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4,843 results · page 56 of 202
Fahad Ali, Muhammad Usman Khurram, Ahmet Şensoy, Xuan Vinh Vo
No abstract is available for this record.
Hilmi Tunahan AKKUŞ
Bu çalışmada altın ile kripto paralar arasındaki ilişkiler doğrusal olmayan modeller ile kapsamlı olarak araştırılmaktadır. Kripto paraları temsilen dijital altın olarak da adlandırılan en büyük kripto para Bitcoin ve en büyük akıllı kontrat platformu Ethereum çalışmada birlikte ele alınmaktadır. Hepsağ (2021) doğrusal olmayan eşbütünleşme testi bulgularına göre, ilgili değişkenler arasında çok zayıf düzeyde uzun dönemli ilişki, doğrusal olmayan Granger nedensellik testi sonuçlarına göre ise iki yönlü nedensellik ilişkisi tespit edilmiştir. Son olarak düzeltilmiş dinamik koşullu korelasyon (cDCC-GARCH) sonuçlarına göre altın ve kripto paralar arasında genellikle pozitif ve sıfıra yakın korelasyon bulunduğu, ancak COVID-19 salgınının görüldüğü 2020 yılı boyunca değişkenler arasındaki korelasyon ilişkisinin daha da arttığı belirlenmiştir. Elde edilen bulgular yatırımcılar için portföy çeşitlendirmesi, risk yönetimi ve piyasa öngörüsü açısından önemli bilgiler sunmaktadır.
R. Queiroz, Sérgio Adriani David
Cryptocurrencies have increasingly attracted the attention of several players interested in crypto assets. Their rapid growth and dynamic nature require robust methods for modeling their volatility. The Generalized Auto Regressive Conditional Heteroskedasticity (GARCH) model is a well-known mathematical tool for predicting volatility. Nonetheless, the Realized-GARCH model has been particularly under-explored in the literature involving cryptocurrency volatility. This study emphasizes an investigation on the performance of the Realized-GARCH against a range of GARCH-based models to predict the volatility of five prominent cryptocurrency assets. Our analyses have been performed in both in-sample and out-of-sample cases. The results indicate that while distinct GARCH models can produce satisfactory in-sample fits, the Realized-GARCH model outperforms its counterparts in out of-sample forecasting. This paper contributes to the existing literature, since it better reveals the predictability performance of Realized-GARCH model when compared to other GARCH-types analyzed when an out-of-sample case is considered.
Shi-Feng Shao, Yonglin Li, Jinhua Cheng
Cryptocurrencies are popular investment tools nowadays. Recently, with requirements for environmental friendliness, green cryptocurrencies emerged, providing market participants with new sustainable options. The interrelatedness between cryptocurrencies and green financial assets should be comprehensively examined. This article examines spillover effects among major cryptocurrencies, green cryptocurrencies, and green financial assets, based on the latest quantile connectivity framework. Cryptocurrencies are verified as net spillers generally, while green assets are net receivers. Connectedness is stronger in extreme market conditions and is obviously affected by the COVID-19 pandemic and the Russia–Ukraine War. Additionally, heterogeneity of green cryptocurrencies is evident, with net spillover direction varying over time and market conditions. The research has reference value for investors, policymakers, regulators, and environmentalists.
Sulalitha Bowala, A. Thavaneswaran, Ruppa K. Thulasiram, Md Erfanul Hoque · 5 authors
Trading volume is an important variable to successfully capture market risks along with asset price/returns. Recently, there has been a growing interest in deep learning methods to forecast the trading volume of stocks using historical volatility as a feature. Unlike the existing work, a novel datadriven log volatility forecast is proposed in this paper as an extra feature to improve trading volume forecasts. Recently, neural networks for volatility and neural nets for electricity demand forecasting, constructed with nnetar function, have shown to be superior. The novelty of this paper is to demonstrate the neural network based on the nnetar function from the forecast package in R for trading volume forecast shows superiority over the other neural network.
Inès Abdelkafi, Youssra Ben Romdhane, Sahar Loukil
The COVID-19 pandemic has challenged the notion that cryptocurrencies are uncorrelated with traditional asset markets. This study uses VAR-OLS techniques to investigate the time-varying correlation between Bitcoin and three major European stock market indices from January 4, 2016, to February 26, 2021. Our results show that cryptocurrencies and stock markets are dependent during crisis periods, but not during non-crisis periods. This confirms the time-varying correlation between cryptocurrencies and stock markets, which depends on the extent and persistence of responses to own and cross shocks. To improve the robustness of our results, we also test the impact of government measures on Bitcoin and stock market indices and find that they are both affected by these measures. Our study adds to the literature by examining the impacts of pandemics on the correlations between Bitcoin returns and the stock market, oil, and gold index returns, which have so far been unaddressed.
Renhong Wu, Md. Alamgir Hossain, H Zhang
To explore the impact of factors from the traditional financial market, such as economic policy uncertainty, oil prices, the NASDAQ index, and gold prices, to identify factors contributing to Bitcoin volatility. This study uses traditional OLS (ordinary least squares) regression analysis to examine how different external factors affect Bitcoin price volatility from January 2014 to March 2023. By employing a comprehensive approach to recognize the distinctive characteristics of the Bitcoin market, namely, 24-hour trading and the short duration of its existence, we’ve included a wide spectrum of data to ensure a cohesive comparison with other financial datasets. The findings of the statistical analysis indicate that EPU and the NASDAQ index promote positive fluctuations in Bitcoin volatility, whereas gold prices act as a dampener. Conversely, we do not find empirical support for the influence of energy prices, such as oil, on Bitcoin volatility. These findings indicate that we should not undervalue Bitcoin in any financial transaction scenario. It means that all stakeholders should treat the issue of Bitcoin volatility more seriously, even including governments, who should actively regulate the Bitcoin market, and investors, who should recognize the dangers of this volatility, make rational decisions based on individual circumstances, and employ flexible trading strategies.
Tulika Shrivastava, Basem Suleiman, Muhammad Johan Alibasa
No abstract is available for this record.
Jéfferson Augusto Colombo, Tanzina Akhter, Peter Wänke, Md. Abul Kalam Azad · 7 authors
In the rapidly evolving domain of digital finance, the interplay between cryptocurrencies and external variables such as financial and social media indicators warrants thorough examination. This investigation employs a novel, entropy-weighted Multiple Attribute Decision Making (MADM) model to decipher these intricate relationships. The study's foundation is an expansive dataset, meticulously compiled to encompass a broad spectrum of financial data alongside diverse social media indicators. Central to this analysis is the employment of the Stepwise Weight Assessment Ratio Analysis (SWARA) method, meticulously applied to ascertain the relative importance of various social media indicators. Complementing this, the Complex Proportional Assessment (COPRAS) methodology is adeptly utilized to derive utility functions for each cryptocurrency under scrutiny. The analytical prowess of neural network regressions is harnessed to delineate the influence exerted by a multitude of financial indicators on these utility functions. The findings of this research are pivotal in understanding the dynamics within the cryptocurrency market. Bitcoin and Ripple emerge as pivotal entities, primarily functioning as primary conduits for market shocks. In contrast, Ethereum is identified as a stabilizing force, predominantly absorbing such fluctuations. A nuanced aspect of this study is the differential impact of social media indicators on various cryptocurrencies. Bitcoin and Ethereum display a negative correlation with these indicators, suggesting a complex, possibly inverse relationship with social media dynamics. Conversely, Litecoin, Dogecoin, and Ripple exhibit a positive responsiveness, indicating a heightened susceptibility to social media attention, sentiment, and prevailing uncertainty.
Monika Chopra, Chhavi Mehta, Prerna Lal, Aman Srivastava
Purpose The purpose of this research is to primarily understand how crypto traders can use the Bitcoin as a hedge or safe haven asset to reduce their losses from crypto trading. The study also aims to provide insights to crypto investors (portfolio managers) who wish to maintain a crypto portfolio for the medium term and can use the Bitcoin to minimize their losses. The findings of this research can also be used by policymakers and regulators for accommodating the Bitcoin as a medium of exchange, considering its safe haven nature. Design/methodology/approach This study applies the cross-quantilogram (CQ) approach introduced by Han et al. (2016) to examine the safe-haven property of the Bitcoin against the other selected crypto assets. This method is robust for estimating bivariate volatility spillover between two markets given unusual distributions and extreme observations. The CQ method is capable of calculating the magnitude of the shock from one market to another under different quantiles. Additionally, this method is suitable for fat-tailed distributions. Finally, the method allows anticipating long lags to evaluate the strength of the relationship between two variables in terms of durations and directions simultaneously. Findings The Bitcoin acts as a weak safe haven asset for a majority of new crypto assets for the entire study period. These results hold even during greed and fear sentiments in the crypto market. The Bitcoin has the ability to protect crypto assets from sharp downturns in the crypto market and hence gives crypto traders some respite when trading in a highly volatile asset class. Originality/value This study is the first attempt to show how the Bitcoin can act as a true matriarch/patriarch for crypto assets and protect them during market turmoil. This study presents a clear and concise representation of this relationship via heatmaps constructed from CQ analysis, depicting the quantile dependence association between the Bitcoin and other crypto assets. The uniqueness of this study also lies in the fact that it assesses the protective properties of the Bitcoin not only for the entire sample period but also specifically during periods of greed and fear in the crypto market.
Valeriia Baklanova, Aleksei Kurkin, Тамара Теплова
Purpose The primary objective of this research is to provide a precise interpretation of the constructed machine learning model and produce definitive summaries that can evaluate the influence of investor sentiment on the overall sales of non-fungible token (NFT) assets. To achieve this objective, the NFT hype index was constructed as well as several approaches of XAI were employed to interpret Black Box models and assess the magnitude and direction of the impact of the features used. Design/methodology/approach The research paper involved the construction of a sentiment index termed the NFT hype index, which aims to measure the influence of market actors within the NFT industry. This index was created by analyzing written content posted by 62 high-profile individuals and opinion leaders on the social media platform Twitter. The authors collected posts from the Twitter accounts that were afterward classified by tonality with a help of natural language processing model VADER. Then the machine learning methods and XAI approaches (feature importance, permutation importance and SHAP) were applied to explain the obtained results. Findings The built index was subjected to rigorous analysis using the gradient boosting regressor model and explainable AI techniques, which confirmed its significant explanatory power. Remarkably, the NFT hype index exhibited a higher degree of predictive accuracy compared to the well-known sentiment indices. Practical implications The NFT hype index, constructed from Twitter textual data, functions as an innovative, sentiment-based indicator for investment decision-making in the NFT market. It offers investors unique insights into the market sentiment that can be used alongside conventional financial analysis techniques to enhance risk management, portfolio optimization and overall investment outcomes within the rapidly evolving NFT ecosystem. Thus, the index plays a crucial role in facilitating well-informed, data-driven investment decisions and ensuring a competitive edge in the digital assets market. Originality/value The authors developed a novel index of investor interest for NFT assets (NFT hype index) based on text messages posted by market influencers and compared it to conventional sentiment indices in terms of their explanatory power. With the application of explainable AI, it was shown that sentiment indices may perform as significant predictors for NFT sales and that the NFT hype index works best among all sentiment indices considered.
Kamyr Gomes de Souza, Flávio Barboza, Daniel Vitor Tartari Garruti
No abstract is available for this record.
Yufei Xia, Yating Fu, Z. J. Zong, Qiong Zheng
Significant climate change has aroused public attention and prompted concentrated research on its impact on the financial market. Using the index of cryptocurrency environmental attention as a proxy for climate risk, this paper investigates the impact of climate risks on cryptocurrency volatility using GARCH-MIDAS (GM)-based models. The in- and out-of-sample analyses demonstrate that climate risks can negatively affect cryptocurrency volatility. The CVI index is positively related to short-term volatility, and the inclusion of it can increase the goodness-of-fit of GM-based models. Moreover, we find that GM-X-student’s t model achieves the best out-of-sample forecasting capability and always enters the model confidence set. These conclusions remain robust for alternative data frequency, green cryptocurrencies, and train-test splits.
Md Iftekhar Hasan Chowdhury, Mudassar Hasan, Elie Bouri, Yayan Tang
No abstract is available for this record.
Shoaib Ali, Muhammad Naveed, Hasan Hanif, Mariya Gubareva
This study investigates the return spillover between the Islamic gold-backed cryptocurrencies and equity markets of the Gulf Cooperation Council (GCC) countries. The study utilizes the QVAR method to determine the quantile connectedness among the asset classes and identify optimal portfolio weights across different economic conditions. The results show that the GCC economies have stronger connections with each other than with the cryptocurrencies. However, there is an increase in connections between the GCC economies and cryptocurrencies during extreme events. This suggests that extreme news can amplify the relationship between the Islamic cryptocurrencies and GCC markets. The findings suggest that asymmetric tails exist in the connectedness between the asset classes, meaning that the relationship between them is stronger during extreme market conditions. Accordingly, the dynamic connectedness analysis reveals varying patterns of connectedness across different periods, outlining pivotal portfolio implications. The study also suggests optimal weights for portfolio managers and investors and outlines the least expensive hedging strategy. The research proposes that investors in the GCC region could potentially mitigate the risk of their Islamic equity portfolios by incorporating the Islamic Shariah-compliant gold-backed cryptocurrencies in their portfolio. Further studies could explore the role of other factors such as liquidity, market volatility, and investor sentiment in the relationship between asset classes. Future research could examine the effects of other types of news, such as macroeconomic news, on the relationship between asset classes. Additional research could focus on the implications of incorporating Islamic gold-backed cryptocurrencies in a portfolio for investors beyond the GCC region.
Burhan Erdoğan
Bu çalışmanın amacı kripto para birimi olan Bitcoin ve küresel bir etki gücüne sahip olan BRENT petrol fiyatlarının gelişmiş ve gelişmekte olan ülkelerin borsa endeksleri üzerindeki dinamik bağlantılılığının analizini gerçekleştirmektir. Analizi gerçekleştirmek amacıyla 12.11.2017 ile 19.11.2023 tarihleri arasındaki Bitcoin, BRENT petrol, Amerika Birleşik Devletleri’nden S&P500 borsa endeksi, Fransa’dan CAC borsa endeksi, Almanya’dan DAX borsa endeksi, Japonya’dan NIKKEI225 borsa endeksi, İspanya’dan IBEX35 borsa endeksi, Türkiye’den BIST100 borsa endeksi, Meksika’dan S&PBMV borsa endeksi, Endonezya’dan IDX borsa endeksi ve Suudi Arabistan’dan TADAWUL borsa endeks değişkenlerine ait haftalık veriler TVP-VAR yöntemi ile analiz edilmiştir. Çalışma sonucunda elde edilen bulgular kriz dönemlerinin varlıklar arasındaki dinamik bağlantılık ilişkisini artırmakta olduğunu ve Bitcoin ve BRENT petrol değişkenlerinin diğer borsa endeksleri tarafından etkilendiğini ortaya koymuştur. Ayrıca incelenen gelişmiş ülke borsa endekslerinin tüm dönemler itibariyle diğer değişkenleri etkilediğini bunun yanında Suudi Arabistan borsa endeksinin de diğer gelişmekte olan ülkelere göre borsa endekslerini daha fazla etkileyen bir görünüme sahip olduğunu ortaya koymuştur.
Tsz Lap Kwok
The autoregressive integrated moving average (ARIMA) model is a widely used technique for capturing past dependencies and trends in order to generate future predictions. This study presents a comparative analysis of the ARIMA model’s forecasting capabilities as applied to gold and Bitcoin prices. The methodology employed consists of obtaining historical price data, implementing machine learning techniques, fitting the ARIMA model, then validating its predictive ability using multiple error metrics. Our results indicated that the optimal ARIMA parameters for Bitcoin and gold are different, which emphasizes their different price behaviors. Additionally, the study examined implications for policy, including issues such as prices for CPUs and GPUs, the role of market dynamics, as well as the possibility for price manipulation, which is of special relevance for cryptocurrencies that exist outside the mainstream. The study also suggests potential directions for future research, such as applying advanced machine-learning techniques and adopting cross-validation. This research offers important insights regarding Bitcoin and gold price dynamics and demonstrates the applicability of the ARIMA model for financial forecasting while demonstrating the necessity of further investigation into more refined predictive models.
Shuai Chen
Modern Portfolio Theory (MPT) has long been a cornerstone in the realm of finance, aiding investors in navigating the complex terrain of risk and reward associated with diverse assets. This theory, formulated by Harry Markowitz in the 1950s, has traditionally guided investment decisions by optimizing the balance between different assets to achieve the desired level of risk and return. However, with the meteoric rise of cryptocurrencies as a new asset class, there is an increasing curiosity surrounding the applicability of MPT to this digital phenomenon. In response to this curiosity, this article undertakes the task of comprehensively assessing the compatibility of MPT with cryptocurrencies. To accomplish this, the research aggregates and analyzes the existing body of knowledge, thereby offering insights into the intersection of modern portfolio theory and the age of cryptocurrencies. A systematic literature review is conducted, encompassing 21 pertinent studies that explore various facets of this confluence. The findings of this article underscore an emerging trend in research, one that showcases the adaptability of MPT to innovative financial instruments like cryptocurrencies. These studies collectively illuminate the ways in which MPT can be employed to optimize portfolios that include digital assets, shedding light on strategies that account for the unique risk-return dynamics inherent in the crypto market. As the cryptocurrency landscape continues to evolve, it is evident that Modern Portfolio Theory is not only relevant but also adaptable, providing valuable tools to guide investors through the exciting yet volatile terrain of digital finance.
Weidong He, Jiahe Yu
With the advent of the Web3.0 era, virtual assets have gained prominence in individuals’ asset portfolios, making Non-Fungible Tokens (NFTs) increasingly significant within the financial trading landscape. To address the issue of multicollinearity in regression analysis, this paper employs Principal Component Analysis (PCA) to perform dimensionality reduction on five correlated foundational sectors. Moreover, to enhance the accuracy and reliability of predictive outcomes, the study combines the Long Short-Term Memory (LSTM) model with the Autoregressive Moving Average-Generalized Autoregressive Conditional Heteroskedasticity (ARMA-GARCH) model. Through the application of these methods and practical implementation, the study forecasts the NFT index of the Hong Kong stock market for the next 30 days. This forecasting of return volatility contributes vital insights for investment decision-making. The research complements and offers application recommendations in financial innovation, deepening, and regulation. By devising novel products and tools to meet investor demands, providing risk management and investment opportunities, the model’s predictive outcomes can be utilized in regulatory and risk management strategies within the national financial trading market. This study provides regulatory guidance, policy formulation insights, and envisions further refinements of the research methodology by integrating information shock effects.
Mingchen Li, Wencan Lin, Yunjie Wei, Shouyang Wang · 5 authors
To explain the volatility of the Bitcoin price, a total of 23 elements from four domains (Bitcoin-related indicators, financial market, exchange rates and commodities, and social sentiment) were collected. With the application of machine learning and game theory, experimental results demonstrate that S&P 500 is the most significant factor on the Bitcoin price and the safe haven effect of Bitcoin for the stock market failed when the Bitcoin price rose and the COVID-19 spread.
Oğuzhan Çepni, Ahmet Faruk Aysan
This paper explores the impact of sentiment on return spillovers among seven major Non-Fungible Tokens (NFTs). Using daily sentiment data from Thomson Reuters MarketPysch Indices and controlling for uncertainty factors and NFT sales, we examine the relationship between media sentiment and NFTs return spillovers using a TVP-VAR model. Our findings show that individual NFTs sentiment is important for spillover dynamics and the effect of sentiment changes based on market uncertainty. The study highlights the need for NFTs investors to focus on market sentiment themes rather than overall sentiment
Fathin Faizah Said, Raja Solan Somasuntharam, Mohd Ridzwan Yaakub, Tamat Sarmidi
Abstract Advanced digitalization and financial technology have of recent times become among the most crucial tools. Data mining and sentiment analysis have revealed the importance of digitalization in modern times. This study examines the influence of Google search activity on the volatility of digital assets. We analyzed six digital asset prices for Bitcoin, Bitcoin Cash, Ethereum, Ethereum Classic, Litecoin, and Ripple from the Coinmarketcap database. We used tweets on Twitter to survey users’ sentiment by using the Twitter search Application Programming Interface and Google trend search from web searches, news searches, and YouTube searches data using RStudio software. The study spanned 1 September 2019 to 31 January 2020 and employed the Vector Autoregression (VAR) approach for analysis. The VAR estimation revealed that Google search variables have significantly influenced the volatility of Bitcoin, Ethereum, Litecoin, and Ripple, as supported by the Granger causality test and impulse response function. The results of this study could be useful for investors and policymakers in drawing up strategies to reduce market volatility. These results should thus be useful to investors in developing profitable investment strategies to mitigate the impact of market turbulence.
Andrea Teruzzi
The issue related to the quantification of the tail risk of cryptocurrencies is considered in this paper. The statistical methods used in the study are those concerning recent developments in Extreme Value Theory (EVT) for weakly dependent data. This research proposes an expectile-based approach for assessing the tail risk of dependent data. Expectile is a summary statistic that generalizes the concept of mean, as the quantile generalizes the concept of the median. We present the empirical findings for a dataset of cryptocurrencies. We propose a method for dynamically evaluating the level of the expectiles by estimating the level of the expectiles of the residuals of a heteroscedastic regression, such as a GARCH model. Finally, we introduce the Marginal Expected Shortfall (MES) as a tool for measuring the marginal impact of single assets on systemic shortfalls. In our case of interest, we are focused on the impact of a single cryptocurrency on the systemic risk of the whole cryptocurrency market. In particular, we present an expectile-based MES for dependent data.
Kehinde Abiodun, Uchenna Obiageli Ogbuonyalu, Selorm Dzamefe, Ezeh Nwakaego Vera · 6 authors
The rapid proliferation of digital assets and the emergence of Central Bank Digital Currencies (CBDCs) are reshaping the global financial landscape, with significant implications for cross-border capital flows and the stability of capital markets. This review paper explores the dynamics of cross-border digital asset movements, analyzing how decentralized finance (DeFi), stable coins, and CBDCs influence liquidity, market volatility, and regulatory oversight. It investigates the potential risks posed by CBDCs to financial stability, including currency substitution, capital flight, and systemic vulnerabilities in interconnected markets. Furthermore, the paper assesses the readiness of global regulatory frameworks to address these challenges and examines the roles of interoperability, digital identity verification, and cross-jurisdictional cooperation in mitigating associated risks. Drawing from recent developments, policy reports, and empirical studies, this review provides a comprehensive analysis of how digital transformation in finance may disrupt traditional monetary mechanisms and market structures. It concludes by offering policy recommendations for ensuring resilient capital markets amid evolving digital asset ecosystems and central bank innovations.