Dataset retrieved with an Ethereum client, and used by the code hosted here for this paper published in the Proceedings of the Web Conference 2021 (WWW ’21) Abstract: Cryptoassets such as cryptocurrencies and tokens are increasingly traded on decentralized exchanges. The advantage for users is that the funds are not in custody of a centralized external entity. However, these exchanges are prone to manipulative behavior. In this paper, we illustrate how wash trading activity can be identified on two of the first popular limit order book-based decentralized exchanges on the Ethereum blockchain, IDEX and EtherDelta. We identify a lower bound of accounts and trading structures that meet the legal definitions of wash trading, discovering that they are responsible for a wash trading volume in equivalent of 159 million U.S. Dollars. While self-trades and two-account structures are predominant, complex forms also occur. We quantify these activities, finding that on both exchanges, more than 30% of all traded tokens have been subject to wash trading activity. On EtherDelta, 10% of the tokens have almost exclusively been wash traded. All data is made available for future research. Our findings underpin the need for countermeasures that are applicable in decentralized systems.
Mohamed Arbi Madani, Zied Ftiti, Waël Louhichi, Hachmi Ben Ameur
We investigate intraday hedging and the safe haven role of Bitcoin for stocks, currencies, and oil. The hedge concept depends on non-correlation or negative interaction, on average, while the safe haven concept depends on non-correlation or negative correlation in times of market turmoil. We look at Bitcoin’s ability to be a hedge or safe haven asset with standard financial assets by considering a short investment horizon using high frequency data. Accordingly, we propose a new measure, the q-detrending moving average cross-correlation coefficient, to characterise intraday market interdependence between Bitcoin and these assets during medium and extreme movements. During medium fluctuations, Bitcoin is a weak hedge against currencies, oil, and stocks. During high fluctuations, we find a negative relationship between Bitcoin and oil, meaning Bitcoin can serve as a safe haven against extreme down movements in this market. However, Bitcoin is a weak safe haven asset for the other two markets.
The present study aims to establish the model of the cryptocurrency price trend based on financial theory using the LSTM model with multiple combinations between the window length and the predicting horizons, the random walk model is also applied with different parameter settings.
Purpose This study aims to analyze the dynamic relationship between the Bitcoin market and the conventional asset classes in India Design/methodology/approach This paper aims to cast light on the dynamic linkages between Bitcoin prices and other conventional asset classes in India by using the wavelet transform frameworks, which can allow us to analyze components of time series without losing the information. To do that, the techniques used with the data set include wavelet-based covariance, correlation, coherence spectrum, continuous power spectrum and Granger causality test. Findings The findings of the study suggest that interrelationships between Bitcoin and the key financial asset returns are statistically significant at low, medium and high frequencies. This study also finds the existence of the unidirectional connectedness between Bitcoin the other assets in India. Practical implications The outcome of the analysis calls for substantial policy implications for investors, portfolio management in India. This research on the existence of the interconnectedness between Bitcoin and other conventional asset classes in a specific country context, India can, therefore, make a significant contribution to the contemporary debate about the speculative nature of the cryptocurrencies. It casts light on whether Bitcoin provides any diversification and risk management benefits for Indian, as well as global investors. Originality/value To the best of the author’s knowledge, this is the first paper investigating the interrelatedness between Bitcoin and key conventional asset classes in India. This research makes methodological advancements by using the wavelet coherence transform. The findings provide empirical bases from which to deal with issues regarding hedging purposes and optimal portfolio allocation for an increasing number of investors in the Indian context. Therefore, the main contribution of this study to related literature in this field is significant.
Gianna Figà‐Talamanca, Sergio M. Focardi, Marco Patacca
Abstract In this paper, we apply dynamic factor analysis to model the joint behaviour of Bitcoin, Ethereum, Litecoin and Monero, as a representative basket of the cryptocurrencies asset class. The empirical results suggest that the basket price is suitably described by a model with two dynamic factors. More precisely, we detect one integrated and one stationary factor until the end of August 2019 and two integrated factors afterwards. Based on this evidence, we define a multiple long-short trading strategy which proves profitable when the second factor is stationary.
Chi‐Wei Su, Meng Qin, Xiaolei Zhang, Ran Tao · 5 authors
This paper probes the interrelationship between Bitcoin price (BP) and the U.S. partisan conflict (PC) by performing the bootstrap full- and sub-sample Granger causality tests. The positive influence from PC to BP reveals that Bitcoin can be considered as a tool to avoid the uncertainty caused by the rise in PC. However, this view cannot be supported by the negative impact, the major reason is that the burst of bubble undermines the hedging ability of Bitcoin. The above results are inconsistent with the intertemporal capital asset pricing model (ICAPM), underlining that high PC may drive BP to rise, in order to compensate for the losses and costs from factionalism. Conversely, BP has a negative impact on PC, suggesting that the U.S. political situation can be reflected by the Bitcoin market. Under the circumstance of the fiercer factionalism in the U.S., this investigation can benefit investors and related authorities.
Asymmetric relationship between price and volatility is a prominent feature of the financial market time series. This paper explores the price–volatility nexus in cryptocurrency markets and investigates the presence of asymmetric volatility effect between uptrend (bull) and downtrend (bear) regimes. The conventional GARCH-class models have shown that in cryptocurrency markets, asymmetric reactions of volatility to returns differ from those of other traditional financial assets. We address this issue from a viewpoint of fractal analysis, which can cover the nonlinear interactions and the self-similarity properties widely acknowledged in the field of econophysics. The asymmetric cross-correlations between price and volatility for Bitcoin (BTC), Ethereum (ETH), Ripple (XRP), and Litecoin (LTC) during the period from June 1, 2016 to December 28, 2020 are investigated using the MF-ADCCA method and quantified via the asymmetric DCCA coefficient. The approaches take into account the nonlinearity and asymmetric multifractal scaling properties, providing new insights in investigating the relationships in a dynamical way. We find that cross-correlations are stronger in downtrend markets than in uptrend markets for maturing BTC and ETH. In contrast, for XRP and LTC, inverted reactions are present where cross-correlations are stronger in uptrend markets.
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.