Zero-Liquidation loans protect ETH borrowers from liquidation risks.
problem Risk of liquidation in DeFi lending protocols.
method Allows borrowers to repay in either USDC or pledged ETH, compensating liquidity providers with higher yield.
result More robust and less contagion-prone lending compared to traditional protocols.
Based on the high-frequency recordings from Kraken, a cryptocurrency exchange and professional trading platform that aims to bring Bitcoin and other cryptocurrencies into the mainstream, the multiscale cross-correlations involving the Bitcoin (BTC), Ethereum (ETH), Euro (EUR) and US dollar (USD) are studied over the pe…
This study compares price discovery in ETH and BTC markets between centralized and decentralized exchanges.
problem Understanding price discovery dynamics in cryptocurrency markets.
method Comparative analysis of centralized and decentralized exchanges, using econometric tools.
result Centralized exchanges lead in ETH price discovery, while futures markets lead in BTC.
Study shows priority access in ELA auctions is less valuable due to volatility risks.
problem Impact of volatility on time-based transaction ordering policies.
method Single-round model with risk-averse bidders, tested using ETH price data.
result Priority access is discounted due to volatility and risk aversion.
Optimizes Ethena's yield strategy by controlling stETH and ETH futures positions.
problem Maximizes Ethena's yield while managing price impacts.
method Formulates and solves stochastic control problems for Ethena's yield-generating strategy.
result Explicitly determines optimal control rates for stETH and ETH futures.
Ethereum's Pectra upgrade introduces 0x02 compounding validators, offering higher stake and potential APR uplifts.
problem Staking rewards on Ethereum
method Analyzing compounding effects on consensus-layer rewards
result 0x02 validators achieve modestly higher median CL APR
Develops a new model for measuring extremal dependence in financial markets.
problem Lack of suitable models for studying extremal dependence in financial markets.
method Constructs regular variation models on Rd and develops a bivariate measure for asymmetry in extremal dependence. result Rejects the Efficient Tail Hypothesis for China's futures market and identifies profitable investment opportunities.
Paper uses ANFIS to predict cryptocurrency prices.
problem Predicting cryptocurrency prices for seven days.
method Adaptive Network Based Fuzzy Inference System (ANFIS) with hybrid and backpropagation algorithms.
result The method can predict cryptocurrency prices in a short time.
This paper surveys a few aspects of the global theory of wave equations. This material is structured around the contents of a minicourse given by the second author during the CMI/ETH Summer School on evolution equations during the Summer of 2008.
Sandbox LAND prices differ based on unit of account, affecting investment returns.
problem Investment returns vary based on how prices are denominated.
method Analyzed over 71,000 transactions to compare different units of account.
result Users are willing to pay more in SAND and less in wETH for transactions.
Research predicts cryptocurrency staking rewards with high accuracy.
problem Predicting cryptocurrency staking rewards.
method Two predictive methodologies: sliding-window average and linear regression models.
result ETH staking rewards can be forecasted with RMSE within 0.7% and 1.1% of the mean value for 1-day and 7-day look-aheads respectively.
This paper explores leverage staking with stETH, revealing high returns but also significant risks.
problem Leverage staking introduces risks through intensified selling pressure and cascading liquidations.
method Formal framework for leverage staking, stress tests under extreme conditions of stETH devaluation.
result Leverage staking amplifies risks, leading to intensified selling pressure and price declines.
dYdX updates liquidity provider incentives to enhance trading efficiency.
problem Incentivizing liquidity providers to maintain efficient market structures.
method Analyzed various metrics (makerVolume, depths, spreads) and used historical trades to update the LP Incentives Programme.
result Updated the LP Incentives Programme to encourage more active and efficient liquidity.
DeFi lending protocols faced challenges during Ethereum's merge, but avoided major liquidations.
problem Ethereum's merge caused volatility and potential liquidations in DeFi lending.
method Analyzed AAVE and Compound lending protocols during the merge and hard fork.
result Borrowing rates spiked but no significant liquidations occurred.
Study evaluates cryptocurrency option pricing models, finds Kou and Bates models perform best.
problem High volatility and low liquidity in cryptocurrency futures contracts make traditional option pricing models unreliable.
method Calibrated and evaluated the performance of six option pricing models (Black-Scholes, Merton Jump Diffusion, Variance Gamma, Kou, Heston, and Bates) on BTC and ETH futures options.
result Kou and Bates models achieve the lowest pricing errors, with Kou outperforming Bates for BTC and ETH options respectively.
This is a set of expository lecture notes created originally for a graduate course on holomorphic curves taught at ETH Zurich and the Humboldt University Berlin in 2009/2010. The notes are still incomplete, but due to recent requests from readers, I've decided to make a presentable half-finished version available here.…
This paper optimizes perpetual contract liquidity by accounting for funding rates.
problem Optimal liquidity provision for perpetual contracts with stochastic funding rates.
method Formulated a control problem, solved with a HJB scheme, and calibrated on real data.
result Funding-aware market making improves performance and reduces inventory risk.
This paper examines autocorrelation in major crypto markets, finding persistent correlations on short time frames.
problem Assessing the efficiency of major cryptocurrency markets through autocorrelation analysis.
method Pearson's autocorrelation coefficient, Ljung-Box test, rolling window analysis.
result Persistent autocorrelation on 5m and 1H time frames, disagreement on 1D and 1W time frames.
On 2 November 2009, the Financial Bubble Experiment was launched within the Financial Crisis Observatory (FCO) at ETH Zurich (\url{http://www.er.ethz.ch/fco/}). In that initial report, we diagnosed and announced three bubbles on three different assets. In this latest release of 23 December 2009 in this ongoing experime…
ETF on CRIX reduces crypto risk and diversifies growth.
problem High volatility in cryptocurrencies makes them risky investments.
method Dynamic ETF construction on CRIX, considering fees, spreads, and rebalancing.
result ETF remains robust in core, low trading costs, increased liquidity.
Study shows monetary policy impacts digital assets like BTC and ETH.
problem Impact of monetary policy on digital assets and DeFi.
method Event study regressions and high-frequency price analysis.
result Monetary policy surprises negatively affect BTC and ETH but not other digital assets.
Study examines trading costs on Uniswap, finding adversarial slippage is significant for large trades and certain assets.
problem Analyzing costs and slippage in decentralized exchanges (DEXs).
method Empirical evaluation of Uniswap's USDC-ETH and PEPE-ETH pools, calculating slippage and reordering slippage.
result Adversarial slippage is significant for large trades and certain assets like PEPE.
This study examines asymmetric cross-correlations in cryptocurrency markets using fractal analysis.
problem Exploring asymmetric multifractal cross-correlations in cryptocurrency markets.
method Fractal analysis and MF-ADCCA method to investigate asymmetric volatility dynamics.
result Cross-correlations are stronger in downtrend markets than in uptrend markets for maturing BTC and ETH.
Study detects unusual trading patterns on crypto exchanges using complexity measures.
problem Detecting artificial trading activity on cryptocurrency exchanges.
method Complexity and statistical-structure measures derived from high-frequency trade-level data.
result Unusual trading patterns detected on Bitget for BTC and ETH after mid-May 2025.
These notes represent a much expanded and updated version of the \textquotedblleft mini course\textquotedblright that the author gave at the ETH (Zürich) and the University of Zürich in February of 1995. The purpose of these notes is to first provide some basic background to Riemannian geometry and stochastic calculus …
This paper discusses the dynamics of intraday prices of twelve cryptocurrencies during last months' boom and bust. The importance of this study lies on the extended coverage of the cryptoworld, accounting for more than 90\% of the total daily turnover. By using the complexity-entropy causality plane, we could discrimin…
A hybrid Convolutional VAE predicts crypto volatility surfaces, outperforming single-symbol approaches.
problem Predicting crypto volatility surfaces
method Convolutional VAE with hybrid predictor
result Model achieves 0.94-1.56 vol-point RMSE across BTC and ETH markets
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.
New method certifies images against transformations like rotations and translations.
problem Certifying robustness of images against transformations like rotations and translations.
method Randomized smoothing with three different kinds of defenses.
result Individual certificates can be obtained via statistical error bounds or efficient online inverse computation.
We consider the L2 gradient flow for the Willmore functional in Riemannian manifolds of bounded geometry. In the euclidean case E.\;Kuwert and R.\;Schätzle [\textsl{Gradient flow for the Willmore functional,} Comm. Anal. Geom., 10: 307-339, 2002] established a lower bound of a smooth solution of such a flow, which d…
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.
FinBERT-BiLSTM predicts cryptocurrency prices using sentiment analysis.
problem Predicting volatile cryptocurrency market prices.
method Hybrid model combining Bi-LSTM and FinBERT for sentiment analysis.
result Enhanced forecasting accuracy for volatile financial markets.
CoSE models complex drawings by treating strokes as a collection that can be composed.
problem Modeling complex free-form structures like diagrams.
method Generative model using autoencoder to project strokes into a fixed latent space, relational model operates in latent space.
result Model captures both individual strokes and their compositional structure.
Information transfer between time series is calculated by using the asymmetric information-theoretic measure known as transfer entropy. Geweke's autoregressive formulation of Granger causality is used to find linear transfer entropy, and Schreiber's general, non-parametric, information-theoretic formulation is used to …
Ethereum transition to PoS reduces energy consumption and decentralizes the network.
problem Transitioning from proof-of-work to proof-of-stake to reduce energy consumption and decentralize the network.
method Analyzed the impact of the Ethereum transition to proof-of-stake on network performance, competing platforms, and transaction fees.
result The transition to PoS has reduced energy consumption by 99.98% and decreased network concentration.
The thesis introduces methods to use semantic hierarchy in image classification.
problem Limited work in training image classifiers with non-conventional external guidance.
method Injects label hierarchy knowledge into arbitrary classifiers and uses order-preserving embeddings for image classification.
result Both embedding-based models and CNN-classifiers with hierarchical information outperform a hierarchy-agnostic model.
The paper develops a new framework for pricing and hedging liquidity in crypto markets.
problem Arbitrage and risk management in crypto market making.
method Developed a new mathematical framework using a coordinate system defined by price and intrinsic liquidity.
result Established a linear dependence of asset reserves and value functions on intrinsic liquidity, facilitating arbitrage-free pricing and delta hedging.
Study finds on-chain data can proxy off-chain cryptocurrency pricing.
problem Develop methods to proxy off-chain cryptocurrency pricing using on-chain data.
method Graphical models, mutual information, and ensemble machine learning.
result A significant amount of pricing information is contained in on-chain data, but precise prices are hard to recover except on short time scales.
The paper investigates cyclic arbitrage opportunities in decentralized exchanges.
problem Price discrepancies in decentralized exchanges lead to arbitrage opportunities.
method Theoretical framework and analysis of transaction-level data.
result Traders have executed over 292,606 cyclic arbitrages over eleven months, exploiting more than 138 million USD in revenue.
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.
Hybrid engine analyzes news sentiment for markets in real-time.
problem Real-time market analysis of news sentiment.
method Three-way ensemble learning combining financial lexicon, adaptive TF-IDF clustering, and auto-calibrated weighting.
result Adaptive statistical clustering learner improves adaptability to market changes.
Conventional financial models fail to explain the economic and monetary properties of cryptocurrencies due to the latter's dual nature: their usage as financial assets on the one side and their tight connection to the underlying blockchain structure on the other. In an effort to examine both components via a unified ap…
Study analyzes how COVID-19 impacts crypto and stock market volatility.
problem Impact of COVID-19 on cryptocurrency and stock market volatility.
method Two-stage multivariate EGARCH model with DCC approach, VaR and CFVaR.
result Significant spillover effects and conditional volatility surges after shocks.
Optimal rebalancing strategy improves AMM pool performance by 25%.
problem Optimizing the sequence of weights in dynamic AMM pools to minimize rebalancing costs.
method Using optimal interpolation and a cheap-to-compute approximation to achieve nearly optimal rebalancing.
result Approximately-optimal weight changes lead to significant increases in pool performance (up to 25%) under various conditions.
This study analyzes cryptocurrency market dynamics using a novel q-dependent detrended cross-correlation method.
problem Capturing correlations at varying fluctuation amplitudes and time scales in complex systems.
method Extends traditional metrics with q-dependent detrended cross-correlation coefficient ρ(q,s) and qMSTs. result Significant shifts in network structures during major disruptions, leading to decentralized correlations.
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.
Backtesting framework for CLMMs on Uniswap V3 reduces reward estimation error.
problem Estimating rewards for CLMMs in Uniswap V3 liquidity pools.
method Parametric model for liquidity distribution, historical data analysis.
result Error in reward estimation less than 1% for each pool.
Traditional nearest points methods use all the samples in an image set to construct a single convex or affine hull model for classification. However, strong artificial features and noisy data may be generated from combinations of training samples when significant intra-class variations and/or noise occur in the image s…