The emergence of cryptocurrencies has posed challenges to governments in managing the money supply. For years, governments have exercised monetary policy by controlling the supply of national currencies. However, cryptocurrencies are decentralized, meaning governments have little or no power to control them. This chapter provides a literature review on the impact of cryptocurrencies on (1) the national monetary policy, (2) the international monetary system, and (3) the role of cryptocurrency within the banking system. Research reveals that governments could consider developing their own cryptocurrency to maintain power and influence the money supply. Alternatively, they can use the legal framework to enable or disable cryptocurrency as legal tender within their jurisdictions. Due to its global nature, cryptocurrency can be used as an international payment method and become an integrated part of the FOREX market. Lastly, as cryptocurrency continues to gain popularity worldwide, regulations on crypto exchanges and issuers will be needed to avoid price bubbles.
Contemporarily, Bitcoin has enjoyed great popularity worldwide based on the development of the blockchain technology. Dozens of researches have been done on forecasting the price of bitcoin, however, the most accurate model remains inconclusive. This paper investigates the performances of Bitcoin price prediction based on ARIMA model and ordinary least square multifactorial linear model in terms of the dataset from May 2019--April 2022 in both short-term and long-term. According to the analysis, linear regression model outperforms ARIMA model in short term test which is significantly accurate. Nevertheless, in terms of long-term prediction, although the linear regression model still performs better than ARIMA model. None of them shows great accuracy on account of uncertain changes that some assumptions are no longer applicable. Therefore, these results shed light on guiding further exploration focusing on cryptocurrency pricing.
Maruf Yakubu Ahmed, Samuel Asumadu Sarkodie, Thomas Leirvik
We examine the relationship between the top five cryptos and the U.S. S&P500 index from January 2018 to December 2021. We use the novel General-to-specific Vector Autoregression (GETS VAR) and traditional Vector Autoregression (VAR) model to analyze the short- and long-run, cumulative impulse-response, and Granger causality test between S&P500 returns and the returns of Bitcoin, Ethereum, Ripple, Binance and Tether. Additionally, we used the Diebold and Yilmaz (DY) spillover index of variance decomposition to validate our findings. Evidence from the analysis suggests positive short- and long-run effects of historical S&P500 returns on Bitcoin, Ethereum, Ripple, and Tether returns--and negative short- and long-run effects of the historical returns of Bitcoin, Ethereum, Ripple, Binance, and Tether on S&P500 returns. Alternatively, evidence suggests a negative short- and long-run effect of historical S&P500 returns on Binance returns. The cumulative test of impulse-response indicates a shock in historical S&P500 returns stimulates a positive response from cryptocurrency returns while a shock in historical crypto returns triggers a negative response from S&P500 returns. Empirical evidence of bi-directional causality between S&P500 returns and crypto returns suggest the mutual coupling of these market. Although, S&P500 returns have high-intensity spillover effects on crypto returns than crypto returns have on S&P500. This contradicts the fundamental attribute of cryptocurrencies for hedging and diversification of assets to reduce risk exposure. Our findings demonstrate the need to monitor and implement appropriate regulatory policies in the crypto market to mitigate the potential risks of financial contagion.
The present study conducts a dynamic conditional cross-correlation and time–frequency correlation analyses between cryptocurrency and equity markets in both advanced and emerging economies. The purpose of the study is twofold. First, the study investigates the presence of the pure (narrow) form of financial contagion between cryptocurrency and stock markets in both advanced and emerging economies, during the black swan event of the COVID-19 crisis. Second, the study examines the hedging and safe-haven properties of cryptocurrencies against equity markets, before and during periods of financial upheaval triggered by the COVID-19 pandemic. Two econometric models are used: (1) the dynamic conditional correlation (DCC) GARCH and (2) the wavelet analysis models. Using the DCC GARCH model, the study found the evidence of high conditional correlations between cryptocurrency and equity markets. The high conditional correlation was mostly detected in periods of financial turmoil corresponding to the first quarter and the second quarter of 2020. The increase in conditional correlation during periods of financial upheaval (compared to a tranquil period) indicates the presence of the pure form of financial contagion. The wavelet cross-correlation analysis showed the evidence of positive cross-correlation between the Bitcoin and the equity markets during period of financial turmoil. The cross-correlation was identified in both short and long (coarse) scales. In short scales, the equity markets lead the cryptocurrency market, while the cryptocurrency market leads equity markets in coarse scales. The findings of the present study revealed that the degree of interdependence between cryptocurrency and equity markets has substantially increased during the COVID-19 period, and this has negated the safe-haven and hedging benefits of cryptocurrencies over equity markets.
This article explores the complexities of cryptocurrency price volatility during times of crisis. We analyze time series data with long-term memory or long-range dependence to understand the impacts of crises on cryptocurrency prices. Specifically, we examine the effects of the Covid-19 pandemic and the Russo-Ukrainian war on cryptocurrency markets, as well as the role of investor sentiment in price fluctuations during periods of uncertainty. To do so, we use fractionally integrated models to analyze the short- and long-term effects of these external factors on cryptocurrency prices. Our study mainly focuses on Bitcoin returns volatility using specific fractionally integrated models during four sub-period of historical crises from 2014. It assesses and compares the fractionally integrated models of the GARCH, the FIGARCH-BBM, the FIGARCH-CHUNG, FIEGARCH, and the FIAPARCH-BBM during the sub-periods of the pre-Covid-19, of the Covid-19 situation, between the Covid-19 and the Russo-Ukrainian War, and of the Russo-Ukrainian War. Conditional volatility models' parameters are first estimated from the four sub-sample data series BTC/USD exchange rate returns and it is calculated. Estimated conditional volatilities are then compared to specific volatilities relying on information criteria, after which the models are ranked. Finally, we test the specifics fractionally integrated volatility models with the normality test, the Q-Statistics on Standardized Residuals Test, the ARCH Test, and the graphic analysis. The specific volatility model of the first sub-period pre-Covid-19 is FIAPARCH-BBM (2,1). BTC/USD returns evolution during the Covid-19 crisis indicates that the FIEGARCH (2,2) is the appropriate volatility model. In addition, our results find that the FIEGARCH (2,1) is the appropriate model of volatility over the third sub-period and during the Russo-Ukrainian War period. By extrapolating the results of the four events, the study showed that the series of BTC/USD returns sampled over the four sub-periods were not immune to risk leading to historical crisis situations. The fluctuations of Bitcoin data during a political or economic event influence the choice of volatility models and their coefficients. More specifically, the parameters of the determined models of conditional volatility show that a war will make cryptocurrency more important on the exchange market even than an epidemic in the example of Covid-19. Our results suggest that the pandemic and geopolitical tensions have had a significant impact on cryptocurrency prices, but investor sentiment has played a crucial role in exacerbating price volatility. Additionally, we demonstrate the effectiveness of fractionally integrated models in predicting cryptocurrency prices during times of crisis. In summary, this study provides important insights into the dynamics of cryptocurrency markets during global crises, highlighting the need for sophisticated modeling techniques to effectively capture the complexities of these markets.
Federico Cernera, Massimo La Morgia, Alessandro Mei, Alberto Maria Mongardini · 5 authors
In the world of cryptocurrencies, public listing of a new token often generates significant hype, in many cases causing its price to skyrocket in a few seconds. In this scenario, timing is crucial to determine the success or failure of an investment opportunity. In this work, we present an in-depth analysis of sniper bots, automated tools designed to buy tokens as soon as they are listed on the market. We leverage GitHub open-source repositories of sniper bots to analyze their features and how they are implemented. Then, we build a dataset of Ethereum and BNB Smart Chain (BSC) liquidity pools to identify addresses that serially take advantage of sniper bots. Our findings reveal 14,029 sniping operations on Ethereum and 1,395,042 in BSC that bought tokens for a total of $10,144,808 dollars and $18,720,447, respectively. We find that Ethereum operations have a higher success rate but require a larger investment. Finally, we analyze token smart contracts to identify mechanisms that can hinder sniper bots.
We investigate the connectedness of automated market makers (AMM) that play a pivotal role in liquidity and ease of operations in the decentralized exchange (DEX). By applying the TVP-VAR model, our findings show higher level of connectivity during periods of turmoil (such as Delta, Omicron variants of SARS-Covid, and the Russia Ukraine conflict). Furthermore, risk transmission/reception is found to be independent of the platform on which they typically run (Ethereum based AMMs were both emitters as well as receivers). Pancake (a Binance based AMM) and Perpetual Protocol (Ethereum based AMM) emerged as moderate to high receivers of risk transmission, whereas all of the other AMMs, including Ethereum, were found to be risk emitters at varying degrees. We argue that AMMs typically depend on the underlying smart contracts. If the contract is flexible, AMMs can vary (either receiver or emitter), otherwise AMMs behave in tandem.
Purpose This study aims to identify the ability of gold and cryptocurrency (Cryptocurrency Uncertainty Index (UCRY) Price) as safe haven assets (SHA) for stocks and bonds in both conventional (i.e. stock indices and government bonds) and Islamic markets (i.e. Islamic stock indices and Islamic bonds (IB)). Design/methodology/approach The authors employed the nonadditive panel quantile regression model by Powell (2016). It measured the safe haven characteristics of gold and UCRY Price for stock indices, government bonds, Islamic stocks, and IB under gold circumstances and level of cryptocurrency uncertainty, respectively. The period spanned from 11 March 2020 to 31 December 2021. Findings This study discovered three findings, including: (1) gold is a strong safe haven for stocks and bonds in conventional and Islamic markets under bearish conditions; (2) UCRY Price is a strong safe haven for conventional stocks and bonds but only a weak safe haven for Islamic stocks under high crypto uncertainty; and (3) gold offers a safe haven in both emerging and developed countries, while UCRY Price provides a better safe haven in developed than in emerging countries. Practical implications Gold always wins big for safe haven properties during unstable economy. It can also win over investors who consider shariah compliant products. Therefore, it should be included in an investor's portfolio. Meanwhile, cryptocurrencies are more common for developed countries. Thus, the governments and regulators of emerging countries need to provide more guidance around cryptocurrency so that the societies have better literacy. On top of that, the investors can consider crypto to mitigate risks but with limited safe haven functions. Originality/value The originality aspects of this study include: (1) four chosen assets from conventional and Islamic markets altogether (i.e. stock indices, government bonds, Islamic stock indices and IB); (2) indicator countries selected based on the most used and owned cryptocurrencies for the SHA study; and (3) the utilization of UCRY Price as a crypto indicator and a further examination of the SHA study toward four financial assets.
The aim of this paper is to investigate the effect of a novel method called linear law-based feature space transformation (LLT) on the accuracy of intraday price movement prediction of cryptocurrencies. To do this, the 1-minute interval price data of Bitcoin, Ethereum, Binance Coin, and Ripple between 1 January 2019 and 22 October 2022 were collected from the Binance cryptocurrency exchange. Then, 14-hour nonoverlapping time windows were applied to sample the price data. The classification was based on the first 12 hours, and the two classes were determined based on whether the closing price rose or fell after the next 2 hours. These price data were first transformed with the LLT, then they were classified by traditional machine learning algorithms with 10-fold cross-validation. Based on the results, LLT greatly increased the accuracy for all cryptocurrencies, which emphasizes the potential of the LLT algorithm in predicting price movements.
The growing potential and high volatility of the cryptocurrency market attract a lot of interest from both businesses and investors. Even though the prices fluctuate, predicting with time serious models such as ARMA and ARIMA would still provide a useful reference for analyzing the market. Recent studies on machine learning methods including RNNs have made new progress in forecasting digital currencies. This study focuses on one of the traditional models ARMA to predict the time serious dataset from 2021-2022 of cryptocurrencies including Bitcoin, Ethereum and Ripple. To be specific, AIC and ADF tests are used to choose the optimal model and suitable dataset. According to the analysis, the ARMA model would be affected by the volatility of Bitcoin. However, the predictions are not precise enough but still a valuable reference for certain businesses and individual investors. More state-of-art machine learning models can be utilized in future study to enhance the performance. Overall, these results shed light on guiding further exploration of crypto currency price prediction.
Harendra Kumar Narang, Vishal K. Shrirame, Bhupesh Kurrey
Predicting or forecasting crypto currency prices is now one of the most difficult tasks in crypto market trading due to its qualities and dynamic nature. The purpose of the present work is to analyse the exchange and blockchain data and develop a prediction model using machine learning. Ethereum (ETH) is one of the crypto currencies, and data has been taken from the price time series from January 1, 2017 to December 31, 2021, on a daily basis. The algorithm for gathering data has been trained and tested using a machine-learning algorithm. The adequacy of the developed machine learning models was validated using MAPE, RSME, MAE, and R2 scores. The developed model can predict future results with an accuracy of up to 85% for 7 days. Based on the findings, it is suggested that blockchain historical data and exchange data can be utilised as input characteristics in the development of a machine learning model to forecast Ethereum's future price.
This study examines the tendency of short-term return spillover across Bahrain stocks, bitcoin, and other commodity assets factoring in the dynamic effect of the COVID-19 pandemic. The study employed vector autoregression (VAR) model using the daily returns of Bahrain All Shares Index, bitcoin, crude oil, and gold futures from January 2018 to March 2022. The results showed a persistent unidirectional short-term spillover of return from the Bahrain stock market to the futures gold market for both the period before and during the pandemic. Moreover, the results also showed that the significant positive shock in the bitcoin returns as granger-caused by the returns of the Bahrain stock market is only during the period before the pandemic. Finally, a significant negative contemporaneous short-term effect on the crude oil market returns can be statistically explained by the shocks in the Bahrain stock market only during the COVID-19 period.
Daniel Pereira Alves de Abreu, Robert Aldo Iquiapaza
Purpose The aim of the study was to analyze the performance of Black-Litterman (BL) portfolios using a views estimation procedure that simulates investor forecasts based on technical analysis. Design/methodology/approach Ibovespa, S&P500, Bitcoin and interbank deposit rate (IDR) indexes were respectively considered proxies for the national, international, cryptocurrency and fixed income stock markets. Forecasts were made out of the sample aiming at incorporating them in the BL model, using several portfolio weighting methods from June 13, 2013 to August 30, 2022. Findings The Sharpe, Treynor and Omega ratios point out that the proposed model, considering only variable return assets, generates portfolios with performances superior to their traditionally calculated counterparts, with emphasis on the risk parity portfolio. Nonetheless, the inclusion of the IDR leads to performance losses, especially in scenarios with lower risk tolerance. And finally, given the impact of turnover, the naive portfolio was also detected as a viable alternative. Practical implications The results obtained can contribute to improve investors practices, specifically by validating both the performance improvement – when including foreign assets and cryptocurrencies –, and the application of the BL model for asset pricing. Originality/value The main contributions of the study are: performance analysis incorporating cryptocurrencies and international assets in an uncertain recent period; the use of a methodology to compute the views simulating the behavior of managers using technical analysis; and comparing the performance of portfolio management strategies based on the BL model, taking into account different levels of risk and uncertainty.
Yapılan bu çalışmanın amacı, 1.1.2016-4.12.2022 dönemini kapsayan günlük veriler yardımıyla Bitcoin ile alakalı çıkan haberler ile hem Bitcoin fiyatı hem de getirisi arasındaki ilişkiyi zamanla değişen nedensellik analizi kapsamında incelemektir. Söz konusu ilişkinin varlığı, Hacker ve Hatemi-J (2006)’nin Boostrapt Temelli Toda-Yamamoto Nedensellik Testi ve zamanla değişen nedensellik analizi kullanılarak araştırılmıştır. Elde edilen nedensellik testi bulguları, Bitcoin ile ilgili çıkan haberler ile Bitcoin fiyatı arasında karşılıklı bir nedensellik ilişkisi olduğu yönündedir. Diğer taraftan, Bitcoin getirisi ile Bitcoin ile ilgili çıkan haberler arasındaki nedensellik bulguları incelendiğinde, Bitcoin ile alakalı çıkan haberlerden Bitcoin getirisine doğru nedensellik ilişkisinin söz konusu olmadığı, buna karşın Bitcoin getirilerinden Bitcoin ile alakalı çıkan haberlere doğru bir nedensellik olduğu söylenebilir. Ayrıca, söz konusu nedensellik ilişkilerinin zamanla nasıl bir seyir izlediğine bakıldığında özellikle Bitcoin fiyatlarının arttığı dönemlerde Bitcoin ile ilgili haber sayılarının arttığı görülmüştür. Bu çerçevede hem Bitcoin hem de altcoin piyasasına yatırım yapacak bireylerin, Bitcoin ve altcoin ile alakalı çıkmış olan haberleri dikkate alarak işlem yapmaları yatırımın sağlıklı olması adına önem teşkil etmektedir.
In this study, the authors investigate the volume as the pricing driver of the top three cryptocurrencies (Bitcoin, Ethereum, and Binance) based on a wavelet analysis from January 1, 2019 to December 31, 2021. The dynamics of the relationship between price and volume in the cryptocurrency market could have valuable market implications for stakeholders and investors and contribute to making optimal investment decisions via portfolio diversification strategies. The results reveal that the relationship between price and volume is positive in the medium and long term and that price is the leading volume for both Bitcoin and Binance markets. The findings suggest that the COVID-19 pandemic significantly affected the cryptocurrency price and volume series links. Indeed, these results contribute to the emerging and growing literature on cryptocurrencies in the time of COVID-19, which has received limited attention during the pandemic compared to the classical asset financial classes.
In this paper, the generalised extreme value distribution (GEVD) model is employed to estimate financial risk in the form of return levels and the value at risk (VaR) for the two exchange rates, BitCoin/US dollar (BTC/USD) and the South African rand/US dollar (ZAR/USD). The Basel Committee on Banking Supervision (BCBS) responsible for developing supervisory guidelines for banks and financial trading desks recommended that VaR be computed and reported. The maximum likelihood estimation (MLE) method is used to estimate the parameters of the GEVD. The estimated risk values are used to compare the riskiness of the two exchange rates and help both traders and investors to define their position in forex trading. This is to helping understanding the risk they are taking when they convert their savings/investments to BitCoin instead of the South African currency, the rand. The high extreme value index associated with the BTC/USD compared to the ZAR/USD implies that BitCoin is riskier than the rand. The BTC/USD has higher values of expected extreme/tail losses of 13.44%, 18.02%, and 23.41% at short (6 months), medium (12 months), and long (24 months) terms, compared to the ZAR/USD expected extreme/tail losses of 2.40%, 2.84%, and 3.28%, respectively. The computed VaR estimates for losses of USD 0.17, USD 0.22, and USD 0.38 per dollar invested in BTC/USD at 90%, 95%, and 99%, compared to ZAR/USD’s USD 0.03, USD 0.03, and USD 0.04 at the respective confidence levels, confirm the high risk associated with BitCoin. The conclusion drawn from this study is that BTC/USD is riskier than ZAR/USD, despite the rand being a developing country’s currency, hence perceived as being risky. The perception is that the rand is riskier than BitCoin and perceptions do influence exchange rates. Kupiec’s backtest results confirmed the model’s adequacy. These findings are helpful to investors, traders, and risk managers when deciding on trading positions for the two currencies.
This paper assesses the effectiveness of a broad set of 1066 active and continuously traded cryptocurrencies as a safe haven instrument against extreme oil price movements, in comparison to the corresponding roles of gold. The uncertainty for the oil market during the COVID-19 pandemic and the subsequent Russia–Ukraine conflict set the tone for natural experiments for our study. We use a trail-blazing dynamic generalized autoregressive score model to estimate the tail riskiness of the potential safe haven assets from January 1, 2020, to September 30, 2022. By estimating the risk exposure of all cryptocurrency assets, we determine top ten safest assets for investment. Our results show the emergence of new safe haven cryptocurrencies, which have previously been ignored by the academic literature and policy makers alike. Intriguingly, our findings reveal that gold has been replaced by altcoins as the safest assets during both the COVID-19 pandemic and the Russia–Ukraine conflict. At this instance, our findings suggest that Bitcoin provides lengthier safe haven properties than gold for oil returns in both periods. However, the safe haven properties of gold and cryptocurrencies are time varying. Last but not least, we introduce a new Cryptocurrency Tail Risk Index (CTRI) that captures the risk exposure of cryptocurrency market, as a whole. Our results suggest that investment in numerous cryptocurrencies provides lengthier safe haven properties than investing in gold alone.
Apr 19, 2023·2023 International Conference on Recent Advances in Electrical, Electronics, Ubiquitous Communication, and Computational Intelligence (RAEEUCCI)
Bitcoin (BTC) is a cryptocurrency meaning that it is a virtual asset that works on encryption systems to control production of units and verify the funds transfer. Bitcoin's operation is independent of central bank and can be used as a decentralized medium of exchange. Being an emerging domain, the blockchain systems on which these cryptocurrencies are based, are a great source of investment among enthusiasts. However, there is high volatility in the price of Bitcoin and thus it paves the way for its prediction so as to get a clear insight on its trend. Forecasting Bitcoin prices can provide valuable insights for investment portfolio management, risk assessment, and the identification of profitable trading or arbitrage opportunities. Furthermore, Bitcoin price predictions may assist firms that accept Bitcoin payments in enhancing their financial management practices by improving their revenue and cash flow forecasting. Additionally, governmental entities and regulatory bodies could leverage Bitcoin price forecasting as a tool for monitoring and regulating the cryptocurrency industry. This paper explores the effectiveness of ML models in predicting Bitcoin price by analyzing a diverse set of historical data. We evaluate several ML algorithms, and compare their performance in terms of accuracy to find out best algorithms for short term and long-term Bitcoin price prediction.