careful when they decide to include Bitcoin in a portfolio.
Richard K. Lyons, Ganesh Viswanath-Natraj
We take this question to be isomorphic to, "What Keeps Fixed Exchange Rates Fixed?" and address it with analysis familiar in exchange-rate economics. Stablecoins solve the volatility problem by pegging to a national currency, typically the US dollar, and are used as vehicles for exchanging national currencies into non-stable cryptocurrencies, with some stablecoins having a ratio of trading volume to outstanding supply exceeding one daily. Using a rich dataset of signed trades and order books on multiple exchanges, we examine how peg-sustaining arbitrage stabilizes the price of the largest stablecoin, Tether. We find that stablecoin issuance, the closest analogue to central-bank intervention, plays only a limited role in stabilization, pointing instead to stabilizing forces on the demand side. Following Tether's introduction to the Ethereum blockchain in 2019, we find increased investor access to arbitrage trades, and a decline in arbitrage spreads from 70 to 30 basis points. We also pin down which fundamentals drive the two-sided distribution of peg-price deviations: Premiums are due to stablecoins' role as a safe haven, exhibiting, for example, premiums greater than 100 basis points during the COVID-19 crisis of March 2020; discounts derive from liquidity effects and collateral concerns.
Cansu Şarkaya İçellioğlu, Selma Öner
Cryptocurrencies have emerged about ten years ago as a new form of currency and have attracted much attention since they depend on a fully decentralized system, and so their transactions are very fast and have zero transaction cost. Therefore, character of cryptocurrencies and their volatility have been discussed widely by investors, policymakers and economists in recent years. From this point of view, this study aims to explain the price volatility of cryptocurrencies with macro-financial indicators, and thereby, the effects of S&P 500 stock market index, gold price, oil price, 2-year benchmark US Bond interest rate and US Dollar index on the prices of four major cryptocurrencies, Bitcoin, Litecoin, Ethereum, and Ripple, are investigated. The study comprises a panel data analysis applied to daily data over the period of August 2016 – April 2019, and analysis results show that increases in gold price, oil price and S&P 500 index raise the prices of cryptocurrencies, while increases in 2-year benchmark US Bond interest rate and US Dollar index cause to a fall. This adverse effects of the US Dollar index and US Bond interest rate on the prices of cryptocurrencies indicates that when the value of US Dollar and US Bond yield decrease investors prefer to invest in cryptocurrencies as alternative investment instruments. On the other hand, cryptocurrencies move with a similar trend of stock market index, gold price and oil price which are overall market indicators. Thereby, findings of this study show that cryptocurrencies behave more like an investment instrument than a currency, and prices of these financial assets interact with significant macro-financial indicators.