Since the inception of Bitcoin in 2008, cryptocurrencies have played an increasing role in the world of e-commerce, but the recent turbulence in the cryptocurrency market in 2018 has raised some concerns about their stability and associated risks. For investors, it is crucial to uncover the dependence relationships between cryptocurrencies for a more resilient portfolio diversification. Moreover, the stochastic behavior in both tails is important, as long positions are sensitive to a decrease in prices (lower tail), while short positions are sensitive to an increase in prices (upper tail). In order to assess both risk types, we develop in this paper a flexible copula model which is able to distinctively capture asymptotic dependence or independence in its lower and upper tails simultaneously. Our proposed model is parsimonious and smoothly bridges (in each tail) both extremal dependence classes in the interior of the parameter space. Inference is performed using a full or censored likelihood approach, and we investigate by simulation the estimators' efficiency under three different censoring schemes which reduce the impact of non-extreme observations. We also develop a local likelihood approach to capture the temporal dynamics of extremal dependence among two leading cryptocurrencies. We here apply our model to historical closing prices of five leading cryotocurrencies, which share most of the cryptocurrency market capitalizations. The results show that our proposed copula model outperforms alternative copula models and that the lower tail dependence level between most pairs of leading cryptocurrencies -- and in particular Bitcoin and Ethereum -- has become stronger over time, smoothly transitioning from an asymptotic independence regime to an asymptotic dependence regime in recent years, whilst the upper tail has been relatively more stable overall at a weaker dependence level.
Anwar Hasan Abdullah Othman, Syed Musa Alhabshi, Razali Haron
Purpose This paper aims to examine whether the crypto-currencies’ market returns are symmetric or asymmetric informative, through analysing the daily logarithmic returns of bitcoin currency over the period of 2011-2017. Design/methodology/approach In doing so, the symmetric informative analysis is estimated by applying the generalised auto-regressive conditional heteroscedasticity (GARCH) (1,1) model, whereas asymmetric informative or leverage effects analysis is estimated by exponential GARCH (1,1), asymmetric power ARCH (1,1) and threshold GARCH (1,1) models. In addition, the generalized autoregressive conditional heteroskedasticity in mean (GARCH-M (1,1)) was applied to examine whether the risk-return trade-off phenomenon was persistent in crypto-currencies market. Findings The main findings indicate that bitcoin market return or volatility is symmetric informative and has a long memory to persist in the future. Furthermore, the sympatric volatility is found to be more sensitive to its past values (lagged) than to the new shock of the market values. However, asymmetric informative response of volatility to the negative and the positive shocks do not exist in the bitcoin market or, in other words, there is no leverage effect. This suggests that the bitcoin market is in harmony with the efficient market hypothesis (EMH) with respect to the asymmetric information and violated the EMH with regard to the symmetric information. Hence, the market price or return of bitcoin currency could not be predicted by simply exercising such past market information in the short-run investment. In addition, the estimated coefficient of conditional variance or risk premium (λ) in the mean equation of CHARCH–M (1,1) model is positive however, statistically insignificant. This indicates the absence of risk-return trade-off, in which case the higher market risk will not essentially lead to higher market returns. This paper has proposed that an investment in the crypto-currency market is more appropriate for risk-averse investors than risk takers. Originality/value The findings of the study will provide investors with necessary information about the bitcoin market price efficiency, hedging effectiveness and risk management.
The article introduces a classification of research programs related to the cryptocurrency market. Each of them is surveyed, with the emphasis placed on the most recent studies. Despite the presence of market frictions, its informational efficiency tends to increase, thereby making it more comparable with the conventional financial markets. In particular, cryptocurrencies can be used for portfolio diversification. Yet, they can hardly compete with fiat money. Currently, central banks are only interested in examining and adopting some of the cryptocurrency features to create their own wholesale digital currencies.
This study proposes to investigate the dynamic relationships between the three weather factors (temperature, humidity, and wind speed) in New York City of USA and Coinbase Index from Federal Reserve Bank of St. Louis, in the USA. Statistical tools like Descriptive Statistics, Unit Root, Granger Causality Test and Johansen Co-Integration test were employed. This study clearly found that the temperature influenced the investors’ mood and their investment decision in respect of Cryptocurrency index (Coinbase Index) and also found that there was long run equilibrium between the sample variables during the study period. The results of study provided strong evidence against the Efficient Market Hypothesis (EMH).
The paper deals with the problems of measuring uneven wealth distribution in the bitcoin ecosystem. All existing bitcoin distribution models depend on the analysis of bitcoin wallets and bitcoin addresses. They are based on the Bitcoin Rich List. This approach is insufficient due to the inscrutable relationships between people owning bitcoin, bitcoin wallets, and bitcoin addresses. In this paper, we used the methods of comparative analysis resulted in graphics as represented by Lorentz and Lamé curves and distribution of the Gini coefficients and the Kolkata index. We identified empirical cumulative functions of wealth distribution and the number of addresses with positive balance during the bubble and after its explosion. Approximations of the distribution of ‘poor’ and ‘rich’ addresses have been obtained and compared with the other results from the cited literature. The general public views the equality of network members as synonymous with the equal distribution of wealth among them. Emerging financial bubbles, especially in the US financial markets, lead to an increase in income inequality. However, after a bubble explodes, the inequality falls to the initial level.
Bitcoin is a digital financial asset that is devoid of a central authority. This makes it distinct from traditional financial assets in a number of ways. For instance, the total number of tokens is limited and it has not explicit use value. Nonetheless, little is know whether it obeys the same stylized facts found in traditional financial assets. Here we test bitcoin for a set of these stylized facts and conclude that it behaves statistically as most of other assets. For instance, it exhibits aggregational Gaussianity and fluctuation scaling. Moreover, we show by an analogy with natural occurring quakes that bitcoin obeys both the Omori and Gutenberg-Richter laws. Finally, we show that the global persistence, originally defined for spin systems, presents a power law behavior with exponent similar to that found in stock markets.
Juan Plazuelo Pascual, Carlos Tardon Rubio, Juan Toro Cebada, Angel Hernando Veciana
This document analyzes price discovery in cryptocurrency markets by comparing centralized and decentralized exchanges, as well as spot and futures markets. The study focuses first on Ethereum (ETH) and then applies a similar approach to Bitcoin (BTC). Chapter 1 outlines the theoretical framework, emphasizing the structural differences between centralized exchanges and decentralized finance mechanisms, especially Automated Market Makers (AMMs). It also explains how to construct an order book from a liquidity pool in a decentralized setting for comparison with centralized exchanges. Chapter 2 describes the methodological tools used: Hasbrouck's Information Share, Gonzalo and Granger's Permanent-Transitory decomposition, and the Hayashi-Yoshida estimator. These are applied to explore lead-lag dynamics, cointegration, and price discovery across market types. Chapter 3 presents the empirical analysis. For ETH, it compares price dynamics on Binance and Uniswap v2 over a one-year period, focusing on five key events in 2024. For BTC, it analyzes the relationship between spot and futures prices on the CME. The study estimates lead-lag effects and cointegration in both cases. Results show that centralized markets typically lead in ETH price discovery. In futures markets, while they tend to lead overall, high-volatility periods produce mixed outcomes. The findings have key implications for traders and institutions regarding liquidity, arbitrage, and market efficiency. Various metrics are used to benchmark the performance of modified AMMs and to understand the interaction between decentralized and centralized structures.
The literature has recently begun to investigate the properties of cryptocurrency markets to identify key drivers for the use of cryptocurrencies in investment strategies. This paper provides a comprehensive review on the financial applications of Bitcoin. The focus is on three lines of research: price formation, detection of market inefficiency, and diversified portfolio construction. Topics such as market micro-structure and the interplay between different cryptocurrencies are only touched on briefly. We observe that many empirical studies find that Bitcoin markets are inefficient, with huge price fluctuations and long-range memory, and that these markets are heavily influenced by news and sector-specific events, or by infrastructure conditions such as volume trading and market liquidity. Nevertheless, astonishing price appreciations and modest correlation values versus other asset classes have contributed significantly to motivate applications of Bitcoin to investment and diversification. Future research may address practical implementations of such solutions and investigate the long-term sustainability and viability of these investment strategies.
This study conducts a systematic survey on whether the pricing behavior of cryptocurrencies is predictable. Thus, the Efficient Market Hypothesis is rejected and speculation is feasible via trading. We center interest on the Rescaled Range (R/S) and Detrended Fluctuation Analysis (DFA) as well as other relevant methodologies of testing long memory in returns and volatility. It is found that the majority of academic papers provides evidence for inefficiency of Bitcoin and other digital currencies of primary importance. Nevertheless, large steps towards efficiency in cryptocurrencies have been traced during the last years. This can lead to less profitable trading strategies for speculators.
We compare Bitcoin performance based on the Aumann and Serrano performance index and Sharpe ratio assuming that asset returns follow the class of discrete normal mixture distributions. The Aumann and Serrano performance index can take into account higher moments of the underlying distribution of assets and is relevant for risk-averse investors. We evaluate Bitcoin performance based on the Aumann and Serrano index relative to the performance of other assets. Our evaluation shows that Bitcoin is rated highly by the Sharpe ratio but rated very poorly by the Aumann and Serrano index. We also find some stock assets can beat Bitcoin by the Sharpe ratio when an investment horizon is monthly.
Cryptocurrencies have developed very dynamically although their future role is yet unclear. In any event, they are too big to ignore. The purpose of this article is to contribute to the understanding of cryptocurrencies in an individual and in a portfolio context. The study is based on daily closing prices of leading cryptocurrencies (Bitcoin, Ethereum, Ripple, Litecoin, and Dash) and fiat currencies (EUR, GBP, CHF, CAD, and JPY), all measured against USD. The analysis is threefold: First, the authors analyze basic statistical properties, such as correlation and autocorrelation of returns. Second, they perform a Kolmogorov–Smirnov test (KS test) and a variance ratio test (VRT) with heteroscedasticity adjustment. Third, they solve more than 4,800 optimization problems to analyze the impact of individual crypto- and fiat currencies on portfolio diversification. Among other findings, the authors find that Bitcoin, Ethereum, Dash, CAD, JPY, and EUR contribute most to reduce the variance of a mixed portfolio. In a portfolio consisting of cryptocurrencies only, Bitcoin and Ripple have the largest diversification effect. The findings provide insights for investors who focus on minimum variance portfolios or, more generally, for investors who seek to reduce return volatility exposure, as well as for monetary authorities, cryptocurrency issuers, and providers of market infrastructure. <b>TOPICS:</b>Currency, statistical methods, portfolio construction
Abstract Periodically collapsing rational bubbles model speculative demand in asset markets. The price and quantity of bitcoin are integrated of different orders, which is evidence of a bubble. Cointegration tests that allow for the potential presence of such bubbles with alternative proxies for fundamentals cannot reject a bubble in bitcoin.