A method estimates causal parameters using a latent variable recovery.
problem Estimating causal parameters in contexts with multiple causes and unobserved confounding.
method Substitute adjustment via recovery of latent variables.
result Substitute adjustment estimates adjusted regression parameters under certain conditions.
The method of covariate adjustment is often used for estimation of population average treatment effects in observational studies. Graphical rules for determining all valid covariate adjustment sets from an assumed causal graphical model are well known. Restricting attention to causal linear models, a recent article der…
Improved ARMA-GARCH model for illiquid assets like cryptocurrencies.
problem Inadequate modeling of illiquid assets, especially cryptocurrencies, with traditional ARMA-GARCH models.
method Introducing liquidity-adjusted liquidity jump and diffusion metrics into ARMA-GARCH framework.
result The liquidity-adjusted model improves model fit and volatility sensitivity for cryptocurrencies.
This article provides a new representation for pricing adjustments in derivatives.
problem Derivative pricing adjustments and XVA (Expected Value of All Risk) models.
method An Ito SDE/parabolic PDE framework to encapsulate pricing adjustments.
result A new representation that encompasses various past adjustments.
Improved estimator reduces bias in statistical learning models.
problem Asymptotic bias in classic WDRO estimator.
method Adjusted Wasserstein distributionally robust estimator.
result Asymptotic unbiased estimator with smaller MSE.
Model predicts risk-adjusted returns across various financial markets.
problem Stationary models fail in predicting risk-adjusted returns due to market regime changes.
method Asset-independent regime-switching model using hidden Markov models.
result Accurately detects bull, bear, and high volatility periods for improved risk-adjusted returns.
Efficient adjustment sets found for cost-minimized causal estimations.
problem Estimating interventional means with minimum cost in causal graphical models.
method Defined cost-adjustment sets, constructed flow networks, and used maximum flow algorithms.
result Minimum cost optimal adjustment sets exist and can be found efficiently.
Risk adjustment has become an increasingly important tool in healthcare. It has been extensively applied to payment adjustment for health plans to reflect the expected cost of providing coverage for members. Risk adjustment models are typically estimated using linear regression, which does not fully exploit the informa…
The MAXFLAT low-pass filter improves factor adjustment for better portfolio performance in China's stock market.
problem Improving factor adjustment for better portfolio performance in China's stock market.
method Using MAXFLAT low-pass volatility model to adjust factors and construct portfolios.
result Adjusted factors by MAXFLAT volatility model show better performance in both large and small cap universes.
Study optimal adjustment sets for causal policies with hidden variables.
problem Estimating dynamic treatment regimes with hidden variables.
method Developed criteria for graphs without hidden variables to compare estimators, extended to dynamic policies and hidden variables.
result Existence and computation of optimal minimal and globally optimal adjustment sets.
New theory connects non-abelian bundle gerbes to abelian ones.
problem Challenges in extending higher gauge theory beyond fake-flat sector.
method Developed a comprehensive theory of adjusted connections on non-abelian bundle gerbes.
result Established a new coordinate-independent formulation of lifting theorem.
Develops a method to approximate convexity adjustments for interest rate products.
problem Finding accurate convexity adjustments for interest rate products.
method Uses Malliavin calculus to develop an approximation method.
result Excellent numerical accuracy of the formulas for various interest rate products.
Causality-aware methods outperform linear residualization in confounding adjustment for anticausal prediction.
problem Adjusting for confounding in anticausal prediction tasks.
method Causality-aware counterfactual confounding adjustment.
result Causality-aware methods asymptotically outperform linear residualization in predictive performance.
New method estimates treatment effects from high dimensional data.
problem Estimating treatment effects from high dimensional data with confounders.
method Generative modeling approach to backdoor adjustment in variational inference.
result Empirically, estimates interventional likelihood in high dimensional settings.
This paper addresses recalibration issues in hedging callable assets, proposing a new risk-adjusted approach.
problem The mismatch between dynamic hedging theory and practice due to daily recalibration.
method Extends HVA model risk approach to callable assets, focusing on recalibration and model risks.
result Model risk reserves adjusted for exercise decisions may significantly exceed basic valuation differences.
We analyze the counterparty risk embedded in CDS contracts, in presence of a bilateral margin agreement. First, we investigate the pricing of collateralized counterparty risk and we derive the bilateral Credit Valuation Adjustment (CVA), unilateral Credit Valuation Adjustment (UCVA) and Debt Valuation Adjustment (DVA).…
Develops a new model to better estimate cryptocurrency and stock volatility.
problem Misrepresentation of volatility and co-movement in traditional models.
method Introduces liquidity-sensitive multivariate volatility framework with novel liquidity measures.
result Liquidity-adjusted models yield more stable and interpretable risk structures.
Confounding bias, missing data, and selection bias are three common obstacles to valid causal inference in the data sciences. Covariate adjustment is the most pervasive technique for recovering casual effects from confounding bias. In this paper, we introduce a covariate adjustment formulation for controlling confoundi…
We consider the bridge linear regression modeling, which can produce a sparse or non-sparse model. A crucial point in the model building process is the selection of adjusted parameters including a regularization parameter and a tuning parameter in bridge regression models. The choice of the adjusted parameters can be v…
We study the problem of treatment effect estimation in randomized experiments with high-dimensional covariate information, and show that essentially any risk-consistent regression adjustment can be used to obtain efficient estimates of the average treatment effect. Our results considerably extend the range of settings …
Bayesian method improves clinical trial efficiency.
problem Increase treatment effect estimates in clinical trials.
method Combines prognostic covariate adjustment with a Bayesian framework.
result Substantial increase in statistical power with controlled type I error.
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.
Prognostic scores improve logistic regression analysis in RCTs with binary outcomes.
problem Non-collapsibility in logistic regression analysis of RCTs with binary endpoints.
method Prognostic score adjustment using AI predictions to address non-collapsibility.
result Prognostic score adjustment increases power or reduces sample size for estimating conditional odds ratios.
DOPE efficiently estimates ATE with complex covariates.
problem Efficient estimation of ATE from complex covariates.
method Proposed DOPE framework for efficient adjustment.
result DOPE retains efficiency even with highly predictive covariates.
A three-dimensional extension of the structural default model with firms' values driven by correlated diffusion processes is presented. Green's function based semi-analytical methods for solving the forward calibration problem and backward pricing problem are developed. These methods are used to analyze bilateral count…
Improves trial efficiency by adjusting for historical prognostic scores.
problem Reducing statistical uncertainty in randomized trial estimates.
method Linear covariate adjustment using a prognostic model trained on historical data.
result Prognostic covariate adjustment achieves minimum variance and reduces mean-squared error.
Neural network models improve ROC curve evaluation of biomarkers, focusing on age's role in physical activity-mortality association.
problem Improving biomarker evaluation using machine learning for complex relationships.
method Proposes neural network-based covariate-adjusted ROC modeling.
result Age has distinct effects on mortality outcomes when physical activity is measured as total activity time.
Investigates adjustments on Lie group crossed modules for gauge theory.
problem Existence and classification of adjustments on crossed modules of Lie groups.
method Differentiation/integration correspondence with infinitesimal adjustments; Lie algebra techniques.
result Infinitesimal adjustments exist if and only if the Kassel-Loday class lies in the image of the Chern-Weil homomorphism.
Research shows that information asymmetry affects how quickly companies adjust their capital structure and expected returns.
problem The relationship between capital structure adjustment speed and expected returns is influenced by information asymmetry.
method A hybrid data regression model was used to test the hypotheses based on data from 120 companies in the Tehran Stock Exchange.
result Information asymmetry positively affects the relationship between capital structure adjustment speed and expected returns.
We introduce the general arbitrage-free valuation framework for counterparty risk adjustments in presence of bilateral default risk, including default of the investor. We illustrate the symmetry in the valuation and show that the adjustment involves a long position in a put option plus a short position in a call option…
A new approach optimizes weights in DLP for better risk-adjusted performance.
problem Optimizing time-varying weights in Double Linear Policy (DLP) for better risk-adjusted performance.
method Stochastic Model Predictive Control (SMPC) framework to maximize risk-adjusted returns while enforcing constraints.
result Empirical results show improved risk-adjusted performance and drawdown control.
The study highlights the importance of Wrong-Way Risk in FVA calculations during financial market turmoil.
problem The relevance of Wrong-Way Risk in Funding Valuation Adjustments (FVA) during financial market uncertainty.
method The study examines the impact of various modelling choices, including default times and stochastic/deterministic funding spreads, on FVA calculations.
result WWR effects are non-negligible in FVA modelling from a risk-management perspective.
We describe principal 3-bundles with adjusted connections using Lie algebras and groupoids.
problem Describing principal 3-bundles with adjusted connections.
method Derived explicit forms of adjustment data for 3-term L∞-algebras, integrated action Lie 3-algebroids to Lie 3-groupoids, and used differential cohomology. result Explicit description of principal 3-bundles with adjusted connections in terms of differential cohomology.
New method corrects seasonal Arctic sea ice predictions with probabilistic models.
problem Systematic biases and errors in climate model forecasts of Arctic sea ice.
method Conditional Variational Autoencoder model to map observation distribution given biased model predictions.
result Probabilistic adjusted forecasts are better calibrated and have smaller errors.
Defines a new short rate model and convexity adjustment formulae.
problem Interest rate convexity in a Gaussian framework.
method Defines a short rate model driven by a Gaussian Volterra process and derives convexity adjustment formulae.
result Explicit formulae for convexity adjustment derived.
When the in-sample Sharpe ratio is obtained by optimizing over a k-dimensional parameter space, it is a biased estimator for what can be expected on unseen data (out-of-sample). We derive (1) an unbiased estimator adjusting for both sources of bias: noise fit and estimation error. We then show (2) how to use the adjust…
Automatically adjusts model size for continual Gaussian processes.
problem Determining optimal model size for incremental data.
method Automatic adjustment of model size during continual learning.
result Near-optimal performance across various datasets without hyperparameter tuning.
New methods for calculating credit valuation adjustment with reduced noise and faster computation.
problem High statistical noise in computing sensitivities of CVA due to non-differentiable default intensities.
method Ad hoc analytical estimators to overcome non-differentiability and finite differences.
result Low statistical noise and fast computation of sensitivities to market quotes.
The paper provides PAC bounds for estimating causal effects using covariate adjustment with a valid set.
problem Estimating causal effects in high-dimensional settings without randomized experiments.
method PAC learning perspective, valid adjustment set, $\eps$-Markov blanket, constraint-based algorithms.
result PAC-bounds the estimation error of covariate adjustment by a term exponential in the size of the adjustment set.
Formula adjusts steady-state models for control confounding.
problem Learning steady-state models from operational data can be flawed due to control confounding.
method Derives a formula to adjust for control confounding using structural dynamical causal models.
result Estimates a causal steady-state model from closed-loop operational data.
Paper tackles long-tailed labels in classification problems.
problem Imbalanced or long-tailed label distribution in real-world classification problems.
method Logit adjustment applied post-hoc or during training to encourage a large relative margin between rare and dominant labels.
result Unified and generalised techniques for coping with long-tailed labels, improving generalisation and performance.
Develops model-free methods for event history analysis and efficient covariate adjustment.
problem Estimating treatment effects while accounting for confounding and understanding event history.
method Model-free prediction techniques, Local Covariance Measure (LCM), Debiased Outcome-adapted Propensity Estimator (DOPE), Aalen Covariance Measure (ACM).
result Demonstrates the effectiveness and robustness of the proposed methods in various settings.
Proposes BA method for unbiased time series anomaly detection evaluation.
problem Anomalies in time series data are rare, making F1-score unreliable.
method Introduces Balanced Point Adjustment (BA) to address F1-score bias.
result BA provides fairer evaluation of time series anomaly detectors.
New risk measures adjust for tail risk inadequacies.
problem Tail risk inadequacy in classical risk measures.
method Developed a family of adjusted risk measures using target risk profiles.
result Analyzed and derived properties of adjusted risk measures.
Study improves MACD trading strategy with volume and price adjustments.
problem Signal lag and false signals in traditional MACD trading rules.
method Develops VP-MACD framework with sensitivity calibration.
result Proposed framework outperforms baseline MACD in profitability and risk-adjusted return.
Unified framework for calculating Shapley values with correlated features.
problem Inaccurate Shapley values due to multicollinearity.
method Unified framework with matrix formulation and mathematical proof.
result Shapley values become independent of feature correlations.
Optimizes treatment duration to maximize quality-adjusted lifetime.
problem Balancing risks and benefits in clinical decision making.
method Proposes a weighted estimating equation to adjust for confounding and informative censoring, and a nonparametric estimator for mean counterfactual quality-adjusted lifetime.
result Shows the optimal time for percutaneous endoscopic gastrostomy insertion in ALS patients.
A new method improves treatment effect inferences in RCTs by adjusting for covariates and heteroskedasticity.
problem Improving treatment effect inferences in RCTs with efficient and powerful methods.
method Weighted Prognostic Covariate Adjustment Method (Weighted PROCOVA) for heteroskedasticity.
result The method reduces variance, maintains Type I error rate, and increases test power for treatment effect.