Bitcoin reacts positively to USDT minting but not burning, showing state-dependence.
problem Understanding Bitcoin's response to Tether's supply changes.
method Analyzing Bitcoin's intraday price movements in response to USDT minting and burning events.
result Bitcoin's response to USDT minting events declines after 60 minutes and is influenced by investor sentiment and public announcements.
Stablecoin liquidity was affected by the SVB collapse, with USDC's transparency leading to market reactions.
problem Impact of stablecoin transparency on liquidity during market turmoil.
method Adapted MCI measure to Uniswap, Difference-in-Differences analysis on MCI and TVL, measured liquidity concentration.
result USDC's transparency led to swift market reactions, while USDT's opacity provided a safety net.
MiCA regulation led to a shift in stablecoin dominance.
problem Impact of MiCA regulation on stablecoin trading.
method Comparative analysis of regulated and non-regulated exchanges.
result USDC gained market share and trading volume post-MiCA regulation.
AutoQuant addresses cryptocurrency backtesting fragility by modeling execution costs and improving strategy selection.
problem Fragile backtests of cryptocurrency perpetual futures ignoring microstructure frictions and execution costs.
method Execution-centric framework with Bayesian optimization, double screening, and strict T+1 semantics.
result Fee-only and zero-cost backtests overestimate returns, highlighting the importance of modeling execution costs.
HFformer outperforms LSTM in high-frequency trading with multiple signals.
problem Improving high-frequency trading performance using deep learning models.
method Introducing HFformer, a hybrid Transformer model for time series forecasting.
result HFformer achieves higher cumulative PnL than LSTM in backtesting.
Model predicts depegging dynamics of stablecoins like Tether and Bitcoin.
problem Understanding depegging effects of stablecoins on cryptocurrencies.
method Multivariate Hawkes process model.
result Numerical example shows model's effectiveness.
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.
This paper introduces new methods for analysing the extreme and erratic behaviour of time series to evaluate the impact of COVID-19 on cryptocurrency market dynamics. Across 51 cryptocurrencies, we examine extreme behaviour through a study of distribution extremities, and erratic behaviour through structural breaks. Fi…
Novel AMM model for pegged cryptoassets using nested OU processes.
problem Liquidity and risk management in markets for pegged cryptoassets.
method Multi-level nested Ornstein-Uhlenbeck (OU) processes for exchange rate dynamics, calibrated and filtered AMM model.
result Consistent efficient quotes and improved liquidity provision for pegged cryptoassets.
This study compares CeFi and DeFi, finding some DeFi assets are not truly decentralized.
problem Understanding the differences and boundaries between CeFi and DeFi.
method Systematic analysis covering legal, economic, security, privacy, and market manipulation aspects.
result Certain DeFi assets may not be truly decentralized and pose security risks.
Uniswap V3 struggles with price accuracy during sudden market drops.
problem Price inaccuracies on Uniswap V3 during abrupt price drops.
method Empirical study of Uniswap V3's performance during market shocks.
result Liquidity providers lack agility and incentives in volatile conditions.
Study uses RL to optimize crypto portfolios with two-sided transactions and lending.
problem Managing downside risk and capital optimization in high-risk crypto markets.
method Integrates RL with a new environmental formulation and PnL-based reward function, using SAC agent with CNN-MHA.
result Significantly outperforms benchmarks, especially in high-volatility scenarios.
The paper develops a method to predict the latent deterioration phase in limit order books before stress is observed.
problem Limit order books can transition rapidly from stable to stressed conditions, making it difficult to detect the latent deterioration phase.
method The paper formalizes a three-regime causal data-generating process and proposes a trigger-based detector combining MAX aggregation of complementary signal channels, a rising-edge condition, and adaptive thresholding.
result The proposed method achieves mean lead-time of +18.6 timesteps with perfect precision and moderate coverage, outperforming classical change-point and microstructure baselines.
Machine learning predicts Bitcoin returns but trading performance drops with costs.
problem Trading Bitcoin predictions with transaction costs.
method XGBoost, LSTM, iTransformer models evaluated in walk-forward protocol; cost-aware execution filter implemented.
result Cost-aware execution filter restores profitability; XGBoost strategy outperforms.
Simpler models outperform deep architectures with proper preprocessing and tuning.
problem Signal extraction from noisy cryptocurrency LOB data.
method Benchmarked a range of models including deep architectures and interpretable baselines.
result Simpler models can match and exceed deep architectures' performance with proper preprocessing and tuning.
Adaptive TFTs improve cryptocurrency price prediction accuracy.
problem Precise short-term price prediction in volatile cryptocurrency markets.
method Dynamic subseries lengths and pattern-based categorization.
result Significantly outperforms baseline models in prediction accuracy and profitability.
Stablecoins are reshaping global monetary systems, offering hybrid structures with public and private monies.
problem The evolution of stablecoins from crypto innovation to a global monetary component.
method Econometric analysis and hybrid system design modeling.
result Stablecoins maintain strong peg stability, and a hybrid system design ensures financial resilience.
This paper uses a mean-field game to model stablecoin market dynamics and recovery.
problem Understanding who restores the peg during de-pegging events of stablecoins.
method Dynamic, agent-based mean-field game framework for fiat-collateralized stablecoins.
result The equilibrium formulation endogenously maps market frictions into a price path and order flows, allowing for stress testing and attribution of peg-reverting pressure.
Study examines cryptoasset service providers in Austria, revealing global integration and distinct responses to market shocks.
problem Understanding cryptoasset integration and stress behavior in national economies.
method Directly identified on-chain addresses of Austrian crypto-asset service providers, reconstructing transaction activity across multiple cryptocurrencies.
result Austrian crypto-asset service providers are globally integrated, with distinct responses to market shocks.
Tether's dominance in U.S. Treasury bills lowers bond yields by 24 basis points.
problem Impact of Tether's market share on U.S. Treasury bill yields.
method Baseline semi-log time trend model and threshold regression analysis.
result Tether's market share reduces 1-month yields by 24 basis points.
Study cryptocurrency market complexity using multifractal and cross-correlation analyses.
problem Understanding the complexity and dynamics of cryptocurrency markets, especially during the COVID-19 pandemic.
method Multifractal formalism, cross-correlation analyses, network representation.
result Cryptocurrency market dynamics exhibit multifractal and intermittent bifractality, with topology changes during significant events.