The paper proposes a machine learning method to estimate proxy CDS rates for illiquid counterparties.
problem Estimating counterparty default risks from illiquid CDS quotes for financial valuation and risk management.
method Constructing proxy CDS rates by associating illiquid counterparty liquid CDS Proxy using machine learning techniques.
result Some classifiers achieve highly satisfactory accuracy rates in constructing proxy CDS rates.
PEARL uses AI to replicate private equity performance with liquid assets.
problem Lack of access to private equity due to high costs and complexity.
method Combines AI with liquid assets, incorporating asymmetry for better performance.
result Model outperforms liquid proxies and aligns with private equity benchmarks.
This work addresses the problem of optimal pricing and hedging of a European option on an illiquid asset Z using two proxies: a liquid asset S and a liquid European option on another liquid asset Y. We assume that the S-hedge is dynamic while the Y-hedge is static. Using the indifference pricing approach we derive a HJ…
We study the problem of optimal pricing and hedging of a European option written on an illiquid asset Z using a set of proxies: a liquid asset S, and N liquid European options Pi, each written on a liquid asset Yi,i=1,N. We assume that the S-hedge is dynamic while the multi-name Y-hedge is static. Usin…
New data-driven HFT measures distinguish supply and demand strategies, improving price informativeness.
problem Lack of effective measures to distinguish between HFT supply and demand strategies.
method Trained machine learning models on proprietary HFT data and applied to public intraday data.
result Measures outperform conventional proxies and capture HFT's temporal dynamics.
Small trades show unexpected price impact after fee changes.
problem Anomaly in price impact for small transaction volumes post-fee restructuring.
method Reviewed existing master curve for price impact, re-estimated after fee reductions, found anomalies, and rescaled by liquidity proxy.
result Master curve for price impact can be approximated using a liquidity proxy, providing a practical method for practitioners.
New method identifies algo trading strategies as liquidity consumers or providers.
problem Determining if algo trading strategies consume or provide liquidity.
method Analyzes trade and price history to classify strategies as liquidity consumers or providers.
result Identifies net liquidity consumption or provision of algo trading strategies.
New framework replicates private equity performance using AI and liquid strategies.
problem Inadequate trust and transparency in private equity markets.
method Advanced graphical models and asymmetric risk adjustments.
result Liquid, scalable solution that closely mimics private equity performance.
We introduce a microscopic model for the dynamics of the order book to study how the lack of liquidity influences price fluctuations. We use the average density of the stored orders (granularity g) as a proxy for liquidity. This leads to a Price Impact Surface which depends on both volume ω and g. The dependence …
New measures detect HFT activity, revealing its impact on stock prices.
problem Lack of public data on HFT activity.
method Developed machine learning models to predict HFT activity using proprietary and public data.
result Measures outperform conventional proxies and reveal HFT's impact on price discovery.
New method detects market liquidity changes using order book data.
problem Detecting changes in market liquidity.
method Marked Hawkes processes and minimax quickest detection problem for doubly-stochastic Poisson process.
result Optimal stopping rule for detecting intensity changes in market liquidity.
Study measures subcritical branching in crypto liquidation cascades.
problem Understanding the mechanism of crypto liquidation cascades.
method Analyzed seven major crypto liquidation events, measured branching ratio, and compared with theoretical models.
result Found the cascades to be subcritical, with branching ratio around 0.1-0.2.
Simple model forecasts asset return distributions precisely.
problem Forecasting dynamic return distributions of assets.
method Ordered binary choice regression with sign predictability constraints.
result Precise forecasts of asset return distributions using past indicators and volatility proxies.
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.
The study extends SPT to account for real-world transaction costs, improving portfolio performance.
problem Real-world transaction costs affect portfolio performance, especially during market stress.
method Developed a continuous-time model with stochastic transaction costs and derived lower bounds for cost-adjusted wealth.
result Functionally generated portfolios can still achieve relative arbitrage after accounting for transaction costs.
Developing an Agent-Based Model to Mitigate Adverse Selection in Uniswap v3 Liquidity Providers
problem Adverse selection in Uniswap v3 liquidity providers
method Agent-Based Model incorporating blockchain microstructure and volatility dynamics
result Dynamic fee schedules improve hedged Profit and Loss for liquidity providers
Study analyzes risk management in Aave and Compound lending protocols, finding v3 better than v2.
problem Risk management in decentralized lending protocols.
method Cross-version and cross-chain analysis using fixed effects model.
result v3 protocols have better risk management, with stronger impact on L2 blockchains.
Estimates self- and cross-impact concavity and decay patterns in financial markets.
problem Understanding the impact of financial transactions on market dynamics.
method Nonparametric estimation of concave multi-asset propagator models using metaorders and order flow data.
result Concave self-impact with shifted power-law decay, significant gain from cross-impact, and improved predictive accuracy.
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.
The stochastic volatility model is one of volatility models which infer latent volatility of asset returns. The Bayesian inference of the stochastic volatility (SV) model is performed by the hybrid Monte Carlo (HMC) algorithm which is superior to other Markov Chain Monte Carlo methods in sampling volatility variables. …
A new framework improves VaR recalibration by balancing reliance on imperfect volatility proxies.
problem How to balance reliance on imperfect volatility proxies in one-sided VaR recalibration.
method Proxy-reliance control framework that interpolates between constant-shift and proxy-scaled corrections.
result Lower or intermediate proxy reliance can outperform fully proxy-scaled recalibration in stressed left-tail VaR control.
Develops a haircut model for non-cash collateral.
problem Addressing the need for accurate non-cash collateral valuation in shadow banking and OTC derivatives markets.
method Expands haircut definitions, uses a double-exponential jump-diffusion model, and solves for credit risk measurements.
result Computational results show potential for collateral agreements and regulatory capital calculations.
Study accelerates NAS research with a large dataset of ZC proxies.
problem Speeding up neural architecture search with ZC proxies.
method Created NAS-Bench-Suite, evaluated 13 ZC proxies across 28 tasks, and provided a unified codebase.
result ZC proxies capture substantial complementary information and can improve NAS algorithm performance.
Unified framework for analyzing neural networks trained by gradient descent.
problem Lack of generalizable guarantees for neural networks trained by gradient descent.
method Proxy convexity and proxy Polyak-Lojasiewicz inequalities.
result Unified guarantees for neural networks trained by gradient descent.
Short-term trend-following has stopped delivering profits since 2009, especially on smaller market ticks.
problem The profitability of short-term trend-following has declined since 2009.
method Cross-sectional analysis of 100 liquid futures contracts from 1995-2025, evaluating four explanations.
result The decline in short-term trend-following profits is linked to smaller market ticks, not asset class or liquidity.
Predicting delayed outcomes in recommender systems using proxies.
problem Predicting delayed outcomes in recommender systems.
method Formalized as adversarial, delayed online learning problem; proposed Factored Forecaster (FF) and Residual Factored Forecaster (RFF) neural network architectures.
result Residual Factored Forecaster (RFF) outperforms direct forecaster and Factored Forecaster (FF) in predicting human behavior.
Paper detects proxies in linear regression models causing discrimination.
problem Discrimination in machine learning models using proxies for protected attributes.
method Formulated a definition of proxy use, identified proxies via second-order cone program, and extended to justified business necessity.
result Proxies in linear regression models can be efficiently identified and removed to reduce discrimination.
Study semi-supervised learning with noisy proxy covariates, deriving bounds and showing gains.
problem Learning from noisy proxy covariates with scarce labels.
method Two-stage estimator learning kernel eigenfeatures from all proxy covariates and fitting a ridge predictor on labeled data.
result Finite sample bounds show fast labeled sample rates and consistent gains over supervised and semi-supervised baselines.
DFPV improves PCL for confounded bandit policy evaluation.
problem Estimating causal effects in confounded settings with high-dimensional data.
method Deep feature proxy variable method (DFPV) for high-dimensional, nonlinear relationships.
result DFPV outperforms state-of-the-art methods on synthetic benchmarks and confounded bandit problems.
Study risk-sensitive market making with entropy regularization for better quote control.
problem Risk-sensitive market making with exponential utility and penalties.
method Entropy-regularized certainty-equivalent Bellman policies for discrete-time market dynamics.
result Proves convergence and performance bounds for entropy-regularized policies.
A framework uses proxies to prioritize treatment without estimating causal effects.
problem Prioritizing treatment when causal effects are hard to estimate.
method Decision-focused framework identifying conditions for proxy usefulness.
result Proxies can recover correct effect ordering under specific conditions.
New method recovers latent confounders from high-dimensional proxy variables.
problem Detecting latent confounders from high-dimensional proxy variables.
method Proxy Confounder Factorization (PCF) framework using ICA-PCF and GD-PCF.
result ICA-PCF recovers confounders with high correlation and low error in synthetic and real-world data.
Single proxy variable helps estimate causal effects from confounders.
problem Estimating causal effects from treatment to outcome when unobserved confounders are present.
method Assumes a single, potentially multi-dimensional proxy variable of the unobserved confounder and a known mechanism generating the proxy from the confounder. Proves causal effects are identifiable under completeness assumption.
result Causal effects are identifiable under SPICE assumption.
A scalable method for deep metric learning using chance constraints.
problem Improving deep metric learning by addressing feasibility issues.
method Relating DML to chance constraints, reformulating as a feasibility problem, and iteratively training proxies.
result The method effectively improves deep metric learning performance across multiple benchmarks.
Develops methods to improve demand counterfactuals from imperfect proxies.
problem Imperfect proxies in demand models lead to biased counterfactuals and invalid inference.
method Practical toolkit for market-level and individual data, requiring minimal computation.
result Improves substitution prediction and counterfactual performance.
New method handles many noisy proxy controls for causal inference.
problem Causal inference with many noisy proxy controls and unknown confounders.
method Linear models with rank-restricted and sparse nuisance parameters, penalization methods.
result Estimators achieve better performance in high dimensions, especially with many proxies.
Optimizes liquidity provision intervals for profitable AMM participation.
problem Financial losses from poor liquidity provision intervals and reallocation costs.
method Developed a tractable stochastic optimization problem.
result Computes optimal liquidity provision intervals for profitable liquidity concentration.
proxymate diagnoses and corrects proxy estimates for reliable inference.
problem Systematic bias in proxy estimates can lead to invalid inference on primary outcomes.
method Four-level framework for proxy validation and adjustment, including diagnostic checks and targeted corrections.
result Facilitated quick decision making on thousands of experiments at Meta.
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.
We use smaller proxy datasets to speed up hyperparameter search.
problem Time-consuming and costly training of neural networks on large datasets.
method Generate smaller 'proxy datasets' by sampling, training on easy/hard examples, or generating synthetic data. Compare proxy quality using variance measures.
result Proxy datasets can explain up to 95% of the variance in full dataset results, significantly faster.
The study introduces new liquidity measures and models for assets with extreme liquidity.
problem Modeling assets with extreme liquidity, especially in crypto markets.
method Developed innovative liquidity premium measures, liquidity-adjusted return and volatility models, and used ARMA-GARCH/EGARCH models.
result The liquidity-adjusted models outperform traditional models in predicting asset performance at extreme liquidity.
Improves fairness without protected group labels by using proxy groups.
problem Lack of protected group labels makes it hard to improve fairness.
method Investigates improving fairness metrics for proxy groups and tests their effectiveness.
result Proxy fairness strategy works well in practice but depends on fairness metric choice.
The study analyzes how cross-chain interoperability affects decentralized lending protocols' performance.
problem Understudied cross-chain elements in DeFi lending risk management.
method Panel regression fixed effects and OLS models applied to empirical analysis.
result Cross-chain activity impacts protocol performance, with bridge volume being a critical driver.
New conditions show proxy data can improve policy learning in sparse expert data contexts.
problem Improving sample complexity of policy learning with sparse expert data.
method Sufficient conditions for proxy data to improve sample complexity, adaptable to LLMs.
result Provable improvement in sample complexity for policy learning with proxy data.
A data proxy reduces NAS computation cost by preserving key examples.
problem High computation cost in Neural Architecture Search (NAS).
method Measuring example importance based on relative accuracy ranking and creating a data proxy.
result Preserves almost same relative accuracy ranking with 10-20x smaller data proxy.
FR-LUX optimizes portfolio management by learning cost-aware policies robust to market conditions.
problem Transaction costs and regime shifts cause failure in live trading portfolios.
method Integrates three ingredients: microstructure-consistent execution model, trade-space trust region, and explicit regime conditioning.
result Achieves top average Sharpe ratio, maintains flat cost-performance slope, and superior risk-return efficiency.
A new probabilistic approach improves deep metric learning by considering image uncertainties and class-specific variances.
problem Proxy-based deep metric learning struggles with image uncertainties and class-specific structures.
method Introduces non-isotropic probabilistic proxy-based deep metric learning using directional von Mises-Fisher distributions.
result Improves generalization performance and competitive on standard benchmarks.
A liquidity measure based on consideration and price range is proposed. Initially defined for daily data, Liquidity Index (LIX) can also be estimated via intraday data by using a time scaling mechanism. The link between LIX and the liquidity measure based on weighted average bid-ask spread is established. Using this li…