arXiv research

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

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

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…

Paper tackles exposure bias in recommender systems using contrastive learning.

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

We introduce a new method to calculate the credit exposure of European and path-dependent options. The proposed method is able to calculate accurate expected exposure and potential future exposure profiles under the risk-neutral and the real-world measure. Key advantage of is that it delivers an accuracy comparable to …

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

It is important to detect anomalous inputs when deploying machine learning systems. The use of larger and more complex inputs in deep learning magnifies the difficulty of distinguishing between anomalous and in-distribution examples. At the same time, diverse image and text data are available in enormous quantities. We…

Paper uses neural networks to compress large portfolios of options, reducing risk and capital requirements.

Sparse grids reduce xVA exposure evaluations by up to 6000 times.

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

Decomposes spillover effects under misspecified exposure mappings.

We propose an interacting particle system to model the evolution of a system of banks with mutual exposures. In this model, a bank defaults when its normalized asset value hits a lower threshold, and its default causes instantaneous losses to other banks, possibly triggering a cascade of defaults. The strength of this …

Model A outperforms passive investment in stock index prediction with less exposure.

LLMs detect market patterns through causal reasoning, not just temporal association.

With the increased availability of large databases of electronic health records (EHRs) comes the chance of enhancing health risks screening. Most post-marketing detections of adverse drug reaction (ADR) rely on physicians' spontaneous reports, leading to under reporting. To take up this challenge, we develop a scalable…

Deep learning approximates Bermudan option exposures and future values.

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

We consider thin incomplete financial markets, where traders with heterogeneous preferences and risk exposures have motive to behave strategically regarding the demand schedules they submit, thereby impacting prices and allocations. We argue that traders relatively more exposed to market risk tend to submit more elasti…

Paper introduces a new method for efficient portfolio risk quantification.

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…

Proposes a fair RMAB framework ensuring equal exposure to arms.

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…

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…

Introduces a new system for modeling bank solvency contagion with heterogeneous impacts and exposures.

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.

The focus of this paper is the efficient computation of counterparty credit risk exposure on portfolio level. Here, the large number of risk factors rules out traditional PDE-based techniques and allows only a relatively small number of paths for nested Monte Carlo simulations, resulting in large variances of estimator…

Management of systemic risk in financial markets is traditionally associated with setting (higher) capital requirements for market participants. There are indications that while equity ratios have been increased massively since the financial crisis, systemic risk levels might not have lowered, but even increased. It ha…

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

A counterparty credit limit (CCL) is a limit that is imposed by a financial institution to cap its maximum possible exposure to a specified counterparty. CCLs help institutions to mitigate counterparty credit risk via selective diversification of their exposures. In this paper, we analyze how CCLs impact the prices tha…

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.

Bayesian method detects outliers and uncertain points in data.

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.

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

TradeMech nets trades without changing counterparty relationships.

Multi-label classification (MLC) assigns multiple labels to each sample. Prior studies show that MLC can be transformed to a sequence prediction problem with a recurrent neural network (RNN) decoder to model the label dependency. However, training a RNN decoder requires a predefined order of labels, which is not direct…

OpFlow predicts robust OD flows by learning choice potentials conditioned on spatial exposures.

Bayesian method improves star location and flux estimation from coadded images.

Measurement and management of credit concentration risk is critical for banks and relevant for micro-prudential requirements. While several methods exist for measuring credit concentration risk within institutions, the systemic effect of different institutions' exposures to the same counterparties has been less explore…