Study examines crypto-backed stable derivatives in DeFi, focusing on DAI.
problem Stability of crypto-backed stablecoins in DeFi.
method Introduced a belief parameter to simulate DAI, proposed a mathematical model, analyzed risk factors.
result Belief parameter improves simulation of DAI price stability.
This thesis builds a real-time VaR calculation workflow for crypto derivatives.
problem Managing risk in volatile cryptocurrency markets.
method Applied EMWA, GARCH, and HAR models to forecast volatility; used delta-gamma-theta approach and Cornish-Fisher expansion.
result Real-time VaR estimates with millisecond calculation latencies.
This paper systematizes knowledge on synthetic assets in crypto.
problem Disparate academic literature on synthetic assets in crypto.
method Broad perspective, general framework, data-driven analyses.
result Highlights risks and areas of research interest in synthetic assets.
Reinforcement learning crypto agent achieves high returns on Bitcoin derivatives.
problem Maximizing returns on volatile cryptocurrency markets.
method Online transfer learning with an echo state network and recurrent reinforcement learning.
result Achieves a total return of 350%, net of transaction costs, over five years.
Study finds Binance's tether-margined contracts significantly impact bitcoin volatility.
problem Understanding volatility transmission in the crypto market, especially through Binance.
method Analyzing high-frequency realised volatility dynamics and spillovers in bitcoin market pairs.
result Binance's tether-margined contracts are the primary source of volatility and transmit strong flows.
Despite being described as a medium of exchange, cryptocurrencies do not have the typical attributes of a medium of exchange. Consequently, cryptocurrencies are more appropriately described as crypto assets. A common investment attribute shared by the more than 2,500 crypto assets is that they are highly volatile. An i…
Investors prioritize ESG in crypto-assets, showing higher exposure than traditional assets.
problem Understanding ESG preferences in crypto-assets and their investment behavior.
method A representative household finance survey in Austria to examine ESG preferences and crypto-investment exposure.
result ESG-conscious investors have higher exposure to crypto-assets compared to traditional asset classes.
This study links blockchain design to cryptos' distributional characteristics.
problem Understanding the relationship between blockchain design and cryptos' distributional characteristics.
method Used spectral clustering to cluster cryptos based on their blockchain mechanisms and operational features.
result Clusters of cryptos share similar blockchain mechanisms, supporting the hypothesis.
Perpetual futures offer leverage without maturity, with prices influenced by funding rates.
problem Understanding and pricing perpetual futures with funding rates.
method Derive no-arbitrage prices and bounds in markets with trading costs. Empirically analyze deviations and Sharpe ratios of implied arbitrage strategies.
result Implied arbitrage strategies in crypto markets yield high Sharpe ratios, indicating significant pricing inefficiencies.
Estimates crypto risk premia using hidden factors and finds significant integration with traditional markets.
problem Estimating risk premia in cryptocurrency returns.
method Giglio-Xiu (2021) three-pass approach, controlling for latent factors and non-tradable state variables.
result Latent factors significantly impact crypto returns, highlighting the importance of controlling for unobserved risks.
This paper analyzes crypto white papers under MiCAR, highlighting NLP's role.
problem Regulatory changes in crypto white papers under MiCAR.
method Survey of existing NLP applications, analysis of MiCAR changes.
result NLP can assist in regulatory compliance and white paper analysis.
We propose a modelling framework for the optimal selection of crypto assets. Crypto assets differ by two essential features: security (technological) and stability (governance). Investors make choices over crypto assets similarly to how they make choices by using a recommender app: the app presents each investor with a…
Study shows SEC crypto classification led to significant market reactions.
problem Impact of SEC classification of crypto assets as securities.
method Event study methodology focusing on explicitly named crypto assets.
result Significant adverse market reactions, with returns plummeting 12% over one week.
New framework detects crypto wash trading using liquidity measures.
problem Detecting and monitoring wash trading in crypto assets.
method Developed a new framework to detect wash trading through real-time liquidity fluctuation measures.
result Joint elevation in liquidity jump and diffusion indicates wash trading in crypto assets.
ChatGPT launch boosted AI-related crypto assets by 10.7% to 15.6%.
problem Investor perception of AI assets after ChatGPT launch.
method Synthetic difference-in-difference methodology.
result AI-related crypto assets experienced significant returns after ChatGPT launch.
We explore inverse and quanto inverse crypto options, their pricing, and applications.
problem Market incompleteness in crypto options trading.
method Comparison of direct and inverse options, and introduction of currency-protected 'quanto' options.
result Pricing and hedging characteristics of inverse and quanto inverse options in a Black-Scholes framework.
Study proposes optimal risk-aware interest rates for crypto lending protocols.
problem Determining optimal interest rates for decentralized lending protocols to maximize profit and minimize risk.
method Agent-based model, Riccati-type ODEs for linear behaviors, Monte-Carlo estimator and deep learning for nonlinear behaviors.
result Calibrated model shows superior risk-adjusted performance compared to industry-standard interest rate models.
Method tracks change-points in crypto-assets extremes.
problem Tracking change-points in multivariate extremes.
method Statistical method for modeling change-points on crypto-assets extremes.
result Developed a method to track crypto-assets extremes.
Study reveals jumps in crypto markets predict future prices.
problem Understanding jumps in high frequency digital asset markets.
method High frequency crypto data analysis, econometric modeling.
result Intra-day jumps significantly influence end of day returns.
Crypto-assets perform better than gold as safe-havens during market crashes.
problem Evaluating safe-haven properties of crypto-assets and gold during the 2020 market crash.
method Comparative analysis of Crypto-assets (Tether, Cardano, Dogecoin, Bitcoin, Ethereum, Litecoin, Ripple) and gold for European indices.
result Tether, Cardano, and Dogecoin exhibited hedging properties similar to gold, while gold was not more efficient as a safe-haven.
Study shows how crypto asset liquidity is affected by wash trading and proposes treatment to reduce liquidity diffusion.
problem Understanding and reducing crypto asset wash trading to improve liquidity.
method Proposed a two-component model for liquidity (jump and diffusion) and demonstrated the effectiveness of autoregressive models.
result Treatment on wash trading significantly reduces liquidity diffusion but not liquidity jump.
SVAR-LiNGAM reveals causal order in crypto-asset markets.
problem Understanding the causal relationships between spot rates and crypto-assets.
method Applied SVAR-LiNGAM to analyze spot exchange rates and crypto-asset exchange rates.
result Causal order found: EUR_USD spot rate -> Bitcoin -> Ethereum -> Ripple.
RDLI integrates domain logic and context grounding to detect crypto anomalies under scarce labels.
problem Extreme label scarcity and evasion strategies in crypto networks.
method Relational Domain Logic Integration (RDLI) with Retrieval Grounded Context (RGC).
result RDLI outperforms GNN baselines by 28.9% in F1 score under 0.01% label scarcity.
Blockchain technology and, in particular, blockchain-based cryptocurrencies offer us information that has never been seen before in the financial world. In contrast to fiat currencies, all transactions of crypto-currencies and crypto-tokens are permanently recorded on distributed ledgers and are publicly available. As …
Study models crypto markets using multi-agent reinforcement learning.
problem Emulating crypto market dynamics and behaviors.
method Multi-agent reinforcement learning (MARL) with RL techniques.
result Model accurately emulates crypto market microstructure and behaviors.
Study finds strong link between crypto narratives and prices.
problem Understanding the impact of crypto narratives on prices.
method Topic modeling of Twitter data combined with sentiment analysis.
result Strong correlation between narratives and crypto prices.
Quantum crypto-economics models price risks in blockchain technology.
problem Quantum technology's potential to undermine blockchain security.
method Building financial models to price quantum risk in blockchain scenarios.
result Quantum crypto-economics models can assess and price quantum risks in blockchain.
Study reveals structure of Bitcoin's crypto flow network.
problem Understanding crypto flows among Bitcoin users.
method Blockchain data, user identification, network construction, bow-tie structure, Hodge decomposition, non-negative matrix factorization.
result Users are located in upstream, downstream, and core of the crypto flow network.
New framework predicts crypto volatility, outperforming traditional models.
problem Forecasting volatility in cryptocurrencies during the crypto-winter.
method Combines LSTM and rough volatility models, using a parsimonious parametric model.
result Similar prediction performances with fewer parameters, suggesting universality of volatility mechanisms.
Crypto markets show negative spillovers between chains, not positive co-movements.
problem Negative spillovers in crypto asset returns across different blockchains.
method On-chain data from multiple blockchains (Ethereum, Solana, Binance, Arbitrum, Avalanche) analyzed over 2022-2025.
result Surges on one chain often coincide with declines on others, especially during attention shocks.
Bitcoins have emerged as a possible competitor to usual currencies, but other crypto-currencies have likewise appeared as competitors to the Bitcoin currency. The expanding market of crypto-currencies now involves capital equivalent to 1010 US Dollars, providing academia with an unusual opportunity to study the em…
The year 2017 saw the rise and fall of the crypto-currency market, followed by high variability in the price of all crypto-currencies. In this work, we study the abrupt transition in crypto-currency residuals, which is associated with the critical transition (the phenomenon of critical slowing down) or the stochastic t…
Crypto simulations show HODL strategy loads risk onto most investors, with macro-sentiment affecting returns.
problem Understanding real risk-return trade-offs and factors affecting crypto returns.
method Two independent analyses: 480 million Monte Carlo simulations and Bayesian multi-horizon local projection framework.
result HODL strategy exposes most investors to extreme downside risk, and macro-sentiment conditions are dominant indicators for future outcomes.
This paper develops a new framework to assess crypto portfolio risk using simulation methods.
problem Traditional financial risk models fail to capture crypto market characteristics like volatility and contagion.
method The framework integrates four components: volatility stress testing, hedging, contagion modeling, and Monte Carlo simulation.
result The framework robustly assesses crypto portfolio risk and is validated with real data.
Study examines how crypto arbitrage affects XRP price and network correlation.
problem Impact of crypto arbitrage on XRP price and network correlation.
method Examined XRP price fluctuations and correlation tensor spectra of transaction networks across crypto exchanges.
result Arbitrage opportunities across crypto exchanges anti-correlate with XRP price during bubble periods.
Research identifies four motivational groups for crypto-metaverse landowners.
problem Understanding motivations of retail investors in the crypto-metaverse.
method Detailed financial behavior survey and principal components analysis.
result Four distinct motivational groups identified: Aesthetics, Social, Speculation, Innovation.
Paper uses AI to optimize crypto portfolios, showing better risk-adjusted returns.
problem Managing volatile crypto markets with high volatility.
method Multi-agent system designed to autonomously construct and evaluate crypto-asset allocations.
result Dynamic optimization strategy outperforms static equal weighting strategy in terms of risk-adjusted returns.
New method models asymmetric data with improved tail dependence.
problem Asymmetric data and tail dependence modeling.
method Generalized Skew-t Probabilistic Principal Component Analysis.
result Improved modeling of asymmetric data with tail effects.
CryptoRLPM uses on-chain data to improve crypto portfolio management performance.
problem Lack of effective use of on-chain data in RL-based crypto portfolio management.
method Developed CryptoRLPM, an RL-based system that incorporates on-chain data for crypto PM, consisting of five units.
result CryptoRLPM outperforms baselines in ARR, DRR, and SR, especially for Bitcoin.
This paper explains tax policy for crypto assets in a rapidly evolving tech landscape.
problem Rapid technological changes in crypto assets create regulatory and tax policy blind spots.
method Explains principles of crypto assets, their technology, and tax issues.
result Tax policies are lagging behind innovation in blockchain and crypto.
Improved crypto market forecasting using historical price reactions to tweets.
problem Challenges in inferring market impact from human sentiment labels.
method Market-derived labeling approach to assign tweet sentiment labels based on historical price trends. Fine-tuned language model with context-aware prompt-tuning.
result 89.6% accuracy on Bitcoin news events, outperforming traditional fusion models.
This paper explores crypto, blockchain, and Metaverse risks and opportunities.
problem Understanding crypto crashes and blockchain technologies.
method Interdisciplinary approach combining fintech, machine learning, and risk assessment.
result Blockchain technologies will continue to dominate, but discerning genuine projects is crucial.
Paper builds ML classifier to detect crypto-ransomware.
problem Detecting crypto-ransomware with high accuracy and low false positives.
method Behavior-based detection using input/output activities and file-content entropy. Deep-learning classifier with adversarial research and Integrated Gradient method for explanation.
result Deep-learning classifier achieves high accuracy and low false positive rate in detecting crypto-ransomware.
Study analyzes crypto asset risk exposures using a divide-and-conquer approach.
problem Lack of high-frequency macro-financial proxies for estimating risk.
method Two-stage divide-and-conquer approach: first stage estimates idiosyncratic and market risk, second stage identifies latent economy-wide factors.
result Heterogeneous exposures to idiosyncratic and systematic risk across crypto assets.
Ethereum trends analyzed through blockchain transactions and Google searches.
problem Identifying market manipulation in crypto prices.
method Big data analysis of Ethereum transactions, smart contracts, and search volumes.
result Big players manipulate crypto markets after price drops.
Study finds TVL doesn't predict cryptocurrency returns.
problem Assumption of TVL predicting returns in crypto markets.
method Examined TVL-sorted portfolios against crypto market returns, using various TVL measures.
result TVL-sorted portfolios' returns are linear functions of crypto market returns, replicable with standard tools.
Privacy-preserving crypto exchanges adjust prices based on Gaussian noise.
problem Ensuring fair pricing in privacy-preserving cryptocurrency exchanges.
method Derive Kyle equilibrium with Gaussian noise perturbation, rescaling price-impact and strategy factors.
result Identify a privacy subsidy as a transfer from LP pool to traders, invariant to noise.
Detects crypto pump-and-dump schemes with a thresholding-based model.
problem Detecting genuine anomalies from minor trading fluctuations.
method Combining threshold-based criteria with EWMA and volatility measures.
result Balances high true-positive detection with minimal noise.