This study examines Bitcoin's potential as an inflation hedge in different countries, including the United States, the Eurozone, the Philippines, Ukraine, Canada, India, and Nigeria. The study reveals varying results across countries using the Vector Error Correlation Model (VECM) with secondary monthly data from January 2012 to June 2023 for Bitcoin prices and inflation rates. Bitcoin exhibits an insignificant short-term relationship in the United States but a significant long-term negative correlation, suggesting it may not be a reliable inflation hedge. Similarly, no significant relationship was found in the Eurozone, the Philippines, Ukraine and Nigeria, indicating Bitcoin's limited effectiveness as an inflation hedge. Contrastingly, the study identifies a significant positive relationship between Bitcoin and inflation in Canada and India, indicating potential hedging against inflation within these economies. Therefore, investors, portfolio managers, and policymakers should consider these country-specific findings when evaluating Bitcoin's role as an inflation hedge. Furthermore, this study contributes valuable insights into cryptocurrencies and their potential in financial risk management.
V. Anandhabalaji, M. Babu, J. Gayathri, J. Sathya · 7 authors
The present study proposes to investigate the influence of the covid-19, on the adjusted closing price of the digital currency based on energy consumption during the process of mining. The study employed the secondary data analysis of top ten market capitalization of cryptocurrencies with the combination of high energy consume mechanism (proof of work) and low energy consume mechanism (proof of stake). Statistical tools like Descriptive analysis,Augmented Dickey-Fuller (ADF) test, ARCH, and GARCH models were used in the study. The present study finds that the prices of cryptocurrencies were highly volatile. This study could assist investors towards better understanding of the dynamics of the cryptocurrency market based on energy consumption which helps them to make more effective decisions, on investing cryptocurrencies with a scientific approach.
Bitcoin has received a great deal of attention as a highly volatile asset with investors attempting to profit from its dramatic price fluctuations. We develop a hybrid deep learning model based on feature selection in different frequency domains to enrich the literature of Bitcoin price prediction. Indicators such as Technology, Economy, Green Finance and Media Attention are considered. We first decompose all the data into different frequencies through CEEMDAN approach, and then the data at the same frequency are integrated into a Random Forest model to reduce the subset of potential predictors by measuring the importance of different factors. Finally, the selected factors are put into the LSTM/GRU to make the prediction of different components of Bitcoin prices at the same frequency, and aggregate together to obtain the predicted Bitcoin prices. The empirical results show that our proposed model outperforms the benchmark models, which is verified by MCS test. The proposed hybrid method obtains much higher return on investment in simulated trading than other benchmark models. Our study inspired the investors to accurately predict Bitcoin price and dig possible relationships between different assets and its determinants in frequency domain.
This article examines the causal relationship between stock indices and cryptocurrencies during the ongoing Russia-Ukraine war. The econometric investigation covers the period from February 24, 2022 to April 12, 2023, and focuses on seven stock market indices (S&P 500, DAX, CAC40, Nikkei, TSX, MOEX, and PFTS) and seven cryptocurrencies (Bitcoin, Ethereum, Litecoin, Dash, Ripple, DigiByte, and XEM). In this article, we investigate how investors react to fluctuations in financial assets and whether they seek safe havens in cryptocurrencies. We use dynamic causality in the Granger (1969) sense to detect a possible causal relationship in the short term, and seven models to estimate the long-term relationship between cryptocurrencies and financial assets. Our results show that in the short term, three famous cryptocurrencies (Bitcoin, Ethereum, and Ripple) and two digital assets with minor popularity (XEM and DigiByte) are impacted by the German, Russian, and Ukrainian stock markets. In the long term, we find a positive and significant effect of the American, Canadian, French and Ukrainian stock market indices on Bitcoin. These findings suggest that the stability of traditional financial markets during the current war period can be explained on the one hand by investors' fears of an unstable business climate, and on the other hand, by speculators' interest in new electronic products that are perceived as hedging instruments and safe havens in times of crisis.
Open access
Market Dynamics and Volatility
Economic Sanctions and International Relations
Environmental and Biological Research in Conflict Zones
Abstract Without theoretically specifying the future of money as an equivalent commodity of other commodities, it is impossible to reveal the recent role of the emergence of cryptocurrencies, as a reflection of speculative competition increasingly sophisticated in its technological aspect and in response to the abusive use of the spurious competition of the big banks promoting the huge financial bubbles that have haunted the world economy, such as the one unleashed from Wall Street in 2008. The explosive growth of transactions in cryptocurrencies may mean, at some point, in the capitalist economic cycle, the possibility of a new financial bubble, as well as the emergence of new swindles to investors; but valid answers can also come from those actors who until now have had to endure the almost exclusive dominance of the international monetary system by the currency issued by the US government, the main exporter of inflation on a global scale.
Purpose This paper aims to investigate the impact of banning cryptocurrencies on stock markets. Design/methodology/approach The paper uses an event study approach and data from stock market indices in nine countries that imposed a ban. It uses the constant mean model and the market model, with two different benchmarks for global returns, to analyze if any of the stock indices show abnormal returns on or around the announcement of a cryptocurrency ban. Findings The analysis shows that banning cryptocurrencies did not affect the returns of stock markets in any of the countries studied, indicating that the cryptocurrency market and stock markets are decoupled from each other, or the ban was not effectively implemented. Originality/value To the best of the author’s knowledge, this paper is the first to explore the potential spillover effect of a cryptocurrency ban on stock markets. It also bridges two strands of literature: the relationship between cryptocurrencies and traditional assets, and the impact of cryptocurrency regulation on their returns.
This research investigates the function of price discovery between the Bitcoin futures and the spot markets while also analyzing the impact of investor sentiment and attention on these markets. This study utilizes various statistical models to examine the short-term and long-term relations between these variables, including the bivariate Granger causality model, the ARDL and NARDL models, and the Johansen cointegration procedure with a vector error correction mechanism. The results suggest that there is no statistical evidence of price discovery between the Bitcoin spot price and futures, and the term structure of the Bitcoin futures neither enriches nor impairs this lead lag relation. However, the study finds robust evidence of a long-run cointegrating relation between the two markets and the presence of asymmetry in them. Moreover, this research indicates that investor sentiment exhibits a lead lag relation with both the Bitcoin futures and the spot markets, while investor attention only leads to the Bitcoin spot market, without showing any lead lag relation with the Bitcoin futures. These findings highlight the crucial role of investor behavior in affecting both Bitcoin futures and spot prices.
Muhammad Naveed, Shoaib Ali, Mariya Gubareva, Anis Omri
Using an event study approach, we examine how the forex, metal, energy, and cryptocurrency markets responded to the SVB collapse. We observe that the forex and metal markets respond positively on event and post-event days. In contrast, the cryptocurrency market reacts negatively but generates positive abnormal returns, indicating that investors may seek refuge in these purported safe-havens. However, the energy market responded adversely to the event, and the trend continued in the aftermath. The study advocates the need for monitoring and minimizing financial contagion risk due to the increased interconnectedness of the financial markets. Our findings highlight the perilous consequences of the SVB collapse, as it triggered contagious effects that may spread throughout the global financial markets. Therefore, investors and financial institutions must diversify their portfolios across various asset classes, which can help mitigate the risks of such events.
The effect of the Russia–Ukraine war has fluctuated in Europe and Asia's economic conjuncture by virtue of constant shifting balances. The portfolios of investors who made decisions in uncertain conditions have been affected by these fluctuations that have caused volatility in the stock market's indexes. The aim of this study is to examine the impact of the Fear Index (FI), the Dollar Index, and Bitcoin on the volatility of the Borsa Istanbul 100 Index (BIST). Autoregressive distributed lag (ARDL) time series analysis was used for the study, which revealed that the Dollar Index has no effect on volatility, while the FI was found to have an effect on volatility both in the short and long runs. In addition, Bitcoin was determined to have an effect on volatility only in the long run. When the period of the data used is examined, the outbreak of the Russia–Ukraine war in February 2022 is thought to be the reason for the increase in the FI. It can be assumed that the decisions of investors to invest in the BIST were adversely affected by the war as a natural consequence of this, and investors who ceased investing in the BIST index opted to invest elsewhere.
Kokulo K. Lawuobahsumo, Bernardina Algieri, Arturo Leccadito
Abstract This study aims to jointly predict conditional quantiles and tail expectations for the returns of the most popular cryptocurrencies (Bitcoin, Ethereum, Ripple, Dogecoin and Litecoin) using financial and macroeconomic indicators as explanatory variables. We adopt a Monotone Composite Quantile Regression Neural Network (MCQRNN) model to make one- and five-steps-ahead predictions of Value-at-Risk (VaR) and Expected Shortfall (ES) based on a rolling window and compare the performance of our model against the Historical simulation and the standard ARMA(1,1)-GARCH(1,1) model used as benchmarks. The superior set of models is then chosen by backtesting VaR and ES using a Model Confidence Set procedure. Our results show that the MCQRNN performs better than both benchmark models for jointly predicting VaR and ES when considering daily data. Models with the implied volatility index, treasury yield spread and inflation expectations sharpen the extreme return predictions. The results are consistent for the two risk measures at the 1% and 5% level both, in the case of a long and short position and for all cryptocurrencies.
Currently, the financial landscape is evolving very quickly, new technologies and changes in customer wishes and fulfillment time make currencies take on different forms and functions, each presenting unique challenges and opportunities. This article explores the historical development and contemporary meaning of currencies, ranging from traditional units of account such as the ECU and the SDR to the emerging association of economic power, the BRICS and the disruptive force of cryptocurrencies. The article begins by tracing the historical evolution of these currencies, shedding light on their origins and roles in international finance. It examines the influence of the ECU, SDR and BRICS and their potential in reshaping the global financial order. The rise of cryptocurrencies, their underlying technology (blockchain), and their transformative impact on traditional financial systems are also explored in depth. Common challenges and issues facing these forms of currency are identified, including regulatory complexities, volatility, security concerns, and barriers to adoption. The article examines the integration of traditional coins, simple or composite, into the cryptocurrency ecosystem, offering insights into potential solutions to address these challenges. Regarding the future, in its dynamics, the article offers a forward-looking perspective on the evolving role of these currencies in a globalized economy, highlighting opportunities for adaptation, cooperation, and resettlement of geopolitical and financial grace. The paper concludes with a call to navigate the complexities of the modern financial landscape with flexibility, innovation and attention to socio-economic impact. This article serves as a comprehensive resource for economists, policymakers, investors, companies, and individuals seeking to understand the dynamic interplay of currencies in the ever-changing world of finance.
This chapter aims to analyze the price efficiency of Bitcoin (BTC), DASH, EOS, Ethereum (ETH), LISK, Litecoin (LTC), Monero, NEO, QUANTUM, RIPPLE, STELLAR, and ZCASH in their weak form between March 1, 2018 and March 1, 2023 and determine whether they experience overreactions. The results show that cryptocurrencies exhibit positive and negative autocorrelations, which can reduce volatility and moderate price fluctuations. The results also show persistence in cryptocurrency returns, suggesting long-term trends or market patterns that individual and institutional investors can exploit. It is essential to recognize that cryptocurrencies are characterized by a high degree of complexity and instability. Investors need to monitor market trends and make the necessary adjustments to their investment strategies to anticipate market changes.
In the last few years, the cryptocurrency market, especially Bitcoin, has attracted many people, including machine learning engineers. They have heavy competition in predicting the price or the rise and fall of the price in the future. To achieve this goal, they used various types of approaches like Linear Regression, SVM, and deep learning methods like Neural networks, Recurrent neural networks like RNN, LSTM, and GRU, Bidirectional neural networks, and a combination of methods. M.L and D.L engineers used various types of information to feed their models especially emotional analysis of people. Emotions that people express on social networks, especially Twitter. The purpose of this paper is to introduce a new approach to Bitcoin trend prediction using deep learning algorithms. By sentiment analysis of extracted data from Twitter and tracking the previous price. The data collected for this research is between January 2012 and December 2020. This article compares LSTM, Bi-LSTM, GRU, and Bi-GRU algorithms to predict the trend of Bitcoin price changes. The Bi-GRU algorithm better performance by registering a record of 72% accuracy in predicting the trend of Bitcoin price changes and improving 20% the speed of the learning process.
Nov 1, 2023·2023 International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE)
Alex David S, Almas Begum, Carmel Mary Belinda M J, D Hemalatha · 5 authors
Technology and finance have experienced a change which leads to the rise of cryptocurrencies, with Bitcoin serving as a pioneer. Investors, researchers, and fans all share a fascination with bitcoin because of its decentralized structure and cryptographic security. The price volatility of Bitcoin has attracted attention and presents opportunities as well as difficulties for traders and analysts. For navigating this turbulent market, precise price prediction models are essential. In order to anticipate Bitcoin prices, this work compares Long Short-Term Memory (LSTM) with Feedforward Neural Networks (FFN). The work assesses the prediction ability of these two neural network architectures using historical pricing data and maybe other relevant factors. The comparison covers issues with anticipating Bitcoin price movements' accuracy, resilience, and generalizability.
Cryptocurrencies are a type of digital money distinguished by a decentralized system that uses encryption to authenticate transactions and keep records, obviating the need for a central authority. A key element of these digital assets is the decentralization of power from a single entity to a dispersed network. The extreme price volatility of cryptocurrencies, on the other hand, has a significant influence on international commerce, making precise price forecasting critical for investors and traders. In this study, the investigation is made on how deep learning models, especially the Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), may be used to solve the problem of excessive price swings in cryptocurrencies like as Bitcoin, Ethereum, Litecoin, and Dogecoin. Previous research has looked at forecasting methods such as ARIMA and Support Vector Machines (SVM), but the findings have fallen short of the promising results obtained by the deep learning models studied in this work. Our major objective is to create strong forecasting models based on LSTM and GRU, with an emphasis on their ability to make credible forecasts for bitcoin values. We find that GRU consistently beats LSTM for the majority of the cryptocurrencies under consideration by comparing their performance using two separate error prediction approaches, namely mean absolute percentage error (MAPE) and root mean square error (RMSE). This study's findings help to enhance prediction methods for understanding and reducing the impact of cryptocurrency price volatility on international commerce. Deep learning algorithms for projecting bitcoin values give significant insights for investors and decision-makers navigating the volatile and ever-changing terrain of the crypto market.
Kamer-Ainur Aivaz, Ionela Munteanu, Flavius Valentin Jakubowicz
Based on traditional market theory, this study aims to investigate whether conventional market investment slopes affect the unconventional Bitcoin market, considering both normal conditions and crises. This study examines three main characteristics of the economy-intensive blockchain system, namely reliability, investment slopes, financial and accounting aspects that ultimately determine the confidence in the choice to invest in cryptocurrency. The analysis focuses on the study of the Bitcoin (BTC) investment slopes during January 2014–April 2023, considering the specifics of blockchain technology and the inferences of ethics, reliability and real-world data on investment Tassets in the context of conventional regulated markets. Using an econometric model that incorporates reliability analysis techniques, factorial comparisons and multinomial regression using economic crisis periods as a dummy variable, this study reveals important findings for practical and academic purposes. The results of this study show that the investment slopes of Bitcoin (BTC) are mostly predictable for downward trends, when statistically significant correlations with the investment slopes of conventional stock markets are observable. The moderate or high increase in performance slopes pose several challenges for predictive analysis, as they are influenced by other factors than conventional regulated market performance inferences. The results of this study are of intense interest to researchers and investors alike, as they demonstrate that investment slopes analysis sheds light on the intricacies of investment decisions, allowing a comprehensive assessment of both conventional markets and Bitcoin transactions.
Purpose: This paper aims to provide an exploratory analysis of Non-Fungible Tokens (NFTs) valuation. NFTs are a new kind of digital asset born out of the dis-ruptive technologies' introduction (i.e., blockchain). A lot of small and medium en-terprises (SMEs), as innovative start-ups, are involved in this domain. Nowadays, several issues in the evaluation field remain unclear. To fill this gap, this research adopts a holistic approach is crucial to draw a clear picture of the first-time ac-counting treatment of these new digital assets. Design/methodology/approach: Using a structured approach, this research considers some of the state-of-the-art international practices and reviews some major scholars' opinions on the matter. Particularly, the study analyses the main contributions provided by international entities (e.g., European Financial Reporting Advisory Group - EFRAG, Chartered Business Valuators institute - CBV, PriceWa-terhouseCoopers - PWC), digital operators, and academia. Findings: Based on the two main strands defined for the NFT's nature, NFTs valuation issues can be resumed in twofold. The first one that considers NFTs as intangible assets suggests following the traditional valuation approaches (cost, in-come, or market) that is already a part of international accounting standards. The second strand that considers NFTs as financial assets proposes a different valua-tion approach based on quantitative methods coming mainly from finance fun-damentals. Originality/value: The originality of this study includes the different NFT val-uation approaches, which enrich the literature and can help SMEs in managing and accounting for this new kind of digital asset.