Has there been a linkage mechanism between the prices of Bitcoin and traditional wealth preservation investment tools, that is, Bitcoin may serve as an investment substitute when other investment tool markets are sluggish, or can also benefit from it when the overall investment market is hot. The price of Bitcoin exhibits extremely unstable characteristics, as it can double its value dozens of times in a very short period of time or return to its starting point in a single day. The rapid rise and short duration of Bitcoin's price show us its infinite potential. Through research, this paper finds that the price of Bitcoin fluctuates greatly, while the U.S. Dollar Index and the S&P 500 index are basically horizontal, and their volatility is relatively small. Therefore, Bitcoin may be used as a speculative product, and there is a lot of speculative behavior in the market. When the investment attributes of Bitcoin dominate, an increase in economic policy uncertainty will significantly suppress investor sentiment and cause Bitcoin prices to decline. In addition, its impact on the world financial system is also increasing.
We study the real economic activity in the Bitcoin blockchain that involves transactions from/to retail users rather than between organizations such as marketplaces, exchanges, or other services. We first introduce a heuristic method to classify Bitcoin players into three main categories: Frequent Receivers (FR), Neighbors of FR, and Others. We show that most real transactions involve Frequent Receivers, representing a small fraction of the total value exchanged according to the blockchain, but a significant fraction of all payments, raising concerns about the centralization of the Bitcoin ecosystem. We also conduct a weekly pattern analysis of activity, providing insights into the geographical location of Bitcoin users and allowing us to quantify the bias of a well-known dataset for actor identification.
NicolĂČ Vallarano, Tiziano Squartini, Claudio J. Tessone
The open availability of the entire history of the Bitcoin transactions opens up the possibility to study this system at an unprecedented level of detail. This contribution is devoted to the analysis of the mesoscale structural properties of the Bitcoin User Network (BUN), across its entire history (i.e. from 2009 to 2017). What emerges from our analysis is that the BUN is characterized by a core-periphery structure a deeper analysis of which reveals a certain degree of bow-tieness (i.e. the presence of a Strongly-Connected Component, an IN- and an OUT-component together with some tendrils attached to the IN-component). Interestingly, the evolution of the BUN structural organization experiences fluctuations that seem to be correlated with the presence of bubbles, i.e. periods of price surge and decline observed throughout the entire Bitcoin history: our results, thus, further confirm the interplay between structural quantities and price movements observed in previous analyses.
Iulia Cioroianu, Shaen Corbet, Charles Larkin, Les Oxley
Using estimated sentiment indices based on CBDC-related social media posts, and testing for the effects of regulatory-related announcements upon blockchain and cryptocurrency-related funds, this research presents two key findings: first, the continued evolution of the pricing structures of digital finance products to respond to such perceived threats constitutes a further evolutionary point in the product's life-cycle. However, secondly, the very fact that returns fall while volatility increases, indicates a largely negative market response to the threat of potential external regulation of cryptocurrencies in the future. The nature of this negative response validates concerns that anonymity continues to be a central attractive feature for cryptocurrency stakeholders, further verifying the necessity for third-party oversight.
The role of cryptocurrencies within the financial systems has been expanding rapidly in recent years among investors and institutions. It is therefore crucial to investigate the phenomena and develop statistical methods able to capture their interrelationships, the links with other global systems, and, at the same time, the serial heterogeneity. For these reasons, this paper introduces hidden Markov regression models for jointly estimating quantiles and expectiles of cryptocurrency returns using regime-switching copulas. The proposed approach allows us to focus on extreme returns and describe their temporal evolution by introducing time-dependent coefficients evolving according to a latent Markov chain. Moreover to model their time-varying dependence structure, we consider elliptical copula functions defined by state-specific parameters. Maximum likelihood estimates are obtained via an Expectation-Maximization algorithm. The empirical analysis investigates the relationship between daily returns of five cryptocurrencies and major world market indices.
This study explores the intricacies of waiting games, a novel dynamic that emerged with Ethereum's transition to a Proof-of-Stake (PoS)-based block proposer selection protocol. Within this PoS framework, validators acquire a distinct monopoly position during their assigned slots, given that block proposal rights are set deterministically, contrasting with Proof-of-Work (PoW) protocols. Consequently, validators have the power to delay block proposals, stepping outside the honest validator specs, optimizing potential returns through MEV payments. Nonetheless, this strategic behaviour introduces the risk of orphaning if attestors fail to observe and vote on the block timely. Our quantitative analysis of this waiting phenomenon and its associated risks reveals an opportunity for enhanced MEV extraction, exceeding standard protocol rewards, and providing sufficient incentives for validators to play the game. Notably, our findings indicate that delayed proposals do not always result in orphaning and orphaned blocks are not consistently proposed later than non-orphaned ones. To further examine consensus stability under varying network conditions, we adopt an agent-based simulation model tailored for PoS-Ethereum, illustrating that consensus disruption will not be observed unless significant delay strategies are adopted. Ultimately, this research offers valuable insights into the advent of waiting games on Ethereum, providing a comprehensive understanding of trade-offs and potential profits for validators within the blockchain ecosystem.
Purpose The current paper proposes a prediction model for a cryptocurrency that encompasses three properties observed in the markets for cryptocurrenciesânamely high volatility, illiquidity, and regime shifts. As far as the authorsâ knowledge extends, this paper is the first attempt to introduce a stochastic differential equation (SDE) for pricing cryptocurrencies while explicitly integrating the mentioned three significant stylized facts. Design/methodology/approach Cryptocurrencies are increasingly utilized by investors and financial institutions worldwide as an alternative means of exchange. To the authorsâ best knowledge, there is no SDE in the literature that can be used for representing and evaluating the data-generating process for the price of a cryptocurrency. Findings By using Ito calculus, the authors provide a solution for the suggested SDE along with mathematical proof. Numerical simulations are performed and compared to the real data, which seems to capture the dynamics of the price path of two main cryptocurrencies in the real markets. Originality/value The stochastic differential model that is introduced and solved in this article is expected to be useful for the pricing of cryptocurrencies in situations of high volatility combined with structural changes and illiquidity. These attributes are apparent in the real markets for cryptocurrencies; therefore, accounting explicitly for these underlying characteristics is a necessary condition for accurate evaluation of cryptocurrencies.
Since the debut of cryptocurrencies, particularly Bitcoin, in 2009, cryptocurrency trading has grown in popularity among investors. Relative to other conventional asset classes, cryptocurrencies exhibit high volatility and, consequently, downside risk. While the prospects of high returns are alluring for investors and speculators, the downside risks are important to consider and model. As a result, the profitability of crypto market operations depends on the predictability of price volatility. Predictive models that can successfully explain volatility help to reduce downside risk. In this paper, we investigate the value-at-risk (VaR) forecasts using a variety of volatility models, including conditional autoregressive VaR (CAViaR) and dynamic quantile range (DQR) models, as well as GARCH-type and generalized autoregressive score (GAS) models. We apply these models to five of some of the largest market capitalization cryptocurrencies (Bitcoin, Ethereum, Ripple, Litecoin, and Steller, respectively). The forecasts are evaluated using various backtesting and model confidence set (MCS) techniques. To create the best VaR forecast model, a weighted aggregative technique is used. The findings demonstrate that the quantile-based models using a weighted average method have the best ability to anticipate the negative risks of cryptocurrencies.
It is important to determine the network effects and store-of-value feature of cryptocurrencies due to the argument that it could be considered as a new âasset classâ. Current studies on cryptocurrencies' network effects mainly focused on using Metcalfe's Law to evaluate the relationship between cryptocurrency prices and the squared number of active wallets addresses. In terms of cryptocurrencies' store-of-value features, previous studies primarily compared daily volatility of limited number of popular cryptocurrencies to Gold. Extant studies are also based on out-of-date data. This research extends the literature by using up-to-date daily data of a sample of the top 100 cryptocurrencies covering 2010â2023 to explore the network effects and the store of value characteristics of a wide range of cryptocurrencies. Firstly, we used nonlinear regression models to examine the relationship between cryptocurrency prices and active wallets addresses, the number of transactions and circulations. Secondly, to deepen our understanding of the store-of-value features of cryptocurrencies, we used a combination of GARCH models and time series analysis to explore the volatility in the daily returns of the sampled cryptocurrencies. Findings indicate that at least one of the network factors (i.e., active wallets addresses, the number of transactions, and number of circulation supply) have a significant effect on cryptocurrency prices. The study also finds that stable coins have comparable daily volatility as Gold, while only mature cryptocurrencies, such as PAXG, Bitcoin, Ethereum, BNB and LINK, demonstrate strong correlation with Gold. Bitcoin also showed a high positive time-series correlation with 24 of the 42 cryptocurrencies. Findings from this study provide important insights to investors, market analysts, regulators and other stakeholders on the marketisation and the store of value potentials of cryptocurrencies.
Cryptocurrencies have obtained a crucial position in the international financial landscape. The cryptocurrency market has been perceived as a highly volatile market since the inception of Bitcoin. This study investigates the relevant performance of extreme value models (EVM) in estimating the Value-at-Risk (VaR) of Bitcoin and Ethereum returns. The extreme value mixture models, GPD-Normal-GPD (GNG) and GPD-KDE-GPD models are fitted to the returns of Bitcoin and Ethereum and the Kupiec likelihood backtesting procedure is performed on the VaR estimates to assess the fits. Both modelsâ results showed that the fits were a much more decent representation of the observed data when compared to the Normal distribution. The backtesting results showed that the GPD-KDE-GPD modelâs fit was superior to that of the GPD-Normal-GPD for both sets of returns at all VaR risk levels except at the 99% level. The results of this study may assist with understanding the dynamics and risks associated with cryptocurrencies and can serve as a beneficial tool for decision-making and risk management to investors, traders, financial institutions and many other participants in the cryptocurrency ecosystem.
Abstract We examine the fractal volatility and longârange dependence of Bitcoin, Ethereum, Tether and USD Coin by employing the continuous wavelet transform, maximal overlap discrete wavelet transform and rescaled range. Our dataset consists of daily prices spanning from January 2017 through to October 2022, encapsulating preâ and postâepidemic eras. Generally, our findings suggest that Tether presents the least overall volatility throughout the timeâfrequency spectrum. USD Coin demonstrates ephemeral turbulence, contrary to Tether's maturity in influencing market equilibrium through token issuance and trade responses. In the postâepidemic sample, both stablecoins indicate mean reversion, with USD Coin showing marginally better efficiency. Conversely, investment tokens display persistent clusters due to retail traders and longâterm fundamental institutions. Although both tokens illustrate multifractal volatility, Ethereum unveils more essence of selfâsimilarity than Bitcoin. Hence, there is no evidence that Ethereum truly duplicates Bitcoin since policyârelated events differ between them, as both return series move incongruously. Conditional dynamics signify that all cryptocurrencies, except Tether, were affected by the pandemic transition of COVIDâ19 and subsequent macroeconomic news. The unconditional volatility of stablecoins evinces zeroâmean errors, antithetical to investment tokens exhibiting annual cycles. The fractal geometry suggests that investment tokens simulate oneâdimensional lines, whereas stablecoins mimic twoâdimensional planes.
Cryptocurrency markets are characterised by high volatility, high returns and comparative immaturity relative to equity and commodity markets. Topological Data Analysis (TDA) persistence norms are effective tools for the analysis of noisy dynamical systems like the cryptocurrency markets. We show how information from the shape of daily return data adds additional inference on activity within the cryptocurrency markets. TDA persistence norms embed volatility and connectedness between coins as well as incorporating information from uncertainty indexes, financial market performance and commodity returns. Our TDA measures are robust to noise and are consistent across a raft of alternative coin selections. Further, we exposit how persistence norms peak to forewarn of crashes and stay low as markets face exogenous shocks. We demonstrate the clear advantages of TDA for the study of cryptocurrency markets and develop the next steps for exploiting the potential of TDA for application to cryptocurrency markets.
This paper aims to compare the empirical performance of two approaches in detecting structural breaks and outliers due to the significant frequent price changes seen in cryptocurrencies.The two approaches are indicator saturation (IS) and Bai and Perron (BP).The cryptocurrency data employed in this study are Bitcoin and Ethereum.In comparing the performance of the two approaches, this study performed multiple empirical comparisons using various significant levels, different data frequencies, as well as the original and log series (price).The findings showed that the prices contained structural breaks and outliers and that the IS approach performed significantly better than the BP test in terms of the identified structural breaks as well as outliers across different settings.The contribution of this study is providing empirical comparisons between IS and BP approaches using cryptocurrency data.These findings are important to the potential stakeholders, in particular, for quality control in industries, for setting price targets, and for confirming trading signals to reduce potential losses.
Disruption and shutdown of exchanges frequently happen in the cryptocurrency market, though its potential impacts are relatively under-investigated due to several empirical challenges. This study employs 20-h of service interruption on October 15th at Upbit , the dominant cryptocurrency exchange in Korea, as an exogenous shock to examine the effect of unexpected service interruption at the exchange on cryptocurrency market. Event study estimation using price data from Binance, the largest cryptocurrency exchange globally, shows the sharp and negative reactions to cryptocurrencies mostly traded at Upbit . Major currencies such as Bitcoin and Ethereum also presented limited reactions, implying that service interruption could be interpreted as vulnerability of overall cryptocurrencies.
Sustainable cryptocurrency systems need to address the challenge of continuous inflation. Systematically maintaining stability during market turbulence can be challenging when token minting rates are predetermined and do not account for actual liquidity demand. Mindful of the critical role played by monetary policy, we propose a decentralized, truthful auction approach for tuning the currency system toward self-stabilization in volatile markets. Concretely, when severe inflation or market overheating occurs, the system enforces a contractionary monetary policy to restrain the market liquidity and avoid potential economic disasters. Should severe deflation and market stagnancy occur, more tokens will be minted than burnt to stimulate the market. Through this approach, our studies aim to provide a tool to help sustain the long-term existence of decentralized commodities.
Abstract The aim of this study is to compare and contrast the volatility of different asset classes namely Bitcoin, gold and crude oil against the US dollar, using the symmetric GARCH (1,1) model. Furthermore, this study examines which of the three assets provide the lowest volatility and identifies if the univariate GARCH (1,1) model can suitably forecast the volatility of the foreign exchange market, commodity market and cryptocurrency market. More specifically, this study uses only estimates from a symmetric GARCH model for the XAU/USD, WTI/USD and BTC/USD financial assets. Although the literature on the volatility of different assets is extensive, it neglects to detect the severe economic recession that is approaching. Considering that powerful nations purchased substantial quantities of gold in order to back their national currency, supremacy of the US dollar is under significant attack. Experts in international relations have claimed that it is essential to have a single, extremely influential national economy to exhibit stability and operate smoothly. Specifically, the international monetary system functioned under the theory of hegemonic stability. Given the fact that gold still remains the safe haven asset during periods of financial and economic distress, central banks are purchasing gold at a rapid pace with Russia and China leading the way. Furthermore, the demand for precious metals has also increased while the US dollar is struggling to keep its supremacy as BRICS reserve currency is seeking to replace it in the future. The daily data encompassing the necessary information for February 2012 - February 2020 is acquired from âInvesting.comâ, reaching 7193 observations. This study will enrich the literature associated with volatility forecasting of different asset classes during financial and economic turmoil while also raising awareness of the economic threats that could follow.
Abstract The fast development of the cryptocurrencies has brought to the attention of the authorities and researchers the importance of studying the risks associated with this category of assets. One of the main directions of analysis was the study of the correlations between the crypto-market and other traditional markets in order to assess the impact on financial stability. In the last 2-3 years, more and more studies have showed increasing correlations between the traditional markets and the crypto-market, which could generate some risks to the financial stability. We applied a novel methodology, based on TVP-VAR model, to study the correlations between Bitcoin and three other traditional assets, respectively gold, S&P 500 and EUR/USD from 01/01/2015 to 01/01/2023. We proved that the correlations between traditional assets, such as equity (S&P 500), respectively commodity (gold) and Bitcoin have increased significantly. However, other assets, such as the exchange rates are not correlated with cryptocurrencies and the correlations in the other way, from Bitcoin to gold, respectively S&P 500 are still very low. Thus, our findings indicate that, at this moment, the crypto-market poses risks to the financial stability, but because of the fact that the correlations are still only unidirectional (from traditional assets to cryptocurrency), the crypto-market could now just amplify the risks to the financial stability originating from the traditional markets.