arXiv research

The paper tackles robust design selection for online experiments under uncertain interference mechanisms.

Bayesian method detects outliers and uncertain points in data.

Online social networking sites are experimenting with the following crowd-powered procedure to reduce the spread of fake news and misinformation: whenever a user is exposed to a story through her feed, she can flag the story as misinformation and, if the story receives enough flags, it is sent to a trusted third party …

Investor optimizes worst-case portfolio in uncertain markets.

Green stocks show less factor exposure heterogeneity compared to brown stocks.

Novel convex risk measures aggregate multiple uncertain sources for insurance firms.

The paper explores how to handle uncertain evidence in probabilistic models.

New method calculates Shapley values for uncertain functions.

Deep learning approximates Bermudan option exposures and future values.

Study shows short exposure and systematic risk exposure affect disposition effect asymmetries.

Randomized neural networks improve exposure and CVA estimation for American options.

In this paper, within the framework of uncertainty theory, the valuation of equity warrants is investigated. Different from the methods of probability theory, the equity warrants pricing problem is solved by using the method of uncertain calculus. Based on the assumption that the firm price follows an uncertain differe…

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.

Paper develops a robust hedging framework to reduce market risk and uncertainty.

Quantum methods model uncertain volatility in financial markets.

New model predicts dynamic volatility in uncertain financial markets.

The target of this paper is to consider model the risky asset price on the financial market under the Knightian uncertainty, and pricing the ask and bid prices of the uncertain risk. We use the nonlinear analysis tool, i.e., G-frame work [26], to construct the model of the risky asset price and bid-ask pricing for the …

The paper calculates MES bounds for systemic risk contributions under uncertain dependence.

Paper optimizes neural networks for Bermudan option pricing with faster convergence and risk management tools.

Exposure bias has been regarded as a central problem for auto-regressive language models (LM). It claims that teacher forcing would cause the test-time generation to be incrementally distorted due to the training-generation discrepancy. Although a lot of algorithms have been proposed to avoid teacher forcing and theref…

Study calculates Bayes risk for semi-supervised learning with uncertain labels.

This paper provides an innovative perspective on the role of gold as a hedge and safe haven. We use a quantile-on-quantile regression approach to capture the dependence structure between gold returns and changes in uncertainty under different gold market conditions, while considering the nuances of uncertainty levels. …

In epidemiology, identifying the effect of exposure variables in relation to a time-to-event outcome is a classical research area of practical importance. Incorporating propensity score in the Cox regression model, as a measure to control for confounding, has certain advantages when outcome is rare. However, in situati…

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.

New algorithm for uncertain time series classification.

Study prenatal PM2.5 exposure and 4th grade reading scores, identifying critical windows of susceptibility.

Mack's estimator improves chain ladder prediction for large exposure insurance models.

Study estimates personalized effects of maternal PM2.5 exposure on birth weight.

We introduce a new method to calculate the credit exposure of Bermudan, discretely monitored barrier and European options. Core of the approach is the application of the dynamic Chebyshev method of Glau et al. (2019). The dynamic Chebyshev method delivers a closed form approximation of the option prices along the paths…

New algorithm for reinforcement learning in uncertain environments with unknown thresholds.

New method debiases selection bias in PU classification with exposure data.

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.

BN^2MF identifies unknown exposure patterns in environmental mixtures.

Researchers develop a new method to assess variable importance in spatial machine learning models for air pollution exposure prediction.

CENNSurv models cumulative effects of time-dependent exposures on survival outcomes.

A method for accurate pricing of multidimensional derivatives under uncertain volatility.

The paper addresses portfolio allocation with uncertain covariance matrices, finding a logarithmic risk dependence.

Bayesian approach models nonignorable missing data using copulas and marginal quantiles.

The study tests a functional-form restriction on risk exposure dynamics using margin debt data.

A new method uses counterfactual learning to improve recommendation system evaluation.

Speeds up complex portfolio exposure calculations.

Bayesian Gaussian process models handle uncertain data locations in PDE approximations.

The paper clarifies long-horizon investment and DCA, showing no risk reduction but different exposure profiles.

TradeMech nets trades without changing counterparty relationships.

Framework for games with uncertain parameters, ensuring no player can improve by changing strategy.