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4,843 results · page 50 of 202
B Soumya, Dinesh Kumar, Anita Kanavalli, S. D. Hrudhay · 7 authors
No abstract is available for this record.
Radu Lupu, Catalina Maria Popa
No abstract is available for this record.
Radovan Vojtko, Juliána Javorská
A comparative analysis between 2013-2017 and 2018-2023 reveals a significant transformation in Bitcoin and cryptocurrency investments.In the earlier phase, methods like the Markowitz Model suggested significant allocation to Bitcoin due to its high returns, diversification benefits, and low correlation with other assets.However, with the financialization of Bitcoin in December 2017, the cryptocurrency market underwent a fundamental shift, integrating into the mainstream financial system and increasing its correlation with traditional assets.In the subsequent period from 2018 to 2023, Bitcoin emerged as an average asset class with relatively high risk compared to others.Given these changes and increased institutional interest, our analysis suggests it's prudent to cap allocation to Bitcoin to maximally 2-3% of the portfolio.The analysis highlights the need for caution and realistic expectations when interpreting historical data and extrapolating long-term conclusions.
BEN ABDALLAH Mohamed, TALBI Omar
The cryptocurrency market has experienced significant growth in international finance in recent years, attracting a large number of investors. This has led to a substantial increase in the overall trading volume[1], surpassing 1.2 trillion USD in July2023. Bitcoin in particular, has garnered significant interest from both advanced and emerging economies. Notably, Bitcoin is legal tender in El Salvador and the Central African Republic. The cryptocurrency market possesses distinct characteristics that set it apart from traditional markets such as exchanges, commodities, and equities. Its decentralization, facilitated by Blockchain technology, stands as a key differentiating factor. This technology enables anonymous trading of cryptocurrencies, with the identities of market participants, account holders, and electronic wallet managers remaining unknown. Consequently, there is an inherent ambiguity when it comes to characterizing the behavior of these investors or determining their preferred trading horizons. In this context, an important question arises: how do investors in the cryptocurrency market navigate price volatility? Furthermore, how do their trading strategies, which are inherently tied to their investment horizons, impact each other and consequently influence market prices? The heterogeneity hypothesis, which is a prevalent assumption in financial markets, is particularly relevant when exploring how investors in the cryptocurrency market navigate price volatility. The diverse trading strategies and approaches to handling price fluctuations among heterogeneous investors impact and influence the overall volatility and price dynamics of cryptocurrencies. The heterogeneity market hypothesis[2] refers to the existence of differences among investors or traders regarding their beliefs, information, risk preferences, investment strategies, and time horizons. In a heterogeneous market, investors, traders and financial institutions have varying views and expectations about the future performance of assets. These differences can manifest in various ways, such as market information access, market liquidity provision, trading strategies and market structure. While numerous empirical studies have explored the concept of market heterogeneity in relation to traditional assets such as those conducted by Müller et al. (1993;1997), Lux and Marchesi (2000), LeBaron (2000), Dacorogna et al. (2001) and Benhmad (2011), there is a paucity of research on this topic specifically focusing on cryptocurrencies. This paper aims to contribute to the existing literature that investigates the hypothesis of investor heterogeneity in traditional markets, including forex, stocks, and commodities. Specifically, we focus on the cryptocurrency market, which is predominantly dominated by Bitcoin. As of November 2021[3], Bitcoin holds the distinction of being the most actively traded cryptocurrency, with a market capitalization exceeding 1.2 trillion USD. Given its significance, Bitcoin garners substantial interest from investors across various categories. Therefore, this research seeks to uncover potential synergies among participants[4] in the cryptocurrency market and identify investor types and investment horizons prevalent in this market. By understanding these factors, we can gain insights into the dynamic volatility displayed by Bitcoin’s price. To tackle this issue, we investigate Bitcoin’s price volatility, analyzing causal relationships among short-term, medium and long-term traders by using wavelet transform to decompose volatility at different trading frequency scales[5] considered, then Granger causality test will be employed to determine if changes in one scale impact others. Furthermore, we extend this analysis by employing the nonlinear causality test proposed by Hmamouche. Y (2020). This nonlinear causality test goes beyond the limitations of the linear causality test and helps identifying potential nonlinear causal effects that may exist between the volatilities at different frequency scales, which could be overlooked by the linear causality test. The rest of this paper is organized as follows: Section 2 presents the theoretical framework relatively to the heterogeneity market hypothesis. Section 3 focuses on the empirical review. Section 4 and 5 turns to the data and methodology. Section 6 provides the empirical findings. Section 7 concludes.
Levent Kutlu
No abstract is available for this record.
Vivek Pandey
No abstract is available for this record.
Wenhao XIE, Guangxi Cao
No abstract is available for this record.
Yanan Niu, Ilja Kantorovitch
No abstract is available for this record.
Daniel Cahill, Zhangxin Liu, Lee A. Smales
We investigate whether investors rely more on technical trading language to rationalise price movements in the absence of substantive information. Compared to equity markets, cryptocurrency markets are characterised by high volatility, often occurring without clear explanation from new information. We apply a machine-learning-based vocabulary of technical trading terms to comments from cryptocurrency- and equity-related subreddits on Reddit.com and analyse how investors use technical talk in different market conditions. We find a U-shaped relationship between technical talk and Bitcoin returns, with higher usage during extreme price movements, while technical talk on equity subreddits is concentrated around median market returns. Technical talk increases in cryptocurrency markets when news is scarce but rises in equity markets alongside greater news availability. Our results suggest that technical talk provides an important communication channel for social media users to describe price variation when information is scarce.
Ruzita Abdul Rahim, Nur Arissa Maisarah Nadhri, Noor Azryani Auzairy, Syahida Zainal Abidin
No abstract is available for this record.
Lumengo Bonga‐Bonga, Muhammad Khalique
No abstract is available for this record.
Kyrylo Troian
No abstract is available for this record.
Ali Asare Nezhad, Ahmad Kalhor, Reshad Hosseini, Babak Nadjar Araabi · 5 authors
No abstract is available for this record.
Audil Rashid Khaki, Nasser Elkanj, Somar Al-Mohamad, Sara Omran · 5 authors
This present study explores the potential of cryptocurrencies in the diversification of a portfolio of rather integrated financial markets or instruments, such as BRICS by employing the mean-variance optimisation approach and the higher-order moments approach. The results indicate that while the theoretical implication of both the mean-variance approach and higher-order moments approach point in the same direction, the latter is more efficient in capturing the asymmetry and the tail risk of the returns, besides the investors' risk-aversion. The results suggest that ETH is the most popular cryptocurrency for portfolio diversification followed by BTC, almost receiving the same asset allocation in different portfolio optimisation strategies. The results also indicate that the potential of cryptocurrencies in portfolio diversification is rather marginal or limited to a risk-averse investor while they may offer an alternative investment avenue for risk-seeking investors. While cryptocurrencies may promise to offer a considerable diversification potential, the allocations to cryptocurrencies must conservatively be made, given the explosive price behaviour of cryptocurrencies in recent times.
Jolana Stejskalová, Dominik Krampla
No abstract is available for this record.
Jiakun Lian
This paper delves into the intriguing realm of cryptocurrency price prediction, with a specific focus on Zcash (ZEC), employing a cutting-edge deep learning approach.The study introduces two crucial features, "close_off_high" and "volatility", then systematically analyzes the correlations between these variables and the price of ZEC.By investigating the predictive accuracy of three prominent neural network architectures-Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and the Transformer model-the study discerns that LSTM and GRU models outperform the others in forecasting ZEC's price movements.Furthermore, the paper scrutinizes the influence of different activation functions on model performance, shedding light on the effectiveness of the linear activation function in this context.The research also addresses common challenges in predictive modeling, such as overfitting and multicollinearity.Moreover, it candidly acknowledges the limitations associated with solely focusing on a single cryptocurrency, recognizing that broader research efforts and interdisciplinary collaboration are required for a more comprehensive understanding of the ever-evolving cryptocurrency landscape.As the cryptocurrency market continues to evolve rapidly, this study provides invaluable insights for investors, offering a rational perspective on cryptocurrency investment.It underscores the importance of utilizing appropriate models and embracing interdisciplinary cooperation to navigate the complex and dynamic world of cryptocurrency.By bridging the gap between the cutting-edge world of deep learning and the financial market, this research paves the way for enhanced future investigations and more informed investment decisions.
sanshao peng, Syed Shams, Catherine Prentice, Tapan Sarker
No abstract is available for this record.
Yakun Liu, Yan Chen
No abstract is available for this record.
Tran Le Nguyen, Lien Thi Kim Nguyen, Nhu-Tai Do
No abstract is available for this record.
Suwan Long, Ying Xie, Zhengyuan Zhou, Brian M. Lucey · 5 authors
No abstract is available for this record.
Suraj Velip, Mrunali Jambotkar
In this study, we analyse if changes in the US dollar index (USDX) have a volatility and asymmetric effect on stock and cryptocurrency market returns in light of the Russia-Ukraine war. We estimate an asymmetric dynamic conditional correlation model (ADCC) that shows the short-term and long-term volatility persistence and asymmetric effect for ten leading cryptocurrencies and stock markets. Except for XRP, ADA, USA and Japan, all other cryptocurrencies and stock markets exhibit significantly higher volatility spillover from USDX. The result also indicates instability in USDX has a significant asymmetric effect in seven cryptocurrencies and four stock markets. A salient contribution concerns the suggestions for policymakers to monitor the cross-market shock, volatility spillover and asymmetric effect, which will assist them in making the cryptocurrencies and stocks more immune to the shock from USDX in times of turmoil.
Asty Khairi Inayah, Lesia Fatma Ginoga, Dahri Tanjungan, Resti Jayeng Ramadhanti · 5 authors
This study analyzes the return and volatility spillover between oil-gold and oil-Bitcoin pairs before and after the COVID-19 pandemic using the Dynamic Conditional Correlation Generalized Autoregressive Conditional Heteroskedasticity (DCC-GARCH) model. The data used in this research consists of daily returns of oil, gold, and Bitcoin from January 2018 to December 2021 to understand volatility dynamics. The data period is divided into two phases: before and after theCOVID-19 pandemic. The analysis results show no significant volatility spillover between oil andgold. The relationship between oil and Bitcoin points to volatility spillover, although not following an identical pattern. The absence of volatility spillover indicates that markets or assets are more independent of each other. This reduces the interdependence between markets, making it more challenging to predict market movements based on the behavior of other markets.
Ajit Dash, Amritkant Mishra, Reeta Tomar, Lopamudra Hota
The current pragmatic investigation strives to estimate the dynamic conditional correlation (DCC) and conditional volatility of the Indian stock exchange with respect to global crude oil and bitcoin price movements. To accomplish the relevant aim, this investigation utilises the generalised autoregressive conditional heteroskedasticity (GARCH) DCC approach for the period with daily time series data ranging from April 4, 2015, to July 31, 2023. The empirical outcome reveals the presence of volatility clustering in the return series of crude oil, bitcoin, and the Indian stock market. Secondly, the outcome of DCC manifests that there is a short-run volatility spillover from crude oil to the Indian stock market; however, there is no such short-run spill existing from bitcoin to the Indian stock market. Finally, our investigation documents the long-term volatility spillover from crude oil and bitcoin price movements to the Indian stock market. Lastly, based on the outcome of conditional variance, it can be concluded that there was an increase in the return volatility of stock exchanges during the period of the COVID-19 pandemic.
Tom J. Espel
No abstract is available for this record.