arXiv research

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

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

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 study aims to prevent unfair content presentation in recommender systems.

The paper studies and mitigates bias in recommender systems.

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

Systemic risk arises as a multi-layer network phenomenon. Layers represent direct financial exposures of various types, including interbank liabilities, derivative- or foreign exchange exposures. Another network layer of systemic risk emerges through common asset holdings of financial institutions. Strongly overlapping…

The study models mortgage prepayment risk, accounting for behavioral uncertainty, and provides replication strategies.

Study analyzes crypto asset risk exposures using a divide-and-conquer approach.

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

Unlike other industries in which intellectual property is patentable, the financial industry relies on trade secrecy to protect its business processes and methods, which can obscure critical financial risk exposures from regulators and the public. We develop methods for sharing and aggregating such risk exposures that …

Algorithm learns fair ranking from biased data.

Proposes a fair RMAB framework ensuring equal exposure to arms.

The literature of heavy tails (typically) starts with a random walk and finds mechanisms that lead to fat tails under aggregation. We follow the inverse route and show how starting with fat tails we get to thin-tails when deriving the probability distribution of the response to a random variable. We introduce a general…

In this paper, we compare static and dynamic (reduced form) approaches for modeling wrong-way risk in the context of CVA. Although all these approaches potentially suffer from arbitrage problems, they are popular (respectively) in industry and academia, mainly due to analytical tractability reasons. We complete the sto…

Deep learning approximates Bermudan option exposures and future values.

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

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…

We study insolvency cascades in an interbank system when banks are allowed to insure their loans with credit default swaps (CDS) sold by other banks. We show that, by properly shifting financial exposures from one institution to another, a CDS market can be designed to rewire the network of interbank exposures in a way…

Variable annuities, as a class of retirement income products, allow equity market exposure for a policyholder's retirement fund with electable additional guarantees to limit the downside risk of the market. Management fees and guarantee insurance fees are charged respectively for the market exposure and for the protect…

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.

Measurement error in observational datasets can lead to systematic bias in inferences based on these datasets. As studies based on observational data are increasingly used to inform decisions with real-world impact, it is critical that we develop a robust set of techniques for analyzing and adjusting for these biases. …

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…

The paper proposes using function approximations to reduce the computational burden in measuring counterparty credit exposure.

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…

NeuralFactors uses deep learning to improve factor analysis in equity modeling.

In the current environment of financial distress, many governments are likely to soon become major holders of financial assets, but the policy debate focuses only on the likelihood and extent of short-term market stabilization. This paper shows that government intervention and propping up are likely to lead to long-ter…

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.

Bayesian method detects outliers and uncertain points in data.

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

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.

Paper explores how poisoning data can increase privacy risks in machine learning models.

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.

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…