Showing 3 results for Bitcoin
Dr Leila Torki, Mr Omid Ghorbanzadeh,
Volume 13, Issue 49 (12-2022)
Abstract
The state of development of technology in today's world is such that the development process and the future of the world in the field of technology cannot be accurately predicted. In the meantime, blockchain technology has been highly regarded as a revolutionary technology. This technology is a protocol that allows information to be exchanged directly between contracting parties in a network without the need for intermediaries. Blockchain has been one of the most important technology trends in recent years, and banking is one of those sectors that many experts believe will accept major changes from blockchain technology. Considering the revolutionary impact that blockchain technology can have on the banking system, it will be very important to examine the impact of this technology on the banking system, which represents how to create, present and acquire value in this sector. The purpose of this research is to investigate the impact of this technology in the banking system. In order to achieve this goal, the method of data collection is the type of document-library research and sample statistics, and it is quantitative-qualitative in nature, and the method is a survey, and the tools used are questionnaires and field observations. According to this research, it confirms the effectiveness of blockchain technology on the banking system. Finally, considering that blockchain technology will challenge almost all the core sectors of the banking system, it is necessary for banks to adopt a suitable strategy to deal with the threats and use the opportunities resulting from this technology.
Mr Nader Hashemnezhad, Dr Sajjad Barkhordari, Dr Ghahreman Abdoli,
Volume 14, Issue 52 (9-2023)
Abstract
Bitcoin is the leader of cryptocurrencies and has the largest market value as a digital asset in most international investment portfolios. However, compared to traditional assets, the nature of this cryptocurrency is not clear from a behavioral perspective. Examining this by following the behavior of the distribution tail or limit behaviors is one of the methods that can help researchers about the nature of this cryptocurrency, because this corresponds to the investigation of limit behaviors and in critical times of this currency. In this regard, this research has used quantile regression to estimate CAViaR models. In addition, to study the effect of each variable on the Bitcoin trend, the GARCH approach has also been used.
The results of this research for the daily period from 2018 June 26 to 2022 May 11, Wednesday, showed that by analyzing the 5% percentile quantile regression, examining the behavior of the right tail of Bitcoin distribution, the behavioral similarity of this currency with all the investigated assets is confirmed. This shows that in a situation where the returns of traditional financial markets are positive and the markets are rising, the behavior of cryptocurrencies aligns with the general behavior of the markets. However, examining the behavior of the left tail of the distribution of the variables shows that Bitcoin has no similarity in behavior with the rest of the traditional assets. In other words, when markets are bearish, Bitcoin's behavior is not aligned with traditional markets. However, the return of the homogenous index does not affect the trend of Bitcoin, which was predictable due to the non-compliance of domestic financial markets with international markets due to Iran's economic isolation and international sanctions. Therefore, until the period investigated by this study, Bitcoin has shown a behavior other than known assets and investing in it is still facing the risk of capital burnout, so it is recommended that investors observe risk management in the arrangement of their portfolios.
Seyed Fakhrodin Fakhrehosseini, Dr Meysam Kaviani,
Volume 15, Issue 55 (5-2024)
Abstract
Predicting financial asset volatility is highly important because this information can help investors make more informed decisions regarding buying and selling. Accurate predictions can also reduce financial risks and identify profitable opportunities. Ultimately, the ability to forecast market changes improves portfolio management strategies and minimizes unexpected losses for investors. This study examines and predicts Bitcoin price volatility by using innovative data analysis models. The Heterogeneous Autoregressive (HAR) model and its variants were selected as the primary tools for modeling volatility because of their high capability to analyze volatility data across different time scales. Given the unique characteristics of cryptocurrency markets and rapid, unpredictable price fluctuations, the use of models that can simultaneously capture both short- and long-term volatility is of significant importance. In this study, high-frequency historical Bitcoin price data from 2018 to 2022, covering 60-minute, daily, weekly, and monthly intervals, were analyzed using the HAR, HARJ, HARQ, and HARQJ models. The results indicate that heterogeneous models have strong predictive power for Bitcoin price volatility, and incorporating jump factors into these models further improves their forecasting accuracy.
Predicting financial asset volatility is highly important because this information can help investors make more informed decisions regarding buying and selling. Accurate predictions can also reduce financial risks and identify profitable opportunities. Ultimately, the ability to forecast market changes improves portfolio management strategies and minimizes unexpected losses for investors. This study examines and predicts Bitcoin price volatility by using innovative data analysis models. The Heterogeneous Autoregressive (HAR) model and its variants were selected as the primary tools for modeling volatility because of their high capability to analyze volatility data across different time scales. Given the unique characteristics of cryptocurrency markets and rapid, unpredictable price fluctuations, the use of models that can simultaneously capture both short- and long-term volatility is of significant importance. In this study, high-frequency historical Bitcoin price data from 2018 to 2022, covering 60-minute, daily, weekly, and monthly intervals, were analyzed using the HAR, HARJ, HARQ, and HARQJ models. The results indicate that heterogeneous models have strong predictive power for Bitcoin price volatility, and incorporating jump factors into these models further improves their forecasting accuracy.
