This study examines the relations of Bitcoin (BTC) prices and fluctuations with gold, USD, oil, VIX index, hedging, and diversification features in Turkiye. For this purpose, wavelet coherence and dynamic conditional correlations (DCCs) were used in the study. Our research explores whether the bubble behavior patterns in BTC prices during the COVID-19 pandemic can be used in the short term to protect against the bubble behavior in the markets that are the subject of this research and vice versa. However, whether other assets can be used to manage and hedge BTC's downside risk is also being explored. The aim is to understand how and at what level critical financial instruments and indicators are affected by each other in times of crisis and economic recession, such as pandemics, and to present valuable results to decision-makers. The sample for this study includes Türkiye for the period between 12/31/2019 and 13/07/2022. Wavelet Coherence and DCC-GARCH results indicate significant positive and negative movements of BTC prices with gold, oil, USD prices, and the VIX fear index during the pandemic. We find evidence of volatility persistence, causality, and phase differences between BTC and other financial instruments and indicators.
This paper provides an analysis of the negative investor attention impact on bitcoin’s performance. By negative investor attention, we mean investor attention preceding a negative event, such as for example, a cyber-attack. Since their creation, the crypto-market has been numerous times the target of various attacks, which lead to important financial losses. Thus, we propose this study, in which we aim to capture the investor’s reaction and impact on the bitcoin’s performance as a consequence of these negative events happening. We are proxying the negative investor attention by using Google volume searches and splitting the search terms into ’specialist’ and ’non-specialist’ investors. The results obtained show that our Google searches and implicitly the negative investor attention impact bitcoin’s performance. Moreover, the non-specialist-considered keywords seem to drive returns more than the ones of a specialist. This result suggests that the majority of crypto-investors are, in fact, amateur or non-specialists.
David Alaminos, M. Belén Salas, Ángela Callejón Gil
<abstract> <p>The blockchain ecosystem has seen a huge growth since 2009, with the introduction of Bitcoin, driven by conceptual and algorithmic innovations, along with the emergence of numerous new cryptocurrencies. While significant attention has been devoted to established cryptocurrencies like Bitcoin and Ethereum, the continuous introduction of new tokens requires a nuanced examination. In this article, we contribute a comparative analysis encompassing deep learning and quantum methods within neural networks and genetic algorithms, incorporating the innovative integration of EGARCH (Exponential Generalized Autoregressive Conditional Heteroscedasticity) into these methodologies. In this study, we evaluated how well Neural Networks and Genetic Algorithms predict "buy" or "sell" decisions for different cryptocurrencies, using F1 score, Precision, and Recall as key metrics. Our findings underscored the Adaptive Genetic Algorithm with Fuzzy Logic as the most accurate and precise within genetic algorithms. Furthermore, neural network methods, particularly the Quantum Neural Network, demonstrated noteworthy accuracy. Importantly, the X2Y2 cryptocurrency consistently attained the highest accuracy levels in both methodologies, emphasizing its predictive strength. Beyond aiding in the selection of optimal trading methodologies, we introduced the potential of EGARCH integration to enhance predictive capabilities, offering valuable insights for reducing risks associated with investing in nascent cryptocurrencies amidst limited historical market data. This research provides insights for investors, regulators, and developers in the cryptocurrency market. Investors can utilize accurate predictions to optimize investment decisions, regulators may consider implementing guidelines to ensure fairness, and developers play a pivotal role in refining neural network models for enhanced analysis.</p> </abstract>
Krzysztof Koszewski, Somnath Mazumdar, Anoop Kumar
Abstract In recent years, cryptocurrencies have been considered as an asset by public investors and received much research attention. It is a volatile asset, thus predicting its prices is not easy due to the dependence on multiple external factors. Machine learning models are becoming popular for cryptocurrency price predictions, while also considering social media data. In this article, we analyze the rate of return of three cryptocurrencies (Bitcoin, Ether, Binance) from an investor point of view. We also consider three traditional external variables: S&P 500 stock market index, gold price, and volatility index. The rate of return prediction is based on three stages. First, we analyze the correlation between the cryptocurrency returns and the traditional external variables. Next, we focus on the influential social media variables (from Twitter, Reddit, and Wikipedia). Later, we use these variables to improve prediction accuracy. Third, we test how the standard time series models (such as ARIMA and SARIMA) and four machine learning models (such as RNN, LSTM, GRU and Bi-LSTM) predict one-day rate of return. Finally, we also analyze the risk of investing in each cryptocurrencies using value risk statistics. Overall, our result shows no correlation between cryptocurrency returns and three traditional external variables. Second, we found that overall LSTM model is the best, GRU is the second-best prediction model, while the impact of the social media variables varies depending on the cryptocurrencies. Finally, we also found that investment in gold offers better returns than cryptocurrency during Covid-19-like situations.
This study investigates the Fear & Greed Index, an indicator designed to reflect market sentiment regarding Bitcoin price, intending to utilize it as a predictive parameter for future price fluctuations. Due to the substantial volatility in Bitcoin prices and its significant influence on prediction outcomes, the dataset was preprocessed through monthly filtering and normalization. To forecast Bitcoin prices, an array of machine learning algorithms, including linear regression, random forest, and XGBoost, as well as their enhanced counterparts, were employed. The optimal model was identified by comparing the Grid Search XGBoost analysis results. This research holds implications for accurately predicting Bitcoin prices and underscores the impact of market sentiment on its valuation.
Cryptocurrency, a novel digital asset within the blockchain technology ecosystem, has recently garnered significant attention in the investment world. Despite its growing popularity, the inherent volatility and instability of cryptocurrency investments necessitate a thorough risk evaluation. This study utilizes the Autoregressive Moving Average (ARMA) model combined with the Generalized Autoregressive Conditionally Heteroscedastic (GARCH) model to analyze the volatility of three major cryptocurrencies-Bitcoin (BTC), Ethereum (ETH), and Binance Coin (BNB)-over a period from January 1, 2017, to October 29, 2022. The dataset comprises daily closing prices, offering a comprehensive view of the market's fluctuations. Our analysis revealed that the value-at-risk (VaR) curves for these cryptocurrencies demonstrate significant volatility, encompassing a broad spectrum of returns. The overall risk profile is relatively high, with ETH exhibiting the highest risk, followed by BTC and BNB. The ARMA-GARCH-VaR model has proven effective in quantifying and assessing the market risks associated with cryptocurrencies, providing valuable insights for investors and policymakers in navigating the complex landscape of digital assets.
The rapid growth of crypto assets raises important questions about their cross-border usage. To gain a better understanding of cross-border Bitcoin flows, we use raw data covering both on-chain (on the Bitcoin blockchain) and off-chain (outside the Bitcoin blockchain) transactions globally. We provide a detailed description of available methodologies and datasets, and discuss the crucial assumptions behind the quantification of cross-border flows. We then present novel stylized facts about Bitcoin cross-border flows and study their global and domestic drivers. Bitcoin cross-border flows respond differently than capital flows to traditional drivers of capital flows, and differences appear between on-chain and off-chain Bitcoin cross-border flows. Off-chain cross-border flows seem correlated with incentives to avoid capital flow restrictions.
We investigate the long-term impact of macroeconomic and financial factors on cryptocurrency metrics using both parametric and non-parametric methods. Our analysis examines how these factors influence cryptocurrency prices, market capitalizations, and Bitcoin’s hash rate. The results establish that two key factors, the US dollar and the price of gold, adversely affect Bitcoin and other cryptocurrency metrics, including the prices and market capitalizations of decentralized finance and layer-one protocols. Bitcoin’s hash rate demonstrates greater market sensitivity than its price, with the dollar having a stronger impact on Bitcoin than gold. The dollar primarily affects Bitcoin’s price, whereas gold mainly influences its hash rate. These findings, along with Bitcoin’s properties, support the view of Bitcoin as a digital asset analogous to physical gold, playing a role similar to a substitute for the latter.
This study aims to examine the intricate dynamics between BRICS traditional stock assets and the evolving landscape of cryptocurrencies. Using a time-varying parameter vector autoregression model (TVP-VAR), we have analyzed data from the BRICS stock market index, cryptocurrencies, and indicators from January 6, 2015, to June 29, 2023. The results show that three out of the five BRICS stock markets serve as primary sources of shocks that subsequently affect the financial network. The transcontinental (TCI) value derived from the dynamic conditional connectedness using the TVP-VAR model demonstrates a higher explanatory power than the static connectedness observed using the standard VAR model. The discoveries from this study offer valuable insights for corporations, investors, and regulators concerning systematic risk and investment strategies.
Abstract On average, stocks have a much higher rate of return than bonds; this has led to research on the equity premium puzzle . Similarly, Bitcoin outperforms stocks; I call this the Bitcoin premium puzzle . I show that standard macroeconomic models predict a low or negative Bitcoin premium. Though Bitcoin is extremely volatile, the model is rejected even when the coefficient of relative risk aversion is above 10. The Bitcoin premium declined after a structural break in late 2013. However, the puzzle is persistent; there has been no downward trend in the premium since.
This paper examines the asymmetric spillovers between Bitcoin, oil and four precious metals (silver, gold, platinum and palladium) on daily returns from 18 August 2011 to 2 October 2019.Using a modified version of the Dieblod and Yilmaz (2012, 2014) index and a similar approach to Barunk (2017), our results indicate slight volatility spillovers between the whole systems.Moreover, the results show that gold is the most influential market since it shifts the highest proportion of volatility.Furthermore, we find that oil, Bitcoin and platinum can serve as a hedge and a diversifier as they are neutral in terms of spillovers.Moreover, we find evidence of asymmetric volatility spillovers since good spillovers dominate bad one, which proves the optimistic mood of the whole system.More interestingly, our results shed light on the ability of Bitcoin, the digital gold, to serve as a hedge and diversifier in both good and bad innovations.
José Daniel Cardoso Rodrigues, Petros Golitsis, Pavlos Gkasis
With the rise of cryptocurrencies and their appeal as alternative investment assets, this study, using daily and weekly data from early 2015 to late 2023, aims to analyze the influence of economic and geopolitical uncertainty factors on cryptocurrencies, particularly Bitcoin, and forecast their volatility using GARCH, EGARCH, and GJR-GARCH models. Our findings reveal that the Geopolitical Acts Index (GPAs), the U.S. Economic Policy Uncertainty Index (EPU), and the Volume of Bitcoin transactions exhibit a positive significant impact on its returns, whereas the Cryptocurrency Uncertainty Index (UCRY), S&P 500, and Volatility Index (VIX) demonstrate a negative one. Furthermore, by decomposing geopolitical turbulence into Geopolitical Risks (GPRs) and Threats (GPTs), these variables were found to be less significant compared to Geopolitical Acts. Finally, the asymmetry analysis (leverage effects) reflects on how negative shocks exhibit a greater influence than positive ones on Bitcoin returns, indicating that adverse news in the media tends to impact the cryptocurrency returns more profoundly. Our conclusions contribute to the existing literature by exploring the role that Bitcoin, and cryptocurrencies in general, play as investment assets, when taking into consideration the volatility they entail, especially following negative shocks in an economy.
Min-Bin Lin, Cathy Yi‐Hsuan Chen, Wolfgang Karl Härdle
This study investigates cryptocurrency volatility dynamics, particularly focusing on Ethereum (ETH). We dissect long- and short-term volatility components to gain deeper insights into its evolution. This approach allows studying the impact of ETH’s Merge upgrade, replacing Proof-of-Work with Proof-of-Stake on September 15, 2022. Employing 29 empirical factors related to blockchain functionality and crypto market characteristics, we explore their long-term equilibrium connection with price volatility. Our findings reveal that scalability factors and wealth dis- tribution significantly influence volatility persistence, ultimately highlighting the stability-enhancing impact of Ethereum’s Merge upgrade.