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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

169,341 papers · 148 categories

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12.5%25.0%37.5%50.0% · May 199319922001200920182026
48 results for OTC claims

The paper develops a comprehensive valuation method for OTC claims that considers credit and funding risks.

problem Valuation of Over-The-Counter (OTC) claims that incorporate credit and funding liquidity risks.
method Develops a holistic approach using nonlinear mathematical models (semilinear PDEs and FBSDEs) and provides an analytical solution for the benchmark claim.
result An analytical solution for the benchmark claim is derived and expressed in terms of the Black-Scholes formula with dividends.

Paper uses reinforcement learning to optimize bid-ask spreads in OTC markets.

problem Optimizing bid-ask spreads in over-the-counter markets with dynamic order sizes.
method Reinforcement learning to solve high-dimensional stochastic control problem.
result Optimal bid-ask spreads follow a Gaussian distribution under certain conditions.

The extended Wild sums considered in this article generalize the classi- cal Wild sums of statistical physics. We first show how to obtain explicit solutions for the evolution equation of a large system where the interactions are given by a single, but general, interacting kernel which involves m components, for a fixe…

2015-03-10abs ↗pdf ↗

ABM simulates OTC government bond market dynamics, enhancing liquidity and stability.

problem Understanding and ensuring market stability and liquidity in OTC government bond markets.
method Developed a bespoke ABM to simulate market-maker interactions and test hypotheses.
result Greater agent diversity enhances market liquidity and reducing market-making costs improves stability.

Paper calculates robust FVA for OTC derivatives under distributional uncertainty.

problem Distributional uncertainty in over the counter derivatives valuation.
method Wasserstein distance as ambiguity measure, dual formulation of robust FVA optimization.
result Additional FVA charge due to distributional uncertainty measured under various configurations.

This paper tackles multi-asset market making by reducing dimensionality and considering different transaction sizes.

problem Optimizing bid and ask prices for multiple assets while managing inventory risk in volatile markets.
method Proposes a dimensionality reduction technique using a factor model and considers different transaction sizes.
result Generalizes existing market making models by incorporating different transaction sizes and prices.

Market makers use a new method to predict and respond to RFQs in the OTC market.

problem Predicting and managing RFQs in the OTC market with Hawkes kernels.
method Developed a hierarchy of Volterra-Riccati approximations for path-dependent control problems.
result The state-feedback Volterra-Riccati policy closely tracks the exact benchmark and improves inventory and P&L risk control.

Study competition in OTC CDS market through CCP and interdealer choice models.

problem Analyze competition dynamics in OTC credit default swap market.
method Developed models for CCP choice and interdealer counterpart selection using semi-supervised learning and game theory.
result Introduced novel metrics and algorithms for understanding market dynamics.

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…

2013-04-18abs ↗pdf ↗

Develops a haircut model for non-cash collateral.

problem Addressing the need for accurate non-cash collateral valuation in shadow banking and OTC derivatives markets.
method Expands haircut definitions, uses a double-exponential jump-diffusion model, and solves for credit risk measurements.
result Computational results show potential for collateral agreements and regulatory capital calculations.

The paper examines how banks charge a KVA to clients under stricter capital requirements.

problem Impact of stricter capital requirements on OTC transaction valuations.
method Optimization using indifference pricing approach, considering both bank and shareholder perspectives.
result The study finds that charging a KVA can affect the profit and loss distribution of transactions.

Broker uses multi-task dynamic pricing to learn competitive prices in credit markets.

problem Lack of data and infrequent trading in credit markets.
method Two-Stage Multi-Task (TSMT) algorithm that leverages shared structure across securities.
result TSMT algorithm achieves a regret bound of O(TMd+Md)O(\sqrt{T M d} + M d), outperforming baselines.

This article prices OTC derivatives with either an exogenously determined initial margin profile or endogenously approximated initial margin. In the former case, margin valuation adjustment (MVA) is defined as the liability-side discounted expected margin profile, while in the latter, an extended partial differential e…

2015-12-23abs ↗pdf ↗

Study multi-agent RL in OTC markets, learning from agents' interactions.

problem Designing efficient RL solutions for OTC market interactions.
method Parameterized reward functions, shared policy learning, RL calibration.
result Agents learn to balance hedging and skewing in market simulations.

Paper details how to smoothly transition from EONIA to ESTR without significant financial impact.

problem Transition from EONIA to ESTR impacts financial instruments, especially OTC derivatives.
method Detailed analysis of how clean discounting approach based on ESTR affects pricing of OIS, IRS, and XVAs.
result The transition to EONIA-free pricing framework is safe and consistent, ensuring complete elimination of EONIA.

In [1] Zawadoski introduces a banking network model in which the asset and counter-party risks are treated separately and the banks hedge their assets risks by appropriate OTC contracts. In his model, each bank has only two counter-party neighbors, a bank fails due to the counter-party risk only if at least one of its …

2014-02-21abs ↗pdf ↗

The paper proposes a machine learning method to estimate proxy CDS rates for illiquid counterparties.

problem Estimating counterparty default risks from illiquid CDS quotes for financial valuation and risk management.
method Constructing proxy CDS rates by associating illiquid counterparty liquid CDS Proxy using machine learning techniques.
result Some classifiers achieve highly satisfactory accuracy rates in constructing proxy CDS rates.

Reinforcement learning improves insurance claims reserving by learning from all claim trajectories.

problem Traditional reserving models learn only from settled claims, missing valuable data from ongoing claims.
method Formulated as a Markov decision process, uses reinforcement learning to update OCL estimates sequentially.
result Soft Actor-Critic implementation achieves competitive claim-level accuracy and strong aggregate performance.

The study analyzes how bonus-malus systems and delayed claims settlement affect insurance companies' financial stability.

problem Analyzing the impact of bonus-malus systems and delayed claims settlement on insurance companies' financial stability.
method Examined a discrete-time risk model with time-varying premiums, evaluating two types of claims and settlement delays.
result Delayed settlement of by-claims leads to lower ruin probabilities under specific assumptions.

Deep Claim predicts payer responses from claims data using deep learning.

problem Predicting payer responses from claims data to improve healthcare performance.
method Learning complex dependencies in claim inputs to create a compact representation, then using deep learning to predict responses.
result Deep Claim improves claim denial prediction by 22.21%.

The tail of the distribution of a sum of a random number of independent and identically distributed nonnegative random variables depends on the tails of the number of terms and of the terms themselves. This situation is of interest in the collective risk model, where the total claim size in a portfolio is the sum of a …

2007-03-01abs ↗pdf ↗

Two machine learning models detect anomalies in ER claims, saving up to 40% in improper payments.

problem Improper health insurance payments from fraud and upcoding.
method Two machine learning models: an upcoding model based on severity code distributions and a random forest model for claim sorting.
result Random forest model saved 12% to 40% in improper payments compared to a baseline approach.

A self-attention model improves fraud detection in health care claims.

problem Fraud detection in health care claims with hierarchical data structures.
method Piecewise feed forward neural networks and self-attention neural networks.
result Self-attention model outperforms other models on a dataset of two million health care claims.

This study compares the largest claims from two insurance portfolios using stochastic orderings.

problem Comparing the largest claims from two heterogeneous insurance portfolios.
method Used various stochastic orderings and established sufficient conditions associated with model parameters.
result Established sufficient conditions for comparing the largest claims from two insurance portfolios.

Model detects insurance fraud using social network analysis.

problem Fraudulent insurance claims by exaggeration or intentional damage.
method Network construction linking claims and parties, BiRank algorithm for fraud score computation, feature extraction from network and claims, supervised model building.
result Network features improve fraud detection performance.

We approximate prices of various financial claims using a combination of expansions.

problem Approximating prices of financial claims in a complex volatility setting.
method Combining Taylor series expansions of diffusion coefficients with an expansion in correlation parameter.
result Rigorous accuracy results for European-style claims, and numerical examples for barrier-style claims.

Model predicts individual insurance claim reserves using activation patterns.

problem Accurately predicting individual claim reserves in insurance contracts.
method Multinomial logistic regression to model claim activation and development.
result The model generates accurate predictions of total and per coverage reserves.