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4,843 results · page 69 of 202
Parinaz Karimi, Majid Mirzaee Ghazani, Seyed Babak Ebrahimi
No abstract is available for this record.
Shrikant Panigrahi
The purpose of this paper is to investigate whether the cryptocurrency market affects the financial stability and economic growth of India. The study used time series quarterly data on bitcoin, financial stability, inflation rate, real GDP, economic volatility uncertainty, exchange rate, and market volatility index for the period 2015Q1â2022Q4. The robustness of the findings was confirmed by the fully modified OLS (FMOLS) and canonical cointegration regression (CCR). The study results demonstrated that an increase in cryptocurrency investments will affect the financial stability of India significantly. Each 1% increase in the cryptocurrency would reduce the financial stability by 5% approximately. However, there was a marginal effect of cryptocurrency on economic growth. The results also found that exchange rate volatility and inflationary pressure would also deteriorate the financial stability of the country. Furthermore, the results also identified positive and significant cointegration between economic growth and financial stability. Due to most transactions in the economy being done through the financial system, it is paramount for economic growth. Going forward, aggressive monetary policy tightening, volatility in capital flows and exchange rates, de-anchoring of inflation expectations, faltering in the economic recovery, disruptions due to global supply chains and climate change will be the major risks to the financial stability and economic growth of India.
Hao Feng, Da Gao, Kun Duan, Andrew Urquhart
No abstract is available for this record.
Wei Li, MingâChih Lee, Wan-Hsiu Cheng, ChiaâHsien Tang · 5 authors
In the turbulent landscape of financial markets, Bitcoin has emerged as a significant focus for investors due to its highly volatile returns. However, the risks and uncertainties associated with it necessitate effective hedging strategies. This paper explores the potential of various financial assets, including interest rates, stock markets, commodities, and exchange rates, as dynamic hedges against Bitcoinâs risk. Utilizing a DCC-GARCH model, we construct a dynamic hedging model to analyze the viability of these financial assets as hedges. The data is categorized into pre-pandemic and pandemic periods to assess any change in hedging performance due to the outbreak of COVID-19. Our empirical findings suggest that the dynamic DCC-GARCH model outperforms the static OLS model in this context. During the pandemic period, a diverse set of financial assets demonstrated enhanced efficiency in hedging Bitcoin risk compared to the pre-pandemic phase. Among the hedging commodities, stock market indices, the US dollar index, and commodity futures displayed superior performance.
Sanjeev Kumar, Reetika Jain, Narain, Faruk Balli · 5 authors
No abstract is available for this record.
Pongsathorn Pichaiyuth, Puwa Termnuphan, Tuul Triyason, Olarn Rojanapornpun · 5 authors
Investment predicated on price trends stands as one of the most prevalent and efficacious approaches, hinging on its capacity to accurately discern the price trajectory for each asset. Such a pursuit poses itself as one of the most formidable challenges within the realm of investments. In this study, the application of machine learning models is employed, while simultaneously comparing their prognostic capabilities to evaluate their performance in forecasting cryptocurrency price trends. Additionally, the normalization technique and the Shapley Additive exPlanations (SHAP) feature selection method are employed to effectively augment the aptitude for projecting cryptocurrency price trends. The prediction period encompasses the time span from January 1, 2014, to December 31, 2021. The experimental findings reveal that the Support Vector Machine (SVM) outperforms other models such as K-Nearest Neighbors (KNN), Random Forest (RFC), NaĂŻve Bayes, and Long short-term memory (LSTM) when forecasting periods extend 7, 15, and 30 days beyond the present, respectively. However, when the forecast horizon is extended to 90 days, the LSTM model exhibits the most optimal performance.
Muhammad Abubakr Naeem, Sitara Karim, Afsheen Abrar, Larisa Yarovaya · 5 authors
No abstract is available for this record.
Pasquale De Rosa, Pascal Felber, Valerio Schiavoni
Cryptocoins (i.e., Bitcoin, Ether, Litecoin) are tradable digital assets. Ownerships of cryptocoins are registered on distributed ledgers (i.e., blockchains). Secure encryption techniques guarantee the security of the transactions (transfers of coins among owners), registered into the ledger. Cryptocoins are exchanged for specific trading prices. The extreme volatility of such trading prices across all different sets of crypto-assets remains undisputed. However, the relations between the trading prices across different cryptocoins remains largely unexplored. Major coin exchanges indicate trend correlation to advise for sells or buys. However, price correlations remain largely unexplored. We shed some light on the trend correlations across a large variety of cryptocoins, by investigating their coin/price correlation trends over the past two years. We study the causality between the trends, and exploit the derived correlations to understand the accuracy of state-of-the-art forecasting techniques for time series modeling (e.g., GBMs, LSTM and GRU) of correlated cryptocoins. Our evaluation shows (i) strong correlation patterns between the most traded coins (e.g., Bitcoin and Ether) and other types of cryptocurrencies, and (ii) state-of-the-art time series forecasting algorithms can be used to forecast cryptocoins price trends. We released datasets and code to reproduce our analysis to the research community.
Utku Altunöz
This study aims to model the volatility features of Bitcoin, Ethereum, and Ripple, which are the cryptocurrencies with the greatest volumes that have come to the agenda since the global crisis, and to determine the presence and dates of price bubbles.After running the ADF and Ng-Perron unit root tests, the EGARCH model was analyzed as the best for Bitcoin and TGARCH for the Ethereum and Ripple. According to the obtained results, negative coefficients for Bitcoin imply that negative shocks will increase volatility more than positive shocks. This means that a leverage effect is present. No leverage effect was reached for Ethereum or Ripple, and positive shocks are understood to increase volatility for them compared to negative shocks. In addition, continuous speculative bubble pricing occurred for all three cryptocurrencies, with much higher bubble prices being understood to have occurred with Ethereum and Bitcoin compared to Ripple.
Indranil Ghosh, Rabin K. Jana
No abstract is available for this record.
Gideon Bruce Arkorful, Haiqiang Chen, Ming Gu, Xiaoqun Liu
No abstract is available for this record.
Nitin Shivsharan, Shardul Gajanan Kambli, Om Vaman Nikharge, Kedar Uttam Kudatarkar
Cryptocurrencies are digital assets built on blockchain technology, known for their high volatility and lack of underlying assets to justify their intrinsic value. The trading and investing in cryptocurrencies heavily rely on price trends and a few supporting parameters. To facilitate cryptocurrency analysis, our platform provides a price chart that showcases the current short-term, medium-term, and long-term trends of each currency. This analysis is supported by additional parameters such as market capitalization, 24-hour percentage-wise price change, and concise information about each element.Moreover, with the wide array of cryptocurrencies available, individuals struggle to maintain a watchlist that reflects their prioritized selection. To address this challenge, we have developed a priority watchlist that assists users in both short-term trading and long-term investing. Our solution is web-based and utilizes various methodologies including an Application Programming Interface (API) for data retrieval, a library for displaying price charts, and an algorithm for implementing the priority watchlist functionality.
Kai Meng, Khalid Khan
No abstract is available for this record.
Shaista Arshad, Thi Hong Nhung Vu, Too Shaw Warn, Loke Mei Ying
This paper examines the hedging ability of gold, silver, and Bitcoin against inflation in ASEAN countries. The inclusion of Bitcoin as a hedge is relatively new in literature and the effect of hedging has been exacerbated post Global Financial Crisis, when the prices of precious metals have increased continuously. To serve that objective, the student-t EGARCH (1,1) model is first used to study the relationship between average asset return and inflation and next a quantile regression model is applied to explore the relationship between different quantiles of asset return and inflation. This ensures the hedging potential of each asset to be equally strong in bearish and bullish conditions. The tests show that the results from student-t EGARCH (1,1) model and quantile regression model are different while the results pre and post GFC are similar in most of the cases. The quantile regression model, which accounts for different quantiles for asset returns, indicates that gold, silver, and Bitcoin appear to be a hedge and safe haven in ASEAN countries. However, from the student-t EGARCH (1,1) model, which accounts for average asset returns, Bitcoin is a hedge asset but none of the three assets serves as a safe haven in ASEAN countries.
Paolo Pagnottoni, Angelo FamĂ , JongâMin Kim
Abstract This paper explores financial networks of cryptocurrency prices in both time and frequency domains. We complement the generalized forecast error variance decomposition method based on a large VAR model with network theory to analyze the dynamic network structure and the shock propagation mechanisms across a set of 40 cryptocurrency prices. Results show that the evolving network topology of spillovers in both time and frequency domains helps towards a more comprehensive understanding of the interactions among cryptocurrencies, and that overall spillovers in the cryptocurrency market have significantly increased in the aftermath of COVID-19. Our findings indicate that a significant portion of these spillovers dissipate in the short-run (1â5 days), highlighting the need to consider the frequency persistence of shocks in the network for effective risk management at different target horizons.
Ahmad El Majzoub, Fethi Rabhi, Walayat Hussain
Summary This study explores various machine learning and deep learning applications on financial data modelling, analysis and prediction processes. The main focus is to test the prediction accuracy of cryptocurrency hourly returns and to explore, analyse and showcase the various interpretability features of the ML models. The study considers the six most dominant cryptocurrencies in the market: Bitcoin, Ethereum, Binance Coin, Cardano, Ripple and Litecoin. The experimental settings explore the formation of the corresponding datasets from technical, fundamental and statistical analysis. The paper compares various existing and enhanced algorithms and explains their results, features and limitations. The algorithms include decision trees, random forests and ensemble methods, SVM, neural networks, single and multiple features NâBEATS, ARIMA and Google AutoML. From experimental results, we see that predicting cryptocurrency returns is possible. However, prediction algorithms may not generalise for different assets and markets over long periods. There is no clear winner that satisfies all requirements, and the main choice of algorithm will be tied to the user needs and provided resources.
Mohammad Enamul Hoque, Low Soo-Wah, Aviral Kumar Tiwari, Tahmina Akhter
No abstract is available for this record.
Xiangling Wu, Shusheng Ding
No abstract is available for this record.
Jiahui Li, Haoshen Liang, Likun Ni
No abstract is available for this record.
K. Balaji, Shaik Karim, N. Gurunatha Naidu, T Venkatesh · 6 authors
The market for virtual currencies in India rose from USD 926 a million during 2020 to USD 15 billion in January 2023. As reported by regulatory bodies, cryptocurrencies popularity has grown by eight hundred percent just this time last year. This study aims to find the effect of utility, user-friendliness of investment and effect of risk in trade on investors behavior (IB) with regard to cryptocurrencies. Digital currencies are an innovative kind of asset that has arisen. Because of its exceptional growth in value, Generation Y are turning to digital currencies as a means of investing. The Technology Acceptance Model is used in this article to investigate cryptocurrency uptake. A structured closed ended questionnaires are used to collect the responses. A userâs opinion is collected based on three parameters: utility, convenience with application, and safety. For collecting information, 185 investors were consulted by provided with questionnaires that they can complete the filling of responses by themselves. The respondents were chosen according to their readiness to participate in the survey. Convergent and discriminant Validity, reliability analysis and SEM model were used to analyze the data to find the results. The utility, utility, user-friendliness of investment and management of risk in trade are all having a major impact on behavioral patterns among investors while engage with cryptocurrency, according to the findings. This study contribution will help firms to comprehend usersâ sensitivities with regard to cryptocurrencies and set various variables that influence its acceptance while delivering digital currency services to ease investment in better manner.
Florin Aliu, Alban Asllani, Simona HaĆĄkovĂĄ
Purpose Since 2008, bitcoin has continued to attract investors due to its growing capitalization and opportunity for speculation. The purpose of this paper is to analyze the impact of bitcoin (BTC) on gold, the volatility index (VIX) and the dollar index (USDX). Design/methodology/approach The series used are weekly and cover the period from January 2016 to November 2022. To generate the results, the unrestricted vector autoregression (VAR), structural vector autoregression (SVAR) and wavelet coherence were performed. Findings The findings are mixed as not all tests show the exact effects of BTC in the three asset classes. However, common to all the tests is the significant influence that BTC maintains on gold and vice versa. The positive shock in BTC significantly increases the gold prices, confirmed in three different tests. The effects on the VIX and USDX are still being determined, where in some tests, it appears to be influential while in others not. Originality/value BTCâs diversification potential with equity stocks and USDX makes it a valuable security for portfolio managers. Furthermore, regulatory authorities should consider that BTC is not an isolated phenomenon and can significantly influence other asset classes such as gold.
Samuel Asumadu Sarkodie, Mohammad Amin Amani, Maruf Yakubu Ahmed, Phebe Asantewaa Owusu
Bitcoin is a breakthrough financial technology but a volatile asset in financial markets with a complex fundamental consensus algorithm (Proof-of-Work) limiting its large-scale adoption due to environmental-related issues. Hitherto, the role of its technical and infrastructural composition that drives carbon footprint from an ecological perspective is rarely discussed in the literature. Here, we use machine learning and econometric techniques to analyze the past, present, and future changes in Bitcoin's carbon footprint with daily data spanning July 18, 2010 to December 04, 2021. We document technical drivers, decomposition effects, causal nexus, and implications of the Bitcoin blockchain's increasing energy and carbon footprint. We show that Bitcoin's technical drivers could have potential impacts on Bitcoin's carbon footprint, and subsequently, global climate change. For example, the network's hashrate increases mining difficultyââthereby increasing Bitcoin's energy consumption and subsequently, carbon footprint. We observed a direct association between the marginal effect of block size and transaction countââimplying that a higher block size improves transaction efficiency and then reduces Bitcoin's energy and carbon footprint. Besides, low mining difficulty increases market capitalization whereas increasing mining difficulty reduces bitcoin mining profit in the long run. This infers the reward for mining Bitcoin has a diminishing return in the long term. Thus, the adoption of advanced hardware for Bitcoin mining will spur energy and carbon intensity, yet will have a low return on investment. We highlight environmental regulations and regulatory changes that could limit Bitcoin's carbon footprint.
YuâJin Kwon, Kornrapat Pongmala, Kaihua Qin, Ariah KlagesâMundt · 8 authors
In May 2022, an apparent speculative attack, followed by market panic, led to the precipitous downfall of UST, one of the most popular stablecoins at that time. However, UST is not the only stablecoin to have been depegged in the past. Designing resilient and long-term stable coins, therefore, appears to present a hard challenge. To further scrutinize existing stablecoin designs and ultimately lead to more robust systems, we need to understand where volatility emerges. Our work provides a game-theoretical model aiming to help identify why stablecoins suffer from a depeg. This game-theoretical model reveals that stablecoins have different price equilibria depending on the coin's architecture and mechanism to minimize volatility. Moreover, our theory is supported by extensive empirical data, spanning $1$ year. To that end, we collect daily prices for 22 stablecoins and on-chain data from five blockchains including the Ethereum and the Terra blockchain.
Klaus Grobys
While the majority of earlier studies used autocorrelation-based methodologies to explore the dependency structure for Bitcoin, this paper follows Benoit Mandelbrot in taking a fractal point of view. It shows that both Bitcoin and S&P 500 returns exhibit fractal-like behavior. Further evidence suggests that the infinite-variance-hypothesis cannot be rejected for both assets supporting Mandelbrotâs (1963) early study on cotton price changes. This result holds across non-overlapping subsamples. Following Mandelbrot (2008), Hurst exponents are estimated using rescaled/range analysis. The key findings are that (i) Bitcoin returns exhibit a higher level of persistence than S&P 500 returns across various subsamples, (ii) the level of persistence in Bitcoin returns has not changed across time, (iii) the S&P 500 moved from efficiency in the first subsample to inefficiency in the ex-post June 17, 2018 period, (iv) even if it was assumed that the variance of S&P 500 returns is finite, the kurtosis remains statistically undefined. The study concludes that correlation-based methods used to explore the S&P 500 universe result in misleading answers.