Modeling bank defaults through hitting thresholds to study systemic risk.
problem Understanding systemic risk in a network of interconnected banks.
method An interacting particle system to model bank defaults and analyze the resulting losses.
result Characterization of discontinuities in the cumulative loss process as systemic events.
Green stocks show less factor exposure heterogeneity compared to brown stocks.
problem Exploring differences in factor exposure between green and brown stocks.
method Examined S&P 500 firms grouped by greenhouse gas emissions, analyzing factor exposure over 2014-2020.
result Green stocks have less factor exposure heterogeneity than brown stocks, except for the value factor.
Deep learning approximates Bermudan option exposures and future values.
problem Computing accurate expected and future exposures for high-dimensional Bermudan options.
method Neural network-based approach combining Deep Optimal Stopping and regression.
result Neural network approximations of pathwise option values are more accurate.
Study shows short exposure and systematic risk exposure affect disposition effect asymmetries.
problem Understanding disposition effect in short vs long exposure positions and systematic risk.
method Generalized Odean measures, introduced Value metric, implemented dispositionEffect R package.
result Short positions exhibit weaker disposition effect than long positions under narrow framing, reversing in integrated framing.
Randomized neural networks improve exposure and CVA estimation for American options.
problem Estimation of exposure and CVA for American options
method Randomized neural networks
result Improves convergence and efficiency in high-dimensional problems
Study finds exposure bias distortion is limited and not incremental in open-ended text generation.
problem Exposure bias in auto-regressive language models causing incremental distortion.
method Proposed metrics to quantify exposure bias impact, used ground-truth prefixes instead of model-generated prefixes.
result Exposure bias distortion is limited and not incremental during generation.
Proposes a method to correct exposure misclassification bias in Cox models.
problem Challenges in estimating exposure-outcome associations with misclassified exposure data.
method An estimating equation method to correct for exposure misclassification-caused bias.
result Proposed method corrects bias in estimating PM2.5 level's association with lung cancer mortality.
New method calculates credit exposures for complex options efficiently.
problem Efficient calculation of credit exposures for complex options.
method Dynamic Chebyshev method for closed-form approximation.
result Highly efficient evaluation of credit exposures for large paths.
Valuation of Credit Valuation Adjustment (CVA) has become an important field as its calculation is required in Basel III, issued in 2010, in the wake of the credit crisis. Exposure, which is defined as the potential future loss of a default event without any recovery, is one of the key elementsfor pricing CVA. This pap…
Faster method for calculating credit exposure of financial options.
problem Accurate and fast calculation of credit exposure for financial options.
method Dynamic programming with function approximation to solve a dynamic programming problem.
result The method delivers accurate expected exposure and potential future exposure profiles faster than regression-based methods.
Paper optimizes neural networks for Bermudan option pricing with faster convergence and risk management tools.
problem Efficiently pricing Bermudan options with static hedging and risk management.
method Monte-Carlo-based artificial neural network framework with novel optimisation algorithm.
result The proposed neural network accelerates convergence and provides improved risk management tools.
We study the impact of central clearing of over-the-counter (OTC) transactions on counterparty exposures in a market with OTC transactions across several asset classes with heterogeneous characteristics. The impact of introducing a central counterparty (CCP) on expected interdealer exposure is determined by the tradeof…
The paper proposes using function approximations to reduce the computational burden in measuring counterparty credit exposure.
problem The need for regular exposure calculations in finance, balancing between computational cost and risk simplification.
method Replacing derivative pricers with function approximations, proving error bounds, and using Chebyshev interpolation for convergence.
result Derives probabilistic and finite sample error bounds, showing significant run-time reductions and asymptotic efficiency gains.
Study prenatal PM2.5 exposure and 4th grade reading scores, identifying critical windows of susceptibility.
problem Understanding the impact of prenatal PM2.5 exposure on educational outcomes.
method Developed a locally adaptive Bayesian regression model with B-spline basis expansion and dynamic shrinkage priors.
result Prenatal PM2.5 exposure during early and late pregnancy is most adverse for 4th grade reading scores.
Mack's estimator improves chain ladder prediction for large exposure insurance models.
problem Uncertainty quantification in compound Poisson loss models.
method Large exposure asymptotics applied to Mack's estimator.
result Chain ladder prediction uncertainty can be quantified without model assumptions.
Study estimates personalized effects of maternal PM2.5 exposure on birth weight.
problem Identify critical windows and heterogeneity in maternal PM2.5 exposure effects on birth weight.
method Heterogeneous Distributed Lag Models and Bayesian Additive Regression Trees.
result Evidence of heterogeneity in PM2.5-birth weight relationship, with some dyads showing 3x larger decrease.
New method debiases selection bias in PU classification with exposure data.
problem Binary classification from positive and unlabeled data with selection bias.
method Automatic Debiased PUE (ADPUE) learning method.
result ADPUE outperforms traditional PU learning methods on various datasets.
Collaborative filtering analyzes user preferences for items (e.g., books, movies, restaurants, academic papers) by exploiting the similarity patterns across users. In implicit feedback settings, all the items, including the ones that a user did not consume, are taken into consideration. But this assumption does not acc…
Modeling incentives for content creators on algorithm-curated platforms.
problem Maximizing exposure for content creators on algorithmic platforms.
method Formalized exposure game model, proving effects of algorithmic choices on equilibria, proposing tools for finding equilibria.
result Algorithmic choices significantly affect content exposure and creator behavior.
The study finds that asset correlations underestimated when exposure pools are not homogeneous.
problem Systematic error in estimating asset correlations from default data due to exposure pool inhomogeneity.
method Investigates the effect of exposure pool homogeneity on asset correlation estimation from default time series.
result Asset correlation is systematically underestimated when exposure pools are inhomogeneous, especially if PD is spread out.
BN^2MF identifies unknown exposure patterns in environmental mixtures.
problem Identifying unknown exposure patterns in environmental mixtures.
method Bayesian non-parametric non-negative matrix factorization (BN^2MF) with non-negative continuous priors and a non-parametric sparse prior.
result Estimates patterns of chemical exposures without specifying the number of patterns.
Combines CPPI and option-based strategy to ensure equity exposure.
problem Cash-in risk and equity market participation in CPPI.
method Two-step strategy: CPPI with guaranteed minimum equity exposure.
result Shows effectiveness through numerical analysis of option prices.
Researchers develop a new method to assess variable importance in spatial machine learning models for air pollution exposure prediction.
problem Understanding the mechanism captured by machine learning models in air pollution studies, especially with spatial correlation.
method Leave-one-out approach for variable importance measure applicable to models with separable mean and covariance components.
result The new method highlights differences in model mechanisms even for similar prediction accuracies.
Bayesian method detects outliers and uncertain points in data.
problem Detecting outliers and uncertain points in data using Bayesian methods.
method Generative model of data curation for aleatoric uncertainty, combining with epistemic uncertainty and outlier exposure.
result Principled Bayesian approach outperforms methods using aleatoric or epistemic uncertainty alone.
CENNSurv models cumulative effects of time-dependent exposures on survival outcomes.
problem Challenges in modeling cumulative effects of time-dependent exposures on survival outcomes.
method CENNSurv, a novel deep learning approach that captures dynamic risk relationships from time-dependent data.
result CENNSurv reveals multi-year lagged and short-term behavioral shifts in survival outcomes.
The study tests a functional-form restriction on risk exposure dynamics using margin debt data.
problem Understanding risk exposure dynamics under capital constraints and slack.
method Testing a regime-conditional functional-form restriction on aggregate risk-exposure dynamics implied by VaR-constrained intermediary models.
result The contraction and growth of exposures under capital constraints and slack are observed and tested.
Bayesian approach models nonignorable missing data using copulas and marginal quantiles.
problem Nonignorable missing data in lead exposure and test score analysis.
method Gaussian copula model with auxiliary marginal quantiles for missingness indicators and study variables.
result Efficient MCMC algorithm estimates copula correlation and marginal distributions consistently.
A new method uses counterfactual learning to improve recommendation system evaluation.
problem Inconsistent results in recommender systems due to exposure mechanisms.
method Proposes a minimax empirical risk formulation with an adversarial game to account for exposure.
result Shows improved learning bounds and effectiveness over various recommendation settings.
Improves anomaly detection in deep learning with an auxiliary dataset of outliers.
problem Detecting anomalous inputs in complex, large-scale deep learning models.
method Outlier Exposure (OE) approach: training anomaly detectors against an auxiliary dataset of outliers.
result Significantly improves detection performance on natural language processing and vision tasks.
The paper clarifies long-horizon investment and DCA, showing no risk reduction but different exposure profiles.
problem Misleading claims about reducing risk with longer investment horizons and DCA.
method Unified probabilistic framework, defining risk and uncertainty, and introducing effective investment exposure.
result Different investment timing strategies can lead to distinct exposure profiles over time, affecting risk and uncertainty.
Speeds up complex portfolio exposure calculations.
problem Calculating exposure of portfolios with exotic derivatives.
method Least Squares Monte Carlo (LSMC) technique.
result Significantly reduces computation time for nested Monte Carlo.
TradeMech nets trades without changing counterparty relationships.
problem Netting trades without altering counterparty exposure in complex financial networks.
method Transforms contracts into chains and cycles, nets designated object multilaterally, and replaces contracts with new multiparty agreements.
result Maximal multilateral netting of a designated object while preserving each agent's profit and counterparty risk.
The study addresses exposure bias in generative models, proposing unconditional generation as a solution.
problem Exposure bias in autoregressive generative models using ground-truth contexts at training and generated ones at test.
method Combining latent variable modeling with reinforcement learning exploration, the study proposes unconditional generation as a benchmark for generalization.
result The model demonstrates improved generalization capability on language modeling and variational sentence auto-encoding tasks.
OpFlow predicts robust OD flows by learning choice potentials conditioned on spatial exposures.
problem Deep models trained on raw counts are vulnerable to distribution shift.
method OpFlow learns row-centered choice potentials and reconstructs flows by combining them with a calibrated origin scale.
result OpFlow improves robustness under environment shifts, as shown by controlled synthetic shifts and a real-world experiment.
Bayesian method improves star location and flux estimation from coadded images.
problem Statistical analysis of coadded astronomical images is complicated by pixel dependence.
method Bayesian approach that implicitly marginalizes single-exposure pixel intensities.
result Method outperforms single-exposure image training for star parameter estimation.
The study models mortgage prepayment risk, accounting for behavioral uncertainty, and provides replication strategies.
problem Modeling and replicating the prepayment option of mortgages with behavioral uncertainty.
method Modeling behavioral uncertainty as a non-hedgeable risk factor, proving its impact on exposure value, and using IRSs and swaptions for replication.
result Including behavioral uncertainty reduces the exposure's value, and swaptions are necessary for optimal replication.
Unified framework for estimating indirect effects in observational studies with unmeasured confounding.
problem Challenges in evaluating indirect effects due to unmeasured confounding and unethical exposures.
method Developed a unified identification and estimation framework using proximal causal inference.
result Unified identification and estimation of PIIE and causal effect of an intervening variable in settings with pervasive unmeasured confounding.
Boundary-induced apparent risk aversion in non-ergodic growth models.
problem Risk aversion in multiplicative growth systems with absorbing boundaries.
method Exact lattice propagation and analysis of binary multiplicative processes.
result Optimal exposure is compressed near absorbing boundaries, mimicking risk aversion.
Sparse grids reduce xVA exposure evaluations by up to 6000 times.
problem Efficiently computing exposures for xVA in large portfolios with many risk factors.
method Sparse Grid Method combined with Stochastic Collocation and Smolyak's extension.
result Significant reduction in the number of portfolio evaluations, up to 6000 times.
Counterparty Risk FAQ: Credit VaR, PFE, CVA, DVA, Closeout, Netting, Collateral, Re-hypothecation, WWR, Basel, Funding, CCDS and Margin Lendingq-fin.PR We present a dialogue on Counterparty Credit Risk touching on Credit Value at Risk (Credit VaR), Potential Future Exposure (PFE), Expected Exposure (EE), Expected Positive Exposure (EPE), Credit Valuation Adjustment (CVA), Debit Valuation Adjustment (DVA), DVA Hedging, Closeout conventions, Netting clauses, Collateral …
Paper proposes a new measure for systemic credit concentration risk.
problem Systemic credit concentration risk not fully captured by single institution measures.
method Network model to describe overlapping portfolios and quantify systemic risk.
result Network metric quantifies systemic risk not reflected in single portfolio measures.
New method analyzes complex multivariate pathways in high-dimensional data.
problem High-dimensional mediation analysis of multivariate exposures, mediators, and outcomes.
method Simultaneous variable selection, indirect effect matrix estimation, and prediction of multivariate outcomes.
result Identifies biologically interpretable genetic-neural-cognitive pathways.
Paper develops consistent estimation of propensity scores for rare exposures.
problem Estimation of propensity score functions for rare exposures in oversampled cohorts.
method Flexible computational implementation using source population probability of exposure and observation weighting.
result Low empirical bias and variance for consistent propensity score function estimators.
The paper tackles robust design selection for online experiments under uncertain interference mechanisms.
problem Designing experiments in ads, recommendations, and member-experience systems when interference mechanisms are unknown.
method Formulates the problem as robust design selection over uncertain exposure mechanisms. Compares designs by worst-case planning risk over an ambiguity set combining various factors.
result Develops a geometry-aware guarantee and robust selector theorem with excess-risk control, exact recovery under separation, and certified shortlists when the risk surface is flat.
The paper examines how banks' exposure to similar risks and direct interbank connections can lead to financial contagion and instability.
problem Financial contagion and instability in interconnected bank networks.
method Analyzes a random network of banks connected through the interbank market, considering different levels of network connectivity and diversification levels.
result Optimal diversification levels for banks' exposure to similar risks are not universally optimal across all levels of network connectivity.
EGMU optimizes portfolios using KL divergence, ensuring positive solutions.
problem Constructing multi-factor target-exposure portfolios efficiently and accurately.
method Convex optimization framework minimizing KL divergence, with explicit solvers.
result Established feasibility and uniqueness of strictly positive solutions under convex-hull conditions.
Method uses derivatives for dynamic index tracking and risk control.
problem Dynamic index tracking and risk exposure control using financial derivatives.
method Continuous-time diffusion framework, pathwise approach to construct dynamic portfolios of derivatives.
result Established a general tracking condition and derived a slippage process.
We present an approach to derivative exposure management based on subjective and implied probabilities. We suggest to maximize the valuation difference subject to risk constraints and propose a class of risk measures derived from the subjective distribution. We illustrate this process with specific examples for the two…