Cryptocurrency and NFT prices are highly correlated, mirroring historical bubbles.
problem Evaluating the wealth effect of cryptocurrency prices on real estate.
method Exploiting metaverse LAND and cryptocurrencies to track correlations and causality.
result Cryptocurrency prices Granger cause NFT LAND prices, similar to historical bubbles.
Study evaluates cryptocurrency markets, focusing on Bitcoin.
problem Evaluating financial markets, especially during crises.
method Multiple-bubble testing approach.
result Identifies Bitcoin's multiple bubbles during economic periods.
The paper models cryptocurrency market bubbles using agent-based models.
problem Understanding speculative bubbles in cryptocurrency markets.
method Agent-based models and ODE models to estimate return rates and market values.
result Formulated formula for total system risk.
New dataset and models detect cryptocurrency bubbles using social media data.
problem Detecting anomalous market behavior in cryptocoins and meme stocks.
method Developed a novel multi-span identification task and sequence-to-sequence hyperbolic models.
result Models effectively detect cryptocoins and meme stocks bubbles in zero-shot settings.
This study examines non-performing assets and cryptocurrencies in Japan.
problem Economic downturn led to non-performing loans, affecting financial institutions.
method Literature analysis to summarize development, issuance, supervision, etc.
result Cryptocurrencies are being regulated in Japan despite non-performing loans.
This paper surveys cryptocurrency trading research, covering various aspects.
problem Understanding the unique nature and behavior of cryptocurrencies as assets.
method Comprehensive review of 146 research papers on cryptocurrency trading.
result Identifies promising open opportunities in cryptocurrency trading.
What is the role of social interactions in the creation of price bubbles? Answering this question requires obtaining collective behavioural traces generated by the activity of a large number of actors. Digital currencies offer a unique possibility to measure socio-economic signals from such digital traces. Here, we foc…
The functioning of the cryptocurrency Bitcoin relies on the open availability of the entire history of its transactions. This makes it a particularly interesting socio-economic system to analyse from the point of view of network science. Here we analyse the evolution of the network of Bitcoin transactions between users…
In the past decade, Bitcoin as an emerging asset class has gained widespread public attention because of their extraordinary returns in phases of extreme price growth and their unpredictable massive crashes. We apply the log-periodic power law singularity (LPPLS) confidence indicator as a diagnostic tool for identifyin…
Modeling financial crises and cryptocurrency shocks using copulae clustering.
problem Detecting financial crises and shock events in stock and cryptocurrency markets.
method Copulae clustering based on probability distribution distances.
result Successfully detected all past crises and shock events in stock and cryptocurrency markets.
Model optimal liquidation in asset bubbles with varying entry times.
problem Optimal liquidation in asset bubbles with variable entry times and exogenous crashes.
method Mean field game (MFG) with varying entry times and progressive enlargement of filtrations.
result Existence of MFG equilibria and decomposition of equilibrium strategies.
Although Bitcoin has long been dominant in the crypto scene, it is certainly not alone. Ether is another cryptocurrency related project that has attracted an intensive attention because of its additional features. This study seeks to test whether these cryptocurrencies differ in terms of their volatile and speculative …
Study examines cryptocurrency risk spillover effects before and after pandemic.
problem Analyzing risk propagation among cryptocurrencies during extreme events.
method Asymmetric breakpoint approach and network analysis.
result Cryptocurrency risk spillover effect increased during pandemic.
The paper uses AI to analyze on-chain parameters and identify risky cryptocurrencies.
problem Identifying risky cryptocurrencies and understanding their price factors.
method Historical data analysis, AI algorithms, clustering, classification.
result A significant negative correlation between cryptocurrency price and maximum and total supply, and a weak positive correlation with 24-hour trading volume.
Characterizes critical points in convex double and triple bubbles.
problem Critical points of double and triple bubbles in convex shapes.
method Characterization through stationary varifolds in Rn and R3. result Characterization of critical points in convex shapes.
Cryptocurrencies show similarities to traditional markets but also have unique characteristics.
problem Understanding the investment potential and characteristics of cryptocurrencies.
method Organized stylized facts and analyzed through empirical asset pricing.
result Cryptocurrencies exhibit similarities to traditional markets but also have distinct characteristics.
Study analyzes cryptocurrency market complexity, comparing it to traditional markets.
problem Understanding the dynamics and characteristics of cryptocurrency markets.
method Statistical physics methods and analysis of price fluctuations.
result Cryptocurrency market exhibits complexity similar to traditional markets but with slower information flow.
Study shows cryptocurrency investor base affects volatility.
problem Investor base changes impact cryptocurrency volatility.
method Proxying investor base with subreddit follower changes, analyzed idiosyncratic volatility.
result Changes in cryptocurrency investor base significantly increase idiosyncratic volatility.
TDA improves cryptocurrency portfolio management.
problem Traditional methods fail to manage cryptocurrencies effectively.
method Topological Data Analysis (TDA) for identifying investment opportunities.
result TDA-based portfolio management outperforms traditional methods.
Cryptocurrencies use blockchain tech for secure transactions, offering new research opportunities.
problem Misunderstanding of cryptocurrency technology and lack of empirical data.
method Analyzing detailed transaction data and summarizing statistics.
result Opportunity for academic research in financial economics.
Survey on soap bubble partitions and their stability.
problem Characterizing and stabilizing soap bubble partitions.
method Survey and analysis of recent research.
result Recent advancements in multi-bubble isoperimetric minimizers and stability.
We endorse the idea, suggested in recent literature, that BitCoin prices are influenced by sentiment and confidence about the underlying technology; as a consequence, an excitement about the BitCoin system may propagate to BitCoin prices causing a Bubble effect, the presence of which is documented in several papers abo…
Clusters cryptocurrency market states via cross correlation analysis.
problem Analyse cryptocurrency market dynamics.
method Cross correlation structure analysis over 5 years.
result Cryptocurrency market clusters into 4 states.
This paper models cryptocurrencies using α-stable distributions, outperforming other models.
problem Modeling the highly speculative and leptokurtic nature of cryptocurrencies.
method Used α-stable distribution and compared it with other heavy tailed distributions. Employed maximum likelihood method for estimation. result The α-stable distribution fits cryptocurrency return data better than other models. Investigates if adding cryptocurrencies to German portfolios diversifies better, finding mixed results.
problem Improving diversification in German investor portfolios using cryptocurrencies.
method Portfolio analysis with descriptive statistics, graphical methods, and econometric spanning tests, using a customized EWCI.
result Cryptocurrencies can improve diversification in some windows but not as a normal case.
Bubbles are essential in certain economic models with high growth and low interest rates.
problem Asset price bubbles exceeding fundamental values.
method Developed the Bubble Necessity Theorem in economic models with specific growth and interest rate conditions.
result Bubbles are inevitable in certain economic scenarios with high growth and low interest rates.
DBNs predict cryptocurrency price directions by uncovering causal relationships.
problem Predicting cryptocurrency price movements due to volatility and external factors.
method Dynamic Bayesian Networks (DBN) approach to identify causal relationships among features.
result DBN significantly outperforms baseline models in predicting cryptocurrency prices.
Transformer predicts Ethereum prices using cross-currency correlation and sentiment analysis.
problem Predicting Ethereum cryptocurrency prices with limited data.
method Transformer-based neural network with cross-currency correlation and sentiment analysis.
result Transformer model outperforms other models on some parameters.
Trend following in cryptocurrencies yields high returns, similar to commodities.
problem Investing in cryptocurrencies using trend following strategies.
method A decade of data analysis on cryptocurrency markets and trend following strategies.
result Cryptocurrencies offer strong returns and diversification against traditional equities.
Optimizes cryptocurrency portfolios using MNTS GARCH model.
problem Optimizing cryptocurrency portfolios with non-Gaussian return dynamics.
method Multivariate normal tempered stable (MNTS) GARCH model for non-Gaussian returns, Foster-Hart risk optimization.
result Foster-Hart optimization yields a more profitable portfolio with better risk-return balance.
Thousands of cryptocurrencies have been issued and publicly exchanged since Bitcoin was invented in 2008. The total cryptocurrency market value exceeds 300 billion US dollars as of 2019. This paper analyzes the prices, volumes, blockchain transactions, coin difficulties and public opinion popularities of 3607 actively …
GRF models predict cryptocurrency VaR better than other methods.
problem Predicting Value at Risk (VaR) for volatile cryptocurrencies.
method Generalized Random Forests (GRF) adapted for quantile prediction.
result GRF models outperform other methods in cryptocurrency VaR predictions.
Cryptocurrencies evolve through survival of the fittest, modeled with evolutionary finance.
problem Understanding the dynamics of cryptocurrency markets.
method Evolutionary finance concepts applied to toy models of cryptocurrency data.
result Survival of the fittest in cryptofinance is explained through scaling laws.
This study attempts to analyze patterns in cryptocurrency markets using a special type of deep neural networks, namely a convolutional autoencoder. The method extracts the dominant features of market behavior and classifies the 40 studied cryptocurrencies into several classes for twelve 6-month periods starting from 15…
Study Yang-Mills connections on four-manifolds, derive obstructions to bubbling.
problem Bubbling configurations in Yang-Mills fields on four-manifolds.
method Derived Pohozaev type compatibility between weak limit connection and bubbles, involving Weyl tensor.
result Obstructions to certain bubbling configurations on CP2.
Investigate the evolving structure of cryptocurrency interactions using high-frequency returns.
problem Evolution of cryptocurrency interactions
method Construct directed and weighted networks from Granger causal relationships between cryptocurrency log-returns.
result Normalized returns exhibit heavy-tailed distributions.
Rational bubbles form in nonstationary models of real assets.
problem Understanding the emergence of rational bubbles in real assets.
method Developed economic models showing bubbles inevitably emerge in nonstationary systems.
result Bubbles in real assets are inevitable and can be analyzed using mathematical theorems.
A time-varying network reveals community structure in cryptocurrencies.
problem Investing in cryptocurrencies from different communities can diversify risk.
method Dynamic covariate-assisted spectral clustering method.
result Investors can earn 1.08% daily return by diversifying across communities.
Cryptocurrencies have heavy-tailed return distributions, requiring diversification.
problem Cryptocurrency returns do not follow Gaussian distributions.
method Applied econophysics and entropy measures to analyze returns.
result Portfolio diversification reduces return uncertainty.
Existence of double bubbles with high constant mean curvatures in Riemannian manifolds.
problem Existence of double bubbles with high constant mean curvatures in Riemannian manifolds.
method Perturbations of geodesic standard double bubbles centered at critical points of the ambient scalar curvature and aligned along eigen-vectors of the ambient Ricci tensor, with general multiplicity results via Lusternik-Schnirelman theory.
result Existence of double bubbles with high constant mean curvatures in Riemannian manifolds.
Network analysis reveals changing cryptocurrency market leaders.
problem Understanding evolving cryptocurrency market leaders and their influence.
method Hourly-resolution data and Kendall's Tau correlation for network analysis.
result Pearson's correlation underestimates market dynamics; FTT and FTX were key during the 2021 bull run.
Deep learning predicts cryptocurrency price movements with 78% accuracy.
problem Predicting price formation in cryptocurrency markets with high volatility and illiquidity.
method Applied deep learning to predict mid-price changes on live tick-level cryptocurrency data.
result Achieved 78% accuracy in predicting mid-price movement of Bitcoin vs USD.
The cryptocurrency market surpassed the barrier of \$100 billion market capitalization in June 2017, after months of steady growth. Despite its increasing relevance in the financial world, however, a comprehensive analysis of the whole system is still lacking, as most studies have focused exclusively on the behaviour o…
Cryptocurrency time-series predictability is low, resembling Brownian noise.
problem Low predictability of cryptocurrency exchange rates.
method Complexity and model predictions of Litecoin, Binance Coin, Bitcoin, Ethereum, and XRP exchange rates.
result Simpler models outperform complex ones in cryptocurrency forecasting.
We study the problem of predicting whether the price of the 21 most popular cryptocurrencies (according to coinmarketcap.com) will go up or down on day d, using data up to day d-1. Our C2P2 algorithm is the first algorithm to consider the fact that the price of a cryptocurrency c might depend not only on historical pri…
Defines speculative bubbles in discrete-time models based on discounted stock price losing mass.
problem Characterizing speculative bubbles in discrete-time models.
method Introduces a new definition based on discounted stock price behavior and provides probabilistic characterizations.
result Speculative bubbles in discrete time are linked to solutions of a linear Volterra integral equation.
Degenerate solutions found in 2D H-system bubbles with higher degrees.
problem Existence of degenerate solutions in H-system bubbles with degree ≥ 3.
method Algebraic characterization of degenerate bubbles.
result Degenerate solutions can exist for H-system bubbles with degree ≥ 3.
CryptoGAT improves cryptocurrency price prediction by treating it as a graph problem.
problem Cryptocurrency price prediction challenges due to extreme volatility.
method CryptoGAT, a Graph Attention Network, redefines cryptocurrency prediction as a cross-asset graph problem.
result CryptoGAT outperforms state-of-the-art methods in cryptocurrency price prediction.