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4,843 results · page 151 of 202
Zaghum Umar, Nader Trabelsi, Faisal Alqahtani
No abstract is available for this record.
Hanna Danylchuk, Liubov Kibalnyk, Oksana Kovtun, Arnold Kiv · 6 authors
In this article, we present the results of simulation for cryptocurrency market based on fractal and entropy analysis using six cryptocurrencies in the first 20 of the capitalization rating. The application of the selected research methods is based on an analysis of existing methodologies and tools of economic and mathematical modeling of financial markets. It has been shown that individual methods are not relevant because they do not provide an adequate assessment of the given market, so an integrated approach is the most appropriate. Daily values of cryptocurrency pairs from August 2016 to August 2020 selected by the monitoring and modelling database. The application of fractal analysis led to the conclusion that the time series of selected cryptocurrencies were persistent. And the use of the window procedure for calculating the local Hurst coefficient allowed to detail and isolate the persistant and antipersistant gaps. Interdisciplinary methods, namely Tsallis entropy and wavelet entropy, are proposed to complement the results. The results of the research show that Tsallis entropy reveals special (crisis) conditions in the cryptocurrency market, despite the nature of the crises’ origin. Wavelet entropy is a warning indicator of crisis phenomena. It provides additional information on a small scale.
Jong‐Min Kim, Seong‐Tae Kim, Sangjin Kim
This paper examines the relationship of the leading financial assets, Bitcoin, Gold, and S&P 500 with GARCH-Dynamic Conditional Correlation (DCC), Nonlinear Asymmetric GARCH DCC (NA-DCC), Gaussian copula-based GARCH-DCC (GC-DCC), and Gaussian copula-based Nonlinear Asymmetric-DCC (GCNA-DCC). Under the high volatility financial situation such as the COVID-19 pandemic occurrence, there exist a computation difficulty to use the traditional DCC method to the selected cryptocurrencies. To solve this limitation, GC-DCC and GCNA-DCC are applied to investigate the time-varying relationship among Bitcoin, Gold, and S&P 500. In terms of log-likelihood, we show that GC-DCC and GCNA-DCC are better models than DCC and NA-DCC to show relationship of Bitcoin with Gold and S&P 500. We also consider the relationships among time-varying conditional correlation with Bitcoin volatility, and S&P 500 volatility by a Gaussian Copula Marginal Regression (GCMR) model. The empirical findings show that S&P 500 and Gold price are statistically significant to Bitcoin in terms of log-return and volatility.
Aman Gupta, Himanshu Nain
No abstract is available for this record.
Trung H. Le, Hung Xuan, Duc Khuong Nguyen, Ahmet Şensoy
No abstract is available for this record.
Abhishek Subramanian, Balaga Mohana Rao
Rational investors look into maximizing returns with minimal risk. Since this is highly unlikely, optimizing risk and return is a practical solution. Bitcoin is a new financial product that can be included in an investment portfolio. This paper looks at Bitcoins as a separate asset class and attempts to capture the volatility using the Exponential GARCH (E‐GARCH) as well as to check if Bitcoins can be used as an optimal tool to hedge using the Dynamic Conditional Correlation GARCH against four traditional asset classes in the U.S. economy which includes the stock market (S&P 500 index), Bonds (U.S. Aggregate Bond Index), Gold and Crude Oil. The period of study is a little over 7 years. The results suggest that Bitcoin stands as a highly speculative class of asset with extremely high volatility and with respect to hedging, Bitcoin stands as a possible tool of hedge with the U.S. Aggregate Bond index and to a certain extent against Gold but fails to be an optimal hedge against the S&P 500 and Crude Oil in the U.S. economy between April 29, 2013 and October 31, 2019 due to its highly volatile nature.
Najaf Iqbal, Zeeshan Fareed, Guangcai Wan, Farrukh Shahzad
No abstract is available for this record.
Stelios Bekiros, Axel Hedström, Evgeniia Jayasekera, Tapas Mishra · 5 authors
No abstract is available for this record.
Thi Ngoc Lan Le, Emmanuel Joel Aikins Abakah, Aviral Kumar Tiwari
No abstract is available for this record.
Christy Dwita Mariana, Irwan Adi Ekaputra, Zaäfri A. Husodo
No abstract is available for this record.
Ahmed Jeribi, Mohamed Fakhfekh
The purpose of this paper is to discuss the determinants of G7, and Chinese stock market returns during the COVID-19 outbreak. We find that Bitcoin and Ethereum can generate benefits from portfolio diversification and hedging strategies for G7 financial investors in early 2020. Our result reveals that Gold is neither hedge nor haven during the COVID-19 pandemic. In addition, the results indicated that the expected volatility of the US stock market has no effect on the Japanese and Chinese financial markets. Finally, our results suggest that the growth rate of confirmed COVID-19 cases and deaths has an impact only on the US stock market.
Khaled Mokni, Ahdi Noomen Ajmi, Elie Bouri, Xuan Vinh Vo
No abstract is available for this record.
Mason Eugene McCoy, Shahram Rahimi
Trading cryptocurrencies (digital currencies) are currently performed by applying methods similar to what is applied to the stock market or commodities; however, these algorithms are not necessarily well-suited for predicting cryptocurrency prices. Unlike stock exchanges, which shut down for several hours or days at a time, digital currency prediction and trading seem to be of a more consistent and predictable nature. In this work, we benefit from sentiment analysis of tweets using both an existing sentiment analysis package and a manually tailored “objective analysis,” to calculate one impact value for each analysis every 15[Formula: see text]min. We then select the most appropriate training method by applying evolutionary techniques and discover the best subset of the generated features to include, as well as other parameters. One of the unique contributions of this work is the analysis of both English and Japanese tweets with a tailored “objective analysis” tool. This resulted in implementation of predictors which yielded 28% to 122% profit in a four-week simulation, much more than simply holding a digital currency for the same period of time.
Jingping Li, Bushra Naqvi, Syed Kumail Abbas Rizvi, Hsu‐Ling Chang
No abstract is available for this record.
Mert Baran Tunçel, Samet Gürsoy
21. yüzyıl toplumsal alanda birçok yeniliği beraberinde getirirken hiç şüphesiz küresel piyasalar açısından da değişim kaçınılmaz olmuştur. Bu değişikliklerden biri de piyasalardaki risk algısı olmuştur. Riskin yönetilmesi her geçen gün daha da önemli hale gelmektedir. Günümüzde uluslararası piyasalarda oluşan finansal risklerin ölçülmesine olanak tanıyan birçok risk endeksi olmakla birlikte en çok takip edilenlerden birinin de VIX korku endeksidir. Uluslararası yatırım kararı alınırken bu endeks yol gösterici olmakta ve özellikle finansal piyasalardaki fonların yönetilmesinde önemli rol oynamaktadır. Finansal piyasalarda ortaya çıkan başka bir yenilik ise kripto para piyasaları olurken, uluslararası yatırımcının ilgisi her geçen gün daha da artmakta ve hatta bu paralarla alışveriş yapılmasını özendiren kurumların sayısı da artış göstermektedir. Bu bağlamda Bitcoine olan bu ilgi bu çalışmanın da ortaya çıkmasında motivasyon kaynaklarından biri olmuştur. Bu çalışmada 06.08.2010 ile 06.01.2020 dönemleri arasında günlük Bitcoin fiyatları ile BİST100 ve VIX korku endeksi arasındaki nedensellik ilişkisi test edilmiştir. Öncelikli olarak yapısal kırılmayı dikkate alan Zivot-Andrews testi ile durağanlık sınanmış ve daha sonra Toda Yamamoto nedensellik analizi gerçekleştirilmiştir. Çalışmanın sonucunda Bitcoin fiyatlarının her iki değişken üzerinde anlamlı bir ilişki içinde olmadığı görülürken, VIX endeksinden BİST100 endeksine doğru tek yönlü bir nedensellik etkisi gerçekleştirdiği tespit edilmiştir.
Imran Yousaf, Shoaib Ali
Using intraday data, this study employs the VAR-DCC-GARCH model to examine return and volatility transmission among Bitcoin, Ethereum, and Litecoin during the pre-COVID-19 and COVID-19 periods. We find that the return spillovers differ across both periods for the Bitcoin-Ethereum, Bitcoin-Litecoin, and Ethereum-Litecoin pairs. The volatility transmission is not significant between cryptocurrencies during the pre-COVID-19 period. We also find that the volatility spillover is unidirectional from Bitcoin to Ethereum and bidirectional between Ethereum and Litecoin during the COVID-19 period. Moreover, volatility transmission is not significant between Bitcoin and Litecoin during the COVID-19 period. The dynamic conditional correlations between all pairs of cryptocurrencies are higher during the COVID-19 period than during the pre-COVID-19 period. Lastly, we compute the optimal portfolio weights, time-varying hedge ratios, and hedging effectiveness for all pairs of cryptocurrencies during the pre-COVID-19 and COVID-19 periods. Overall, our findings provide new insights into channels of information transmission, which may improve the investment decisions and trading strategies of portfolio investors during crisis and non-crisis periods.
Bekarys Martzhan
The emergence of cryptocurrency has introduced a transformative force in the global financial landscape, challenging the conventional structures of traditional financial markets. This paper explores the dynamic relationship between digital currencies and established financial systems, focusing on areas such as investment behavior, regulatory responses, market volatility, and the evolving role of financial institutions. It highlights how cryptocurrencies, particularly Bitcoin and Ethereum, have begun to influence asset allocation strategies, capital flows, and risk perceptions among investors. Furthermore, the paper examines the integration of blockchain technology in financial services and how its decentralized nature poses both opportunities and threats to conventional banking practices. While cryptocurrencies have opened up avenues for innovation and financial inclusion, their unregulated nature raises concerns regarding market stability and security. This study aims to provide a comprehensive understanding of the implications of cryptocurrency growth for traditional financial markets, suggesting the need for adaptive regulatory frameworks and strategic responses from financial institutions.
Hongwei Zhang, Peijin Wang
No abstract is available for this record.
Binh Quang Nguyen, Thai‐Ha Le, Canh Phuc Nguyen
Abstract This study examines the influences of different types of uncertainty, namely, World Uncertainty (WUI), Global Economic Policy Uncertainty (GEPU), and Geopolitical Uncertainty (GUI) on the returns and liquidity of 964 cryptocurrencies over the period from April 28, 2013 to July 14, 2018. Besides the full sample, three sub‐portfolios are separated by market capitalization. The principal findings are: (i) increased GEPU has significantly negative effect on the cryptocurrency portfolios' returns; (ii) higher WUI has significantly negative impact on the cryptocurrency portfolios' liquidity, especially for the medium sub‐portfolio; (iii) the effects of uncertainty proxied by GEPU and WUI on cryptocurrency portfolios' returns and liquidity, respectively, are asymmetric; and (iv) GUI is not found to have any significant impact on the returns and liquidity of cryptocurrency portfolios.
Saralees Nadarajah, Emmanuel Afuecheta, Stephen Chan
No abstract is available for this record.
Tak Kuen Siu, Robert J. Elliott
This paper aims to study the pricing of Bitcoin options with a view to incorporating both conditional heteroscedasticity and regime switching in Bitcoin returns. Specifically, a nonlinear time series model combining both the self-exciting threshold autoregressive (SETAR) model and the generalized autoregressive conditional heteroscedastic (GARCH) model is adopted for modeling Bitcoin return dynamics. Specifically, the SETAR model is used to model regime switching and the Heston-Nandi GARCH model is adopted to model conditional heteroscedasticity. Both the conditional Esscher transform and the variance-dependent pricing kernel are used to specify pricing kernels. Numerical studies on the Bitcoin option prices using real bitcoins data are presented.
Andrei Shynkevich
Abstract This study examines the informational efficiency of the bitcoin spot market by evaluating the predictive power of mechanical trading rules designed to exploit price continuation. Significant return predictability is found until the introduction of bitcoin futures in December 2017. The forecasting ability of trend‐chasing trading rules declines dramatically afterwards. Although evidence suggests that the introduction of bitcoin futures has increased the informational efficiency of the bitcoin spot market, no signs of improvement in informational efficiency are found in ethereum, the second‐largest cryptocurrency—following the introduction of bitcoin futures.
Yiming Miao, Jeungeun Song, Haoquan Wang, Long Hu · 6 authors
With the increase in natural gas consumption, distributed natural gas supply and transaction have become new development goals of the industrial Internet of Things (IoT) for natural gas. However, there are obvious disadvantages of the existing natural gas pipeline network in aspects of infrastructure warning, multilevel data transmission, automatic transaction, and security. Emerging technologies, such as blockchain, edge computing, and AI have been introduced to address these shortcomings. This article proposes Smart Micro-GaS, i.e., the concept of a cognitive micro natural gas industrial ecosystem based on mixed blockchain and edge computing. Three aspects, multilevel, multiview, and multidimension, are put forward for its design and deployment. Then, based on the most important smart contract algorithm in blockchain, a mixed transaction model for natural gas is established. Finally, a case analysis is conducted on a smart natural gas testbed for data prediction and the proposed smart contract algorithm. The framework proposed in this article makes the natural gas data have multilevel liquidity and realizes diversified transactions.
Vasily Derbentsev, Natalia Datsenko, Vitalina Babenko, Olha Pushko · 5 authors
This paper is devoted to the problems of the short-term forecasting cryptocurrencies time series using machine learning approach. We applied two the most powerful ensembles methods: Random Forests (RF) and Gradient Boosting Machine (GBM). For testing models we used the daily close prices of three the most capitalized coins: Bitcoin (BTC), Ethereum (ETH) and Ripple (XRP), and as a features were selected the past price information and technical indicators (moving average). To check the efficiency of these models we made out-of-sample forecast for three cryptocurrencies by using one step ahead technique. As the accuracy rate for our models we were selected Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) metrics. According to comparative analysis of the predictive ability of the RF and GBM both models showed the same order of accuracy for the out-of-sample dataset prediction, although boosting also was somewhat more accurate. Computer experiments have confirmed the feasibility of using the machine learning ensembles approaches considered for the short-term forecasting of cryptocurrencies time series. Built models and their ensembles can be used as the basis algorithms for automated Internet trading systems.