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3,636 results · page 136 of 152
Sashikanta Khuntia, J. K. Pattanayak
No abstract is available for this record.
Percy Venegas
Value in algorithmic currencies resides literally in the information content of the calculations; but given the constraints of consensus (security drivers) and the necessity for network effects (economic drivers), the definition of value extends to the multilayered structure of the network itself --that is, to the information content of the topology of the nodes in the blockchain network, and, on the complexity of the economic activity in the peripheral networks of the web, mesh-IoT networks, and so on. In this phase change between the information flows of the native network that serves as the substrate to the blockchain, and that of the real-world data, is where a new "fragility vector" emerges. Our research question is whether factors related to market structure and design, transaction and timing cost, price formation and price discovery, information and disclosure, and market maker and investor behavior, are quantifiable to the degree that can be used to price risk in digital asset markets. The results obtained show that while in the popular discourse blockchains are considered robust and cryptocurrencies anti-fragile, the cryptocurrency markets are in fact fragile. This research is pertinent to the regulatory function of governments, that are actively seeking to advance the state of knowledge regarding systemic risk, to develop policies for crypto markets, and for investors, who are in need of expanding their understanding of market behavior beyond explicit price signals and technical analysis.
Sungjoo Ha, Byung-Ro Moon
No abstract is available for this record.
Ke Wu, Spencer Wheatley, Didier Sornette
We empirically verify that the market capitalizations of coins and tokens in the cryptocurrency universe follow power-law distributions with significantly different values for the tail exponent falling between 0.5 and 0.7 for coins, and between 1.0 and 1.3 for tokens. We provide a rationale for this, based on a simple proportional growth with birth and death model previously employed to describe the size distribution of firms, cities, webpages, etc. We empirically validate the model and its main predictions, in terms of proportional growth (Gibrat's Law) of the coins and tokens. Estimating the main parameters of the model, the theoretical predictions for the power-law exponents of coin and token distributions are in remarkable agreement with the empirical estimations, given the simplicity of the model. Our results clearly characterize coins as being 'entrenched incumbents' and tokens as an 'explosive immature ecosystem', largely due to massive and exuberant Initial Coin Offering activity in the token space. The theory predicts that the exponent for tokens should converge to 1 in the future, reflecting a more reasonable rate of new entrants associated with genuine technological innovations.
Ke Wu, Spencer Wheatley, Didier Sornette
We empirically verify that the market capitalisations of coins and tokens in the cryptocurrency universe follow power-law distributions with significantly different values, with the tail exponent falling between 0.5 and 0.7 for coins, and between 1.0 and 1.3 for tokens. We provide a rationale for this, based on a simple proportional growth with birth & death model previously employed to describe the size distribution of firms, cities, webpages, etc. We empirically validate the model and its main predictions, in terms of proportional growth (Gibrat's law) of the coins and tokens. Estimating the main parameters of the model, the theoretical predictions for the power-law exponents of coin and token distributions are in remarkable agreement with the empirical estimations, given the simplicity of the model. Our results clearly characterize coins as being "entrenched incumbents" and tokens as an "explosive immature ecosystem", largely due to massive and exuberant Initial Coin Offering activity in the token space. The theory predicts that the exponent for tokens should converge to 1 in the future, reflecting a more reasonable rate of new entrants associated with genuine technological innovations.
Ladislav Krištoufek
No abstract is available for this record.
Gabriel Gajardo, Werner Kristjanpoller, Marcel C. Minutolo
No abstract is available for this record.
Aaron Brown
The same investors who invest in normal markets, using the same strategies they use in normal markets, for the same motivations they have in normal markets, can cause bubbles.
Leopoldo Catania, Stefano Grassi, Francesco Ravazzolo
This paper studies the predictability of cryptocurrencies time series. We compare several alternative univariate and multivariate models in point and density forecasting of four of the most capitalized series: Bitcoin, Litecoin, Ripple and Ethereum. We apply a set of crypto–predictors and rely on Dynamic Model Averaging to combine a large set of univariate Dynamic Linear Models and several multivariate Vector Autoregressive models with different forms of time variation. We find statistical significant improvements in point forecasting when using combinations of univariate models and in density forecasting when relying on selection of multivariate models.
Mareena Fernandes, Saloni Khanna, Leandra Monteiro, Anu Thomas · 5 authors
Advancement in technological developments introduced virtual currency exchange methods viz Bitcoin, Litecoin, Ethereum and so on which are evolving rapidly. Cryptocurrencies were introduced to eliminate financial intermediaries leading to direct peer-to-peer transactions. With the spread of the global Coronavirus pandemic, the relationship between Bitcoin and the equity market has expanded. Cryptocurrencies are highly volatile but can also prove to be good investments. Cryptocurrency, being a novel technique for transaction systems, has led to a lot of confusion among investors and any rumours or news on social media has been claimed to significantly affect the prices of cryptocurrencies. The huge percentage increase/decrease in Bitcoin's price over a short period of time is an intriguing phenomenon that cannot be foreseen. For a long time, bitcoin price prediction has been a hot topic of study.In this paper, we discuss the implementation and results of the Deep Learning Bitcoin Price Prediction Model and prepare a strategy to maximize gains for investors. The paper covers to framework with a set of deep learning models, analysis methods with a fixed set of factors to predict daily Bitcoin prices and design-integration of price prediction of different cryptocurrencies using RNN (Recurrent Neural Network), LSTM (Long Short-Term Memory) and GRU (Gated recurrent units). The idea of incorporating Public Sentiment in the prediction of the hikes and falls of the Bitcoin market from Social Media platforms like Reddit and Twitter leading to meaningful predicted results. This prediction can bring confidence to the common man to invest with lesser risk and more profit. Also, this can enable the digital new-age currency to become a primary method of transaction.
Stjepan Begušić, Zvonko Kostanjčar, H. Eugene Stanley, Boris Podobnik
Detection of power-law behavior and studies of scaling exponents uncover the characteristics of complexity in many real world phenomena. The complexity of financial markets has always presented challenging issues and provided interesting findings, such as the inverse cubic law in the tails of stock price fluctuation distributions. Motivated by the rise of novel digital assets based on blockchain technology, we study the distributions of cryptocurrency price fluctuations. We consider Bitcoin returns over various time intervals and from multiple digital exchanges, in order to investigate the existence of universal scaling behavior in the tails, and ascertain whether the scaling exponent supports the presence of a finite second moment. We provide empirical evidence on slowly decaying tails in the distributions of returns over multiple time intervals and different exchanges, corresponding to a power-law. We estimate the scaling exponent and find an asymptotic power-law behavior with 2 < α < 2.5 suggesting that Bitcoin returns, in addition to being more volatile, also exhibit heavier tails than stocks, which are known to be around 3. Our results also imply the existence of a finite second moment, thus providing a fundamental basis for the usage of standard financial theories and covariance-based techniques in risk management and portfolio optimization scenarios.
Yutaka Kurihara, Akio Fukushima
The trading volume of Bitcoin has increased immensely since its conception. Bitcoin is a cryptocurrency, it is not a legal currency but rather a private monetary system that manages by itself and does not depend on governments or central banks. It is an autonomous currency system that is not liable to any governing body. Some fear that the increase of Bitcoin usage, as it is quite different from traditional currencies and is free from control or regulations by monetary authorities. Although its popularity has grown worldwide, fluctuations of the prices are sometimes erratic. Hence, such large and sudden movements would dampen the sound development of Bitcoin. This paper examines how the volatile price of Bitcoin changes empirically. The empirical results show that there is a difference between short-term volatility and long-term volatility. Traders should see not only the short-term movements in volatile Bitcoin pricing but also long-term developments.
Eng-Tuck Cheah, Tapas Mishra, Mamata Parhi, Zhuang Zhang
No abstract is available for this record.
Tian Guo, Albert Bifet, Nino Antulov-Fantulin
Bitcoin is one of the most prominent decentralized digital cryptocurrencies. Ability to understand which factors drive the fluctuations of the Bitcoin price and to what extent they are predictable is interesting both from the theoretical and practical perspective. In this paper, we study the problem of the Bitcoin short-term volatility forecasting based on volatility history and order book data. Order book, consisting of buy and sell orders over time, reflects the intention of the market and is closely related to the evolution of volatility. We propose temporal mixture models capable of adaptively exploiting both volatility history and order book features. By leveraging rolling and incremental learning and evaluation procedures, we demonstrate the prediction performance of our model as well as studying the robustness, in comparison to a variety of statistical and machine learning baselines. Meanwhile, our temporal mixture model enables to decipher the time-varying effect of order book features on volatility. It demonstrates the prospect of our temporal mixture model as an interpretable forecasting framework over heterogeneous Bitcoin data.
Tian Guo, Nino Antulov-Fantulin
In this paper, we study the ability to make the short-term prediction of the exchange price fluctuations towards the United States dollar for the Bitcoin market. We use the data of realized volatility collected from one of the largest Bitcoin digital trading offices in 2016 and 2017 as well as order information. Experiments are performed to evaluate a variety of statistical and machine learning approaches.
Li Guo, Yubo Tao, Wolfgang Karl Härdle
Cryptocurrencies are becoming an attractive asset class and are the focus of recent quantitative research. The joint dynamics of the cryptocurrency market yields information on network risk. Utilizing the adaptive LASSO approach, we build a dynamic network of cryptocurrencies and model the latent communities with a dynamic stochastic blockmodel. We develop a dynamic covariate-assisted spectral clustering method to uniformly estimate the latent group membership of cryptocurrencies consistently. We show that return inter-predictability and crypto characteristics, including hashing algorithms and proof types, jointly determine the crypto market segmentation. Based on this classification result, it is natural to employ eigenvector centrality to identify a cryptocurrency’s idiosyncratic risk. An asset pricing analysis finds that a cross-sectional portfolio with a higher centrality earns a higher risk premium. Further tests confirm that centrality serves as a risk factor well and delivers valuable information content on cryptocurrency markets.
Li Guo, Yubo Tao, Wolfgang Karl Härdle
Cryptocurrencies return cross-predictability yields information on risk propagation and market segmentation. To explore these effects, we build a dynamic network of cryptocurrencies based on the evolution of return cross-predictability and develop a dynamic covariate-assisted spectral clustering method to consistently estimate the latent group membership of cryptocurrencies. We show that return cross-predictability and cryptocurrencies' characteristics, including hashing algorithms and proof types, jointly determine the cryptocurrencies market segmentation. Portfolio analysis reveals that more centred cryptocurrencies in the network earn higher risk premiums.
Li Guo, Wolfgang Karl Härdle, Yubo Tao
Cryptocurrencies return cross-predictability and technological similarity yield information on risk propagation and market segmentation. To investigate these effects, we build a time-varying network for cryptocurrencies, based on the evolution of return cross-predictability and technological similarities. We develop a dynamic covariate-assisted spectral clustering method to consistently estimate the latent community structure of cryptocurrencies network that accounts for both sets of information. We demonstrate that investors can achieve better risk diversification by investing in cryptocurrencies from different communities. A cross-sectional portfolio that implements an inter-crypto momentum trading strategy earns a 1.08% daily return. By dissecting the portfolio returns on behavioral factors, we confirm that our results are not driven by behavioral mechanisms.
Carey Caginalp, Gunduz Caginalp
Cryptocurrencies are examined through the asset flow equations and experimental asset markets. Since tangible value of a typical cryptocurrency is non-existent, the theory suggests that price will gravitate toward liquidity value, i.e., the total amount of cash available for purchase of the asset divided by the number of units. Thus it is unlikely that cryptocurrencies in their current form will be stable in the absence of a mechanism of a link to value.
Alexander Brauneis, Roland Mestel
No abstract is available for this record.
Κωνσταντίνος Γκίλλας, Paraskevi Katsiampa
No abstract is available for this record.
Jesús Fernández‐Villaverde, Daniel R. Sanches
Can a monetary system in which privately issued cryptocurrencies circulate as media of exchange work? Is such a system stable? How should governments react to digital currencies? Can these currencies and government-issued money coexist? Are cryptocurrencies consistent with an e cient allocation? These are some of the important questions that the sudden rise of cryptocurrencies has brought to contemporary policy discussions. To answer these questions, we construct a model of competition among privately issued at currencies. We nd that a purely private arrangement fails to implement an e cient allocation, even though it can deliver price stability under certain technological conditions. Currency competition creates problems for monetary policy implementation under conventional methods. However, it is possible to design a policy rule that uniquely implements an e cient allocation by driving private currencies out of the market. We also show that unique implementation of an e cient allocation can be achieved without government intervention if productive capital is introduced.
Theodore Panagiotidis, Thanasis Stengos, Orestis Vravosinos
We examine the significance of twenty-one potential drivers of bitcoin returns for the period 2010–2017 (2533 daily observations). Within a LASSO framework, we examine the effects of factors such as stock market returns, exchange rates, gold and oil returns, FED’s and ECB’s rates and internet trends on bitcoin returns for alternate time periods. Search intensity and gold returns emerge as the most important variables for bitcoin returns.
Ender Demir, Giray Gözgör, Chi Keung Marco Lau, Samuel A. Vigne
No abstract is available for this record.