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4,843 results · page 102 of 202
Muhammad Abubakr Naeem, Sitara Karim, Aviral Kumar Tiwari
No abstract is available for this record.
Pascal Bruhn, Dietmar Ernst
The cryptocurrency market offers significant investment opportunities but also entails higher risks as compared to other asset classes. This article aims to analyse the financial risk characteristics of individual cryptocurrencies and of a broad cryptocurrency market portfolio. We construct a portfolio comprising the 20 largest cryptocurrencies, which cover 82.1% of the total cryptocurrency market. The returns are examined for extreme tail risks by the application of Extreme Value Theory. We utilise the GARCH-EVT approach in combination with a novel algorithm to automatically determine the optimal threshold to model the tail distribution. Furthermore, we aggregate the individual market risks with a t-Student Copula to investigate possible diversification effects on a portfolio level. The empirical analysis indicates that all examined cryptocurrencies show high volatility in their price movements, whereby Bitcoin acts as the most stable cryptocurrency. All return distributions are heavy-tailed and subject to extreme tail risks. We find strong, positive intra-market correlations, in particular with the two largest cryptocurrencies Bitcoin and Ethereum. No diversification effect can be achieved by aggregating market risks. On the contrary, a negligibly lower expected return and higher joint extreme returns can be observed. From this analysis, it can be concluded that investments in individual cryptocurrencies as well as in a portfolio show extreme risks of losses. From the investor’s point of view, a possible strategy of risk reduction through portfolio formation within cryptocurrencies is only promising to a limited extent and does not offer a satisfactory solution to significantly reduce the risk within this asset class.
Ying Guan
Cryptocurrency has evolved from a fringe phenomenon to a far more popular method of investing and financing. For investors and traders, predicting the price of bitcoin is critical. Several machine learning algorithms are utilized to anticipate the price of digital money in this research paper. The analysis employed Decision Trees, Light Gradient Boosting Machines, and Neural Networks. The purpose of this study is to look at the predicted accuracy of each machine learning method. According to the analysis, decision tree, lightGBM, and neural networks have a very high accuracy rate when it comes to forecasting cryptocurrencies. These results shed light on guiding further exploration to help investors in building an appropriate digital currency portfolio and reducing risks.
Priya Bansal, Shikha Jain
Due to the redefining of money and its price volatility, cryptocurrencies have become one of the most prominent phenomena in recent years. This research investigates how well public opinion on Twitter and news stories may be used to estimate cryptocurrency returns. Three models are designed and compared: LSTM based, LSTM-and-GRU-based, and LSTM and CNN-based models. Firstly, numerals and historical datasets of Bitcoins are used for all three models, which are further extended to Twitter and news datasets. An error score of 1015.17, 1106.71, and 3010.63 is obtained. Then, the proposed models are applied to the combined dataset of Twitter and news from Ethereum, and an error score of 47.85, 34.01, and 58.27 is obtained. Finally, the same methodology is applied to the combined dataset of Litecoin and obtained an error score of 9.45, 8.81, and 15.94. It is observed that LSTM with GRU generates the best results for all the datasets.
Fu Bing
This review focuses on blockchain technology, and its application and common problem with reference solution. The blockchain technology is nascent and complex and involves many different fields, which leads to the development of cryptocurrency. However, the crptocurrency has high volatility that demands prompt solution. Deep learning technology is considered as a promising approach to address this issue. After research, this paper develops four models with high efficiency and accuracy, including NLANN, JNN. LSTM and GRN to realize prediction in crptocurrency.
Andrew Phiri
No abstract is available for this record.
Salim Lahmiri, Stelios Bekiros, Frank Bezzina
No abstract is available for this record.
Hao Chen, Chao Xu
No abstract is available for this record.
Arianna Agosto, Paola Cerchiello, Paolo Pagnottoni
No abstract is available for this record.
Eliana Angelini, Giuliana Birindelli, Helen Chiappini, Matteo Foglia
This paper studies the dependence between the clean energy markets and brown assets (oil and Bitcoin) over the years 2011–2019. For this purpose, we use the VAR for VaR framework to capture the extreme dependence (tail risk). Moreover, we compute the Granger-causality in risk to study the impact of the Paris Agreement on these markets. We provide novel evidence of the relationship between the clean and oil markets. Notably, the results suggest that they are highly integrated in terms of risk spillover: their lagged returns, risks and extreme events influence both the VaRs of the clean energy sector and oil prices. Additionally, there is a symmetrical and an asymmetrical effect between returns and risks depending on market condition (downside/upside). The focus on the Paris Agreement demonstrates that this event is not neutral concerning the risk transmission. The effects of spillover from oil to clean energy are present before the agreement, while afterwards, we do not find evidence. Finally, the findings provide fresh insights into the relationship between clean energy and Bitcoin. The empirical analysis shows a significant spillover effect of extreme events between the two markets, suggesting a possible substitution effect.
Theodore Panagiotidis, Georgios Papapanagiotou, Thanasis Stengos
No abstract is available for this record.
Raluca Micu, Dalina Dumitrescu
Abstract Developments in digital technologies are considered to be the most important innovations since the advent of the internet. In several countries, this has led to a significant change in the way payments are made, leading to new forms of payment, such as crypto-currencies. With regard to cryptocurrencies, it remains a complex issue involving especially volatility, but also money laundering and consumer protection issues. While most countries consider cryptocurrencies too volatile to be used as a payment alternative, crypto-currencies gain interest of investors in the last 10 years due to the possibility of obtaining large profits. The aim of the paper is to study the volatility of the first 5 cryptocurrencies (Bitcoin, Ethereum, Binance Coin, Cardano and Ripple) through GARCH models. The process of evaluating highly volatile cryptocurrencies is complex and depends on many parameters. Therefore, our results would be particularly useful in terms of portfolio and risk management and could help them to be more agile in evaluating their investments, in making optimal decisions and making future forecasts. We find that the GARCH (1.1) models provide the best fit, in terms of modelling of the volatility in the most popular and largest cryptocurrencies. The results show that for BTC, ETH and XRP the appropriate model is GARCH (1.1) and in the case of BNC and CARDANO GARCH-M explain better the volatility of the crypto-currencies. Therefore, more in depth analysis of the datasets may be required to confirm or deny possible structural change. The study can be complemented by carrying out an event study on the 5 cryptocurrencies analyzed or extending the analysis by applying other GARCH models, to research the optimal model for several cryptocurrencies.
Tonghui Li
In response to the rise of the bitcoin market, the nonlinear variation of bitcoin price has always been the center of research in the community. Using bitcoin transaction data from 2014 to 2017, this study removes the uncontrollability and unpredictability of external factors and discusses the relationship between the predict and actual price of a single-feature LSTM model and a multi-feature LSTM model that incorporates thermodynamic chart to point out potentially highly correlated variables for the bitcoin price itself only, sets up a one-day prior algorithm, uses Python 3.7, Keras and LSTM tools to plot line plots of predicted and true prices and compare the accuracy of both. We conclude that the LSTM prediction is better with multiple features, which can greatly reduce the error and hedge the risk. Even in the chance case of more drastic fluctuations, the prediction is still better, which improves the utility and applicability of the model.
Ethem KILIÇ
Çalışmanın temel amacı bitcoin ile BIST30 vadeli, altın vadeli ve döviz vadeli işlemler piyasası arasındaki volatilite etkileşimini araştırmaktır. Bu doğrultuda 25.07.2010 – 13.02.2022 dönemine ait haftalık veriler kullanılmıştır. Bitcoin ile BIST30 vadeli, altın vadeli ve döviz vadeli işlemler piyasası arasındaki volatilite etkileşimini araştırmak için çok değişkenli GARCH modellerinden DCC-GARCH modeli kullanılmıştır. Bitcoin, BIST30 vadeli, altın vadeli ve döviz vadeli işlemler piyasasında meydana gelen volatilitenin kalıcı olduğu tespit edilmiştir. Bitcoin ile BIST30 vadeli işlemler piyasası arasında çift yönlü, altın ve döviz vadeli işlemler piyasasında bitcoin’e doğru tek yönlü volatilite etkileşimi bulunmaktadır. Bitcoin ve BIST30 vadeli işlemler piyasası, altın vadeli işlemler piyasasından bitcoine doğru negatif yönlü etkileşim mevcuttur. Fakat döviz vadeli işlemler piyasasından bitcoine doğru volatilite etkileşimi ise pozitif yönde olduğu saptanmıştır.
Delia Elena Diaconaşu, Seyed Mehdian, Ovidiu Stoica
Political observers predicted the Ukraine invasion by Russia for many days, but they could not precisely anticipate the scheme and timing of the invasion. This paper investigates the effects of the Russian invasion of Ukraine on the global commodity and stock markets using an event study methodology. The empirical results of this study suggest that this invasion unevenly affected the financial markets. More precisely, our results suggest that the onset of war has put pressure on global gold and stock markets. Furthermore, it seems that the only asset that could be considered a safe haven for investors after the outbreak of the invasion was oil.
Jocelyn Grira, Sana Guizani, Inès Kahloul
Purpose The purpose of this paper is to analyze the hedging capacity of Bitcoin in relation to the S&P 500 index during the COVID-19 pandemic. Design/methodology/approach In order to investigate the hedging features of Bitcoin in relation to the S&P 500 index during the COVID-19 pandemic, the authors use the Granger causality applied on a daily sample of observations ranging from January 1st, 2019 to December 31st, 2020. As robustness checks, the authors use autoregressive models to test the validity of the findings. Findings Using time series of daily data from 1st January 2019 to 31st December 2020, the results show that Bitcoin is not considered as a safe haven because it moves at the same pace as the S&P 500. As a robustness check, the authors use the exponential GARCH model and confirm our previous findings. Overall, the study contributes to the debate on both COVID-19's impact on financial systems and the hypothesis of Bitcoin being a safe haven during extreme global crises. Originality/value The study contributes to the debate on both COVID-19's impact on financial systems and the hypothesis of Bitcoin being a safe haven during extreme global crises.
Audil Rashid Khaki, Somar Al-Mohamad, Ammar Jreisat, Fadia Al-Hajj · 5 authors
The emergence of disruptive cryptocurrency platforms and decentralized finance (DEFI) has revolutionized the financial landscape over the last couple of years. Against this backdrop, and in view of the limited opportunities for diversification in the conventional assets, it becomes evident for the investors to look for better opportunities by searching for rather non-conventional assets like cryptocurrencies as a means of portfolio diversification. This study analyses the major cryptocurrencies based on their market capitalization and the major markets from MENA regions to evaluate the potential of cryptocurrencies in portfolio diversification. The study employs the mean-variance approach and then compares the results with the higher-order moments. We found that cryptocurrencies offer considerable potential for diversification for the MENA markets, but these exposures must be conservatively given the explosive price evolution and extreme volatility of the cryptocurrencies. The results also suggest that cryptocurrencies do not considerably contribute to the portfolio diversification in the uncertain market movements and crises, such as that witnessed during Fed's regime shift, accentuated by the Ukraine crisis.
Carol Alexander, Jun Deng, Bin Zou
Bitcoin derivatives positions are maintained with a self-selected margin, which is often too low to avoid automatic liquidation by the exchange, without notice, especially during periods of excessive volatility. Indeed, according to CryptoQuant, almost $80 billion of positions on centralised exchanges were liquidated during 2021, that is an average of over $200 million per day. So hedgers of bitcoin price risk should account for the possibility of automatic liquidation when taking positions on bitcoin futures. We derive a semi-closed form for an optimal hedging strategy with dual objectives – to minimize both the variance of the hedged portfolio and the probability of liquidation due to insufficient collateral. The solution depends on the statistical characteristics of the spot and futures extreme returns, and other parameters that characterize the hedger by choice of leverage, loss aversion and collateral management. An empirical analysis based on minute-level data compares the performance of the major direct and inverse bitcoin hedging instruments traded on five major exchanges.
Antonio Briola, David Vidal-Tomás, Yuanrong Wang, Tomaso Aste
We quantitatively describe the main events that led to the Terra project's failure in May 2022. We first review, in a systematic way, news from heterogeneous social media sources; we discuss the fragility of the Terra project and its vicious dependence on the Anchor protocol. We hence identify the crash's trigger events, analysing hourly and transaction data for Bitcoin, Luna, and TerraUSD. Finally, using state-of-the-art techniques from network science, we study the evolution of dependency structures for 61 highly capitalised cryptocurrencies during the down-market and we also highlight the absence of herding behaviour analysing cross-sectional absolute deviation of returns.
Zaghum Umar, Afsheen Abrar, Adam Zaremba, Тамара Теплова · 5 authors
No abstract is available for this record.
Chi-Wei He, Yung-Jang Wang
Bitcoin has attracted significant attention from investors over recent years. Due to infrequent jumps in Bitcoin prices, this paper employs the ARJI model of Chan and Maheu (2002) to describe jump risks of Bitcoin prices, and to examine the possible influencing factors of jump risks. Empirical results find that the jump component is the most important driving force of the volatility of Bitcoin returns, and that two investor sentiment indicators (the Bitcoin trading volumes and the number of Bitcoin unique addresses) are positive related to the jump risk of Bitcoin returns. These findings provide an important insight into the investment risk of Bitcoin prices.
Christian Hafner, Sabrine Majeri
No abstract is available for this record.
Muhammad Abubakr Naeem, Brian M. Lucey, Sitara Karim, Abdul Ghafoor
No abstract is available for this record.
Christian Urom, Gideon Ndubuisi, Khaled Guesmi
No abstract is available for this record.