This study investigates the volatility of daily Bitcoin returns and multifractal properties of the Bitcoin market by employing the rolling window method and examines relationships between the volatility asymmetry and market efficiency. Whilst we find an inverted asymmetry in the volatility of Bitcoin, its magnitude changes over time, and recently, it has become small. This asymmetric pattern of volatility also exists in higher frequency returns. Other measurements, such as kurtosis, skewness, average, serial correlation, and multifractal degree, also change over time. Thus, we argue that properties of the Bitcoin market are mostly time dependent. We examine efficiency-related measures: the Hurst exponent, multifractal degree, and kurtosis. We find that when these measures represent that the market is more efficient, the volatility asymmetry weakens. For the recent Bitcoin market, both efficiency-related measures and the volatility asymmetry prove that the market becomes more efficient.
Flash Loan attack can grab millions of dollars from decentralized vaults in one single transaction, drawing increasing attention from the Decentralized Finance (DeFi) players. It has also demonstrated an exciting opportunity that a huge wealth could be created by composing DeFi's building blocks and exploring the arbitrage change. However, a fundamental framework to study the field of DeFi has not yet reached a consensus and there's a lack of standard tools or languages to help better describe, design and improve the running processes of the infant DeFi systems, which naturally makes it harder to understand the basic principles behind the complexity of Flash Loan attacks. In this paper, we are the first to propose Flashot, a prototype that is able to transparently illustrate the precise asset flows intertwined with smart contracts in a standardized diagram for each Flash Loan event. Some use cases are shown and specifically, based on Flashot, we study a typical Pump and Arbitrage case and present in-depth economic explanations to the attacker's behaviors. Finally, we conclude the development trends of Flash Loan attacks and discuss the great impact on DeFi ecosystem brought by Flash Loan. We envision a brand new quantitative financial industry powered by highly efficient automatic risk and profit detection systems based on the blockchain.
This paper adds to the growing literature of cryptocurrency and behavioral finance. Specifically, we investigate the relationships between the novel investor attention and financial characteristics of Bitcoin, i.e., return and realized volatility, which are the two most important characteristics of one certain asset. Our empirical results show supports in the behavior finance area and argue that investor attention is the granger cause to changes in Bitcoin market both in return and realized volatility. Moreover, we make in-depth investigations by exploring the linear and non-linear connections of investor attention on Bitcoin. The results indeed demonstrate that investor attention shows sophisticated impacts on return and realized volatility of Bitcoin. Furthermore, we conduct one basic and several long horizons out-of-sample forecasts to explore the predictive ability of investor attention. The results show that compared with the traditional historical average benchmark model in forecasting technologies, investor attention improves prediction accuracy in Bitcoin return. Finally, we build economic portfolios based on investor attention and argue that investor attention can further generate significant economic values. To sum up, investor attention is a non-negligible pricing factor for Bitcoin asset.
Murat AKBALIK, Nicholas Apergis, Melis Zeren, Ömer Sarıgül
The paper investigates the impact of Bitcoin volatility on international capital inflows through the methodology of an AR(1)-CGARCH model across a global panel of 132 countries, as well as across different regions, i.e. Asia, European Union (EU), America (including the US, Canada and Latin American countries), and Africa. The findings document that there is a strong impact of Bitcoin volatility on global international capital inflows, as well as in the cases of the American and Asian cases. However, the results document a statistically insignificant effect for the cases of the EU and African countries.
Blok zincir sisteminde işlem gören en yeni inovatif finansal ürünlerden biri olan kripto paralar, yatırımcılardan yüksek ilgi görmektedir. Kripto para piyasasının en yüksek işlem hacimli ürünü Bitcoin (BTC), gösterdiği yüksek oynaklıklar ve spekülatif fiyat balonları ile de ön plana çıkmıştır. BTC’nin volatilite yapısında ABD borsa endeks getirilerinin varlığını araştıran bu çalışma, 10.03.2016 – 11.06.2019 dönemindeki günlük verileri kapsar. Genelleştirilmiş Otoregresif Koşullu Değişen Varyans modellerinden GARCH, EGARCH ve TARCH modellerinin kullanıldığı çalışmada, SP500, Nasdaq100 ve Dow Jones Industrial varyans değişkeni olarak kullanılmıştır. Bulgular, (1) her üç endeksin de BTC’in volatilitesini açıklamada anlamlı olduğu, (2) borsa endeksleri ile geliştirilmiş modellerin, GARCH, EGARCH ve TARCH modellerinin tamamında benzer temel modelden daha güçlü olduğu ve (3) endekslerle geliştirilmiş EGARCH modelinin ise en güçlü model olduğunu göstermektedir
This paper studies how sentiment affect Bitcoin pricing by examining, at an hourly frequency, the linkage between sentiment of finance-related Twitter messages and return as well as the volatility of Bitcoin as a financial asset. On the one hand, there was calculated the return from minute-level Bitcoin exchange quotes and use of both rolling variance and high-minus-low price to proxy for Bitcoin volatility per each trading hour. On the other hand, the mood signals from tweets were extracted based on a list of positive, negative, and uncertain words according to the Loughran-McDonald finance-specific dictionary. These signals were translated by categorizing each tweet into one of three sentiments, namely, bullish, bearish, and null. Then the total number of tweets were adopted in each category over one hour and their differences as potential Bitcoin price predictors. The empirical results indicate that after controlling a list of lagged returns and volatilities, stronger bullish sentiment significantly foreshadows higher Bitcoin return and volatility over the time range of 24 hours. While bearish and neutral financial Twitter sentiments have no such consistent performance, the difference between bullish and bearish ratings can improve prediction consistency. Overall, this research results add to the growing Bitcoin literature by demonstrating that the Bitcoin pricing mechanism can be partially revealed by the momentum on sentiment in social media networks, justifying a sentimental appetite for cryptocurrency investment.
Since the launch of Bitcoin, there has been a lot of controversy surrounding what asset class it is. Several authors recognize the potential of cryptocurrencies but also certain deviations with respect to the functions of a conventional currency. Instead, Bitcoin’s diversifying factor and its high return potential have generated the attention of portfolio managers. In this context, understanding how its volatility is explained is a critical element of investor decision-making. By modeling the volatility of classic assets, nonlinear models such as Generalized Autoregressive Conditional Heteroskedasticity (GARCH) offer suitable results. Therefore, taking GARCH(1,1) as a reference point, the main aim of this study is to model and assess the relationship between the Bitcoin volatility and key financial environment variables through a Conditional Correlation (CC) Multivariate GARCH (MGARCH) approach. For this, several commodities, exchange rates, stock market indices, and company stocks linked to cryptocurrencies have been tested. The results obtained show certain heterogeneity in the fit of the different variables, highlighting the uncorrelation with respect to traditional safe haven assets such as gold and oil. Focusing on the CC-MGARCH model, a better behavior of the dynamic conditional correlation is found compared to the constant.
Abstract Using an analogy between finance and astrophysics, this study aims to investigate whether there exists a mechanism that can describe the explosive increase in the number of traded cryptocurrencies and the cryptocurrency market in general. In physics, the Schwarzschild radius indicates that black holes are constantly expanding because of their mass increase. Enriching this analogy, we consider the cryptocurrency market as a self-gravitational body whose mass is denoted by (1) the number of traded cryptocurrencies and (2) in terms of increasing market capitalization for a given number of traded cryptocurrencies. By analyzing weekly snapshot data of all traded cryptocurrencies from January 4, 2009, to June 14, 2020, we find evidence that the above-mentioned mechanism exists. The results clearly indicate the self-gravitational property of the cryptocurrency market, which is direct evidence toward the hypothesis that the changes in the traded cryptocurrencies are a positive function of the previous period’s number of traded cryptocurrencies.
Decentralization has been widely acknowledged as a core virtue of blockchains. However, in the past, there have been few measurement studies on measuring and comparing the actual level of decentralization between existing blockchains using multiple metrics and granularities. This paper presents a new comparison study of the degree of decentralization in Bitcoin and Ethereum, the two most prominent blockchains, with various decentralization metrics and different granularities within the time dimension. Specifically, we measure the degree of decentralization in the two blockchains during 2019 by computing the distribution of mining power with three metrics (Gini coefficient, Shannon entropy, and Nakamoto coefficient) as well as three granularities (days, weeks, and months). Our measurement results with different metrics and granularities reveal the same trend that, compared with each other, the degree of decentralization in Bitcoin is higher, while the degree of decentralization in Ethereum is more stable. To obtain the cross-interval information missed in the fixed window based measurements, we propose the sliding window based measurement approach. The corresponding results demonstrate that the use of sliding windows could reveal additional cross-interval information overlooked by the fixed window based measurements, thus enhancing the effectiveness of measuring decentralization in terms of continuous trends and abnormal situations. We believe that the methodologies and findings in this paper can facilitate future studies of decentralization in blockchains.
The virtual nature of digital money is fueling the conflict between usability, functionality and trust in the digital form. Institutional trust drivers should move forward in understanding the nature of confidence in digital money. Do central banks digital money (CBDC – central bank digital currency) and private cryptocurrencies demonstrate the same or different trust patterns? The paper used the general regression method to discover the relationship between trust in different forms of digital money and selected variables that may generate this trust. Simple empirical tests were sufficient to find the fundamental importance of age as a confidence driver relevant to CBDC and cryptocurrencies. It is found that traditional factors associated with the inflation history and quality of monetary order (central banks independence and rule of law) do not play a role in the case of CBDC, but are important in the case of cryptocurrencies. Structural features (like FinTech development or social trust) that should support trust in digital money are not found to be important. Societies with larger fraction of younger generations demonstrate higher confidence in centralized and decentralized forms of digital money. This challenges the traditional approach to money and calls into question the future role of monetary stability institutions in the digital age. Digitalization is perceived as an improvement in welfare only when fiat money institutions become fragile. The efficiency and credibility of central banks are not a bonus to confidence in CBDC. This is a challenge for the institutional design of the future digital-based monetary order.
Human behavior as they engaged in financial activities is intimately connected to the observed market dynamics. Despite many existing theories and studies on the fundamental motivations of the behavior of humans in financial systems, there is still limited empirical deduction of the behavioral compositions of the financial agents from a detailed market analysis. Blockchain technology has provided an avenue for the latter investigation with its voluminous data and its transparency of financial transactions. It has enabled us to perform empirical inference on the behavioral patterns of users in the market, which we explore in the bitcoin and ethereum cryptocurrency markets. In our study, we first determine various properties of the bitcoin and ethereum users by a temporal complex network analysis. After which, we develop methodology by combining k -means clustering and Support Vector Machines to derive behavioral types of users in the two cryptocurrency markets. Interestingly, we found four distinct strategies that are common in both markets: optimists, pessimists, positive traders and negative traders. The composition of user behavior is remarkably different between the bitcoin and ethereum market during periods of local price fluctuations and large systemic events. We observe that bitcoin (ethereum) users tend to take a short-term (long-term) view of the market during the local events. For the large systemic events, ethereum (bitcoin) users are found to consistently display a greater sense of pessimism (optimism) towards the future of the market.
Sérgio Adriani David, Claudio Marcio Cassela Inacio, Rafael Amorim Belo Nunes, J. A. Tenreiro Machado
Introduction: Cryptocurrencies have been attracting the attention from media, investors, regulators and academia during the last years. In spite of some scepticism in the financial area, cryptocurrencies are a relevant subject of academic research. Objectives: In this paper, several tools are adopted as an instrument that can help market agents and investors to more clearly assess the cryptocurrencies price dynamics and, thus, guide investment decisions more assertively while mitigating risks. Methods: We consider three methods, namely the Auto-Regressive Integrated Moving Average (ARIMA), Auto-Regressive Fractionally Integrated Moving Average (ARFIMA) and Detrended Fluctuation Analysis, and three indices given by the Hurst and Lyapunov exponents or the Fractal Dimension. This information allows assessing the behaviour of the time series, such as their persistence, randomness, predictability and chaoticity. Results: The results suggest that, except for the Bitcoin, the other cryptocurrencies exhibit the characteristic of mean reverting, showing a lower predictability when compared to the Bitcoin. The results for the Bitcoin also indicate a persistent behavior that is related to the long memory effect. Conclusions: The ARFIMA reveals better predictive performance than the ARIMA for all cryptocurrencies. Indeed, the obtained residual values for the ARFIMA are smaller for the auto and partial auto correlations functions, as well as for confidence intervals.
Alexander Brauneis, Roland Mestel, Ryan Riordan, Erik Theissen
This paper investigates the efficacy of low-frequency transactions-based liquidity measures to describe actual (high-frequency) liquidity. We show that the Corwin and Schultz (2012) and Abdi and Ranaldo (2017) estimators outperform other measures in describing time-series variations, irrespective of the observation frequency, trading venue, high-frequency liquidity benchmark, and cryptocurrency. Both measures perform well during high and low return, volatility and volume periods. The Kyle and Obizhaeva (2016) estimator and the Amihud (2002) illiquidity ratio outperform when estimating liquidity levels. These two estimators also reliably identify liquidity differences between trading venues. Overall, the results suggest that there is not yet a universally bestmeasure but there are reasonably good low-frequency measures.
The aim of this study is to examine the daily return spillover among 18 cryptocurrencies under low and high volatility regimes, while considering three pricing factors and the effect of the COVID-19 outbreak. To do so, we apply a Markov regime-switching (MS) vector autoregressive with exogenous variables (VARX) model to a daily dataset from 25-July-2016 to 1-April-2020. The results indicate various patterns of spillover in high and low volatility regimes, especially during the COVID-19 outbreak. The total spillover index varies with time and abruptly intensifies following the outbreak of COVID-19, especially in the high volatility regime. Notably, the network analysis reveals further evidence of much higher spillovers in the high volatility regime during the COVID-19 outbreak, which is consistent with the notion of contagion during stress periods.
This paper uses new and recently introduced methodologies to study the similarity in the dynamics and behaviours of cryptocurrencies and equities surrounding the COVID-19 pandemic. We study two collections; 45 cryptocurrencies and 72 equities, both independently and in conjunction. First, we examine the evolution of cryptocurrency and equity market dynamics, with a particular focus on their change during the COVID-19 pandemic. We demonstrate markedly more similar dynamics during times of crisis. Next, we apply recently introduced methods to contrast trajectories, erratic behaviours, and extreme values among the two multivariate time series. Finally, we introduce a new framework for determining the persistence of market anomalies over time. Surprisingly, we find that although cryptocurrencies exhibit stronger collective dynamics and correlation in all market conditions, equities behave more similarly in their trajectories, extremes, and show greater persistence in anomalies over time.
The blockchain technology and cryptocurrency are now in the centre of the financial market. The raise of the cryptocurrencies represented by Bitcoin have attracted a large group of scholars to analyze the underlying dynamics of their price fluctuations. Intensive debate emerged on the intrinsic features of Bitcoin. In theoretical analysis, we developed the principle of monetary convention to define the concept of monetary consensus, capturing the nature of monetary system, and categorize it into three types: traditional, algorithm and hybrid. Based on the Wavelet Coherence Analysis, we try to analyze Bitcoin price dynamics in both time and frequency domains, comparing Bitcoin with financial assets, economic and financial indexes, and other cryptocurrencies.