Estimates cross-impact on derivatives markets using E-Mini futures and options.
problem Empirical estimation of cross-impact on complex financial instruments like derivatives.
method Modeling derivatives prices as a function of stochastic factors and trades on both factors and derivatives.
result Simple framework successfully captures cross-impact on derivatives markets.
Modeling trading costs for correlated instruments to improve execution strategies.
problem Incorrect estimation of liquidity and suboptimal execution strategies due to neglecting cross-impact effects.
method Extending the linear propagator model to the multivariate case for correlated instruments, calibrating a cost model free of arbitrage and manipulation.
result Synchronizing the execution of correlated contracts is crucial for accurate liquidity estimation and optimal execution strategies.
The study extends a framework to analyze cross-impact in multi-asset markets.
problem Analyzing cross-impact and no-dynamic-arbitrage in multi-asset markets.
method Deriving theoretical limits for cross-impact from the condition of absence of dynamical arbitrage, testing these constraints with data.
result Significant violations of cross-impact symmetry found, but not exploitable due to bid-ask spread.
Two models are identified for robust cross-impact analysis.
problem Developing and validating cross-impact models that fit data and are well-behaved.
method Classified cross-impact models according to desirable properties and evaluated them on three asset classes.
result Only one model satisfies all desirable properties and is suitable for applications.
Investigates cross-impact kernels for financial asset prices.
problem Understanding and parameterizing cross-impact kernels for financial asset prices.
method Examined martingale-admissible and no-statistical-arbitrage-admissible kernels, determined their overlap, and provided calibration formulas.
result Identified the overlap between martingale-admissible and no-statistical-arbitrage-admissible kernels and provided formulas for their calibration.
Optimizes trading pairs of stocks to reduce cross-impact costs.
problem Minimizing costs from trades of one stock affecting another.
method Develops a strategy to minimize cross-impacts by optimizing trading rates and periods.
result An optimal trading strategy for stock pairs is found.
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.
Study shows integrating OFI from multiple levels improves price impact explanation but not forecasting.
problem Explaining and forecasting price movements in equity markets using OFI.
method Systematic approach to combine OFIs from multiple levels into an integrated variable, testing multi-asset models with and without cross-impact terms.
result Lagged cross-asset OFIs improve future return forecasting but not contemporaneous price impact.
Optimal portfolio choice with cross-impact propagators, solving complex equations.
problem Maximizing revenue-risk in a continuous-time portfolio choice problem with cross-impact.
method Formulated as a maximization problem, solved explicitly using operator resolvents and stochastic Fredholm equations.
result Sufficient conditions for the absence of price manipulation, providing financial insights.
The study identifies features making cross-impact relevant in explaining price variance of US assets.
problem Understanding the relevance of cross-impact in explaining price variance of US assets.
method Using tick-by-tick data spanning 5 years for 500 US assets, the study investigates the features making cross-impact relevant.
result Price formation is endogenous within highly liquid assets, influencing less liquid correlated products with a constrained impact velocity.
Agent hedges non-tradable risk with traded asset, accounting for cross-impact and risk aversion.
problem Hedging non-tradable risks with transaction costs and price impact.
method Solving stochastic control problem to derive optimal hedging strategy.
result Closed-form expressions for optimal hedging strategies under different exposure conditions.
Revisits multivariate Kyle model, proving unicity of impact matrix.
problem Calibrating cross-impact matrices in market microstructure.
method Risk-neutral setting with a rational insider and market maker, proving unicity of impact matrix.
result Unicity of symmetric, positive definite solution for impact matrix.
Study optimizes trading in multiple assets with cross-effects.
problem Optimizing trade execution in multiple assets with cross-impact effects.
method Formulated as a stochastic control problem, extended to progressively measurable controls, solved using linear-quadratic control theory.
result Cross-hedging effects can be optimal, e.g., trading in an asset without an initial position.
We construct a price impact model between stocks in a correlated market. For the price change of a given stock induced by the short-run liquidity of this stock itself and of the information about other stocks, we introduce a self- and a cross-impact function of the time lag. We model the average cross-response function…
Model explains yield curve dynamics using order flow shocks.
problem Understanding the yield curve's fluctuations and their relation to order flows.
method Relates exogenous shocks to order flow surprises, creating a microstructural model that incorporates price and order flow dynamics.
result The model explains yield curve dynamics with fewer parameters and generates liquidity-dependent correlations.
Paper solves optimal portfolio deleveraging with cross asset impacts.
problem Maximize equity while meeting debt/equity requirement with cross asset price impacts.
method Developed successive convex optimization (SCO) and an effective global algorithm integrating SCO, convex relaxation, and branch-and-bound.
result Proposed algorithms find global optimal solutions efficiently.
Study shows portfolio trading impacts intraday liquidity and optimizes execution strategies.
problem Impact of portfolio trading on intraday liquidity and execution strategies.
method Stylized model capturing portfolio trading, linear cross-asset market impact, optimal execution schedule.
result Optimal execution schedule can reduce costs by up to 6% compared to separable VWAP-like approach.
The vast majority of market impact studies assess each product individually, and the interactions between the different order flows are disregarded. This strong approximation may lead to an underestimation of trading costs and possible contagion effects. Transactions in fact mediate a significant part of the correlatio…
We analyze small price impacts in a multidimensional utility maximization problem using PDEs.
problem Small nonlinear price impacts in a multidimensional utility maximization problem.
method Asymptotic expansion using nonlinear PDEs related to ergodic control and linear parabolic PDEs.
result Leading order correction to the value function is characterized by a nonlinear second order PDE.
Estimates price impacts and finds asymmetric market structures.
problem Understanding asymmetric price impacts in financial markets.
method Quantifies price impacts using spectral statistics and Shannon entropy.
result Asymmetric and non-random price impacts across the market.
Study examines Bitcoin market fragmentation and price formation, revealing market leader-lagger dynamics and trading strategies.
problem Understanding price formation in fragmented Bitcoin markets at sub-second time scales.
method Utilized granular orderbook and trades data, constructed features, and trained linear models to explain market returns.
result Fee regime determines market leadership and profitability of taker strategies, maker strategies tested in real-world trading.
The leverage effect weakly impacts return distributions, especially for small firms.
problem The leverage effect's impact on return distributions is inconsistent and puzzling.
method Analyzed the determinants of return distributions and proposed an indirect method to measure the interaction effect.
result The interaction effect between leverage and mean-reversion is weak and impacts return distributions mainly for small firms.
New method for interpreting non-linear models using forward marginal effects.
problem Interpreting non-linear models' feature effects is challenging.
method Introducing forward marginal effects and partitioning feature space for better interpretation.
result Improved interpretation of non-linear prediction functions.
The Kalinin effectivity is studied and applied to compactifications and Hilbert squares.
problem Understanding Kalinin effectivity in compactifications and its applications.
method Definition, construction methods, and analysis of Kalinin effectivity in various compactifications.
result Wonderful compactifications of hyperplane arrangements and configuration spaces are Kalinin effective.
New method estimates treatment effects in network data, accounting for spillover effects.
problem Treatment effect estimation in networks with spillover effects.
method Augmented inverse probability weighting (AIPW) with cross-fitting and machine learning.
result Semiparametric treatment effect estimator converges at parametric rate and follows Gaussian distribution.
Causalfe estimates treatment effects in panel data with fixed effects.
problem Spurious heterogeneity in treatment effect estimates due to fixed effects in panel data.
method CFFE approach with node-level residualization during tree construction.
result Validates the estimator's performance through simulation studies.
The paper clarifies the distinction between CATE and ITE under ignorability assumptions.
problem Confusion between CATE and ITE hinders personalized effect estimation.
method Clarifies the distinction between CATE and ITE under ignorability assumptions.
result CATE and ITE are not necessarily the same under ignorability assumptions.
GADGET framework decomposes global feature effects using recursive partitioning.
problem Misleading global feature effects when feature interactions are present.
method Generalized additive decomposition of global effects (GADGET) based on recursive partitioning.
result Minimizes interaction-related heterogeneity of local feature effects.
A new RL framework evaluates dynamic mediation effects over time.
problem Dynamic mediation effects in sequentially assigned treatments.
method Reinforcement Learning framework for decomposition and estimation of causal effects.
result Superior performance demonstrated through numerical studies and real data analysis.
The Zumbach effect is significant under rough Heston but negligible in classical Heston.
problem Identifying the Zumbach effect in stochastic volatility models.
method Explicit computations of the Zumbach effect under rough Heston model.
result The Zumbach effect is negligible in the classical Heston model but significant under rough Heston.
New method predicts drug side effects from combined use.
problem Predicting side effects from drug combinations.
method Multi-relational knowledge graph completion.
result State-of-the-art results on polypharmacy side effect prediction.
A new method purifies interaction effects in models to improve interpretability.
problem Interaction effects can be misinterpreted as separate main effects, complicating model interpretation.
method Proposes pure interaction effects and a Functional ANOVA decomposition algorithm to identify and isolate interaction effects.
result Identifies and separates interaction effects from main effects, showing large disparities in model interpretation.
New memory effect discovered in gravitational wave behavior.
problem Understanding gravitational wave behavior in spacetimes with angular momentum.
method Mathematical analysis of Minkowski spacetime and Kerr black holes.
result Angular momentum memory effect observed at future null infinity.
Decagon models polypharmacy side effects using graph convolutional networks.
problem Discovering polypharmacy side effects due to complex drug interactions.
method Developed a graph convolutional neural network for multirelational link prediction in multimodal networks.
result Accurately predicts polypharmacy side effects, outperforming baselines by up to 69%.
Effective Yau-Tian-Donaldson conjecture for spherical varieties.
problem Finding effective K-stability criteria for spherical varieties.
method Formulated an effective variant of the Yau-Tian-Donaldson conjecture and reviewed effective K-stability criteria for spherical varieties.
result Effective K-stability criteria can be computed given combinatorial data.
A new method estimates treatment effects in mixed groups, improving accuracy.
problem Estimating treatment effects in mixed groups with heterogeneous responses.
method PCM (pre-cluster and merge) approach for nonparametric estimation.
result Asymptotic consistency and significant improvement in accuracy over existing methods.
Combines observational and experimental data to identify heterogeneous treatment effects.
problem Underpowered A/B tests for heterogeneous treatment effects with high-dimensional covariates.
method Uses observational data to estimate unit-level effects, then applies to experimental data.
result Reduces power demands for detecting heterogeneous treatment effects.
Study estimates heterogeneous principal causal effects with binary treatments and intermediate variables.
problem Estimating subgroup effects within strata defined by potential values of an intermediate variable.
method Proposes a framework for estimating and forming confidence intervals for heterogeneous principal causal effects under principal ignorability assumption. Develops several estimators with varying robustness properties.
result Established large-sample theory and analyzed bias contributions of each approach.
Study develops method for estimating causal effects in continuous variables.
problem Lack of methods for estimating causal effects in continuous variables.
method Develops a method independent of data generating models for continuous variable interventions.
result Preserves identifiability of data and applies to any generating models.
ICA accurately estimates treatment effects even with confounders.
problem Estimating treatment effects in the presence of confounding variables.
method Uses Independent Component Analysis (ICA) to identify latent sources and estimate mixing coefficients.
result Linear ICA can consistently estimate multiple treatment effects, even with Gaussian confounders, and is more sample-efficient than Orthogonal Machine Learning (OML).
Method improves treatment effect estimation in randomized experiments.
problem Estimating distributional treatment effects in randomized experiments.
method Distributional regression framework with machine learning for variance reduction.
result The proposed method reduces variance of distributional treatment effect estimators.
New method discovers context effects in choice data.
problem Identifying context effects from choice data is challenging.
method Automatic discovery of context effects from observed choices.
result Automatic discovery of context effects from observed choices.
Dropout introduces both explicit and implicit regularization effects.
problem Understanding the full impact of dropout regularization.
method Disentangled explicit and implicit regularization effects through experiments and analytic simplifications.
result Explicit and implicit regularization effects of dropout are distinct and can be characterized analytically.
New test for binary treatment effects using kernel methods.
problem Testing distributional effects of binary treatments.
method Kernel-based doubly-robust test, avoiding permutations.
result Valid type-I error with computational efficiency.
New Krylov subspace methods speed up mixed-effects models with crossed random effects.
problem Slow computations for high-dimensional crossed random effects in mixed-effects models.
method Krylov subspace-based methods for generalized mixed-effects models with cross effects.
result Speedups by factors of up to 10,000 in computations for mixed-effects models.
Identifies causal effects in LiNGAM models with latent variables.
problem Identifying causal effects in LiNGAM models with latent confounders.
method Complete graphical characterization and efficient algorithms for certification. RICA adaptation for estimation.
result Efficient algorithms and RICA adaptation for estimating causal effects.
Develops HCQRF for estimating heterogeneous treatment effects with censored data.
problem Estimating heterogeneous treatment effects on censored responses with high-dimensional variables.
method Hybrid Censored Quantile Regression Forest (HCQRF) combining random forests and censored quantile regression.
result Demonstrates the effectiveness and stability of HCQRF through simulation studies and real-world application.
The paper studies how and when a treatment triggers different effects for individuals.
problem Estimating how treatment effects vary among individuals based on their characteristics.
method Tree-based learning method to find individual-level treatment triggers.
result The proposed method learns treatment triggers better than existing approaches.