A new method detects and removes false trailing balances in credit data.
problem False trailing balances in credit data corrupt risk event timing.
method TruEnd-procedure defines and removes false trailing balances.
result Improved accuracy in predicting risk events and reducing credit losses.
Study finds no significant impact of US sovereign credit rating downgrade on equity market.
problem Impact of US sovereign credit rating downgrade on US equity market.
method Event study methodology using three companies and S&P500 index.
result No significant effects of US sovereign credit rating downgrade on US equity market.
Paper analyzes CoCos with short-term uncertainty and noisy firm reports.
problem Modeling conversion and default in CoCos under short-term uncertainty.
method Combines Duffie and Lando (2001) and Jeanblanc and Valchev (2005) models.
result Describes CoCo bond conversion and default using a short-term uncertainty model.
Axient creates a blockchain protocol for managing leveraged event markets, separating roles and formalizing capital management.
problem Managing credit and losses in leveraged event markets on a blockchain.
method Develops a venue-agnostic on-chain credit architecture, formalizing roles and capital management.
result Establishes a balanced accounting system, settlement-confirmed debt priority, and loss-allocation mechanisms.
We propose two structural models for stochastic losses given default which allow to model the credit losses of a portfolio of defaultable financial instruments. The credit losses are integrated into a structural model of default events accounting for correlations between the default events and the associated losses. We…
Corporate defaults may be triggered by some major market news or events such as financial crises or collapses of major banks or financial institutions. With a view to develop a more realistic model for credit risk analysis, we introduce a new type of reduced-form intensity-based model that can incorporate the impacts o…
PRAGMA models financial event sequences for various banking tasks.
problem Handling diverse financial data for multiple applications.
method Pre-training a Transformer model on a large banking event corpus with a self-supervised objective.
result PRAGMA achieves superior performance across multiple financial domains from raw event sequences.
We redefine SICR-events for better loan classification under IFRS 9.
problem Ambiguity in SICR-event definition under IFRS 9.
method Proposed alternative framework with three parameters: delinquency, stickiness, and outcome period. Varying these parameters, we generated 27 unique SICR-definitions and fitted logistic regression models.
result The proposed SICR-models outperform the PD-comparison approach as an early-warning system for credit losses.
The paper analyzes Lending Club's loan applicants to predict default risk.
problem Predicting default risk in loan applicants of Lending Club.
method Exploratory data analysis and machine learning (Logistic Regression, Random Forest) were used.
result A credit derivative based on Credit Default Swap was designed to hedge default risk.
The paper models default probabilities and total defaults in credit portfolios using a contagion process with self-exciting jumps.
problem Modeling default probabilities and total defaults in credit portfolios to mitigate credit risk.
method Developed a contagion process with self-exciting jumps to model credit events and derive closed-form expressions for default probabilities and total defaults.
result The proposed framework captures the feedback effect and can be used to price synthetic CDOs.
Study dynamic hedging of credit risk using a new model.
problem Dynamic hedging of counterparty risk for credit derivatives.
method Empirically driven credit model with interacting default intensities; Galtchouk-Kunita-Watanabe decomposition; closed-form risk minimizing strategy.
result Closed-form representation for risk minimizing strategy in nonlinear recursive systems.
Simple tabular event prediction model outperforms existing methods.
problem Predicting events from tabular data with historic events.
method Standard autoregressive LLM-style transformers with elementary positional embeddings and causal language modeling.
result Simple model outperforms existing approaches across various datasets and use-cases.
The study examines how class imbalance impacts logistic regression models in low-default credit portfolios.
problem The impact of class imbalance on logistic regression models in low-default credit portfolios.
method Simulation study with controlled data-generating mechanisms to vary class imbalance and predictor-response association strength.
result Classification accuracy decreases significantly as event rate decreases, and optimal cut-off shifts with imbalance.
The impact of a stress scenario of default events on the loss distribution of a credit portfolio can be assessed by determining the loss distribution conditional on these events. While it is conceptually easy to estimate loss distributions conditional on default events by means of Monte Carlo simulation, it becomes imp…
Paper proposes efficient method for estimating portfolio credit risk using importance sampling.
problem Measuring credit risk in large, heterogeneous portfolios under multi-factor models is challenging.
method Developed an efficient simulation method based on importance sampling for multi-factor models with a normal mixture copula.
result Demonstrated an efficient algorithm to estimate portfolio credit risk probability under normal mixture copula.
We detect the backbone of the weighted bipartite network of the Japanese credit market relationships. The backbone is detected by adapting a general method used in the investigation of weighted networks. With this approach we detect a backbone that is statistically validated against a null hypothesis of uniform diversi…
Wrong-way risk in counterparty and funding exposures is most dramatic in the situations of systemic crises and tails events. A consistent model of wrong-way risk (WWR) is developed here with the probability-weighted addition of tail events to the calculation of credit valuation and funding valuation adjustments (CVA an…
The paper shows how variable discretization and cost-sensitive logistic regression improve credit scoring models on imbalanced data.
problem Bias in classification models on imbalanced datasets.
method Variable discretization and cost-sensitive logistic regression.
result Improves model performance on imbalanced credit scoring data and other domains.
The mixed-fractional CEV model improves CDS pricing by accounting for default risk.
problem Improving the pricing of Credit Default Swaps (CDS) by accounting for default risk.
method Using a mixed-fractional Brownian motion to model the Constant Elasticity of Variance (CEV) model.
result The mixed-fractional CEV model yields more realistic CDS spreads and default probabilities.
NeuCredit model predicts consumer credit risk using e-commerce data.
problem Predicting consumer credit risk in e-commerce environments.
method Deep learning approach capturing serial dependences and nonlinear interactions.
result Deep learning enhances forecasting performance in e-commerce credit risk.
An adversarial detector identifies anomalous sequences in sequential data.
problem Detecting anomalous sequences in one-class settings with limited data.
method Solves a minimax problem to find an optimal detector against the worst-case sequences from a generator, using marked point process model.
result Demonstrated good performance on simulations and real credit card fraud datasets.
This paper studies the valuation of a class of default swaps with the embedded option to switch to a different premium and notional principal anytime prior to a credit event. These are early exercisable contracts that give the protection buyer or seller the right to step-up, step-down, or cancel the swap position. The …
This paper develops the Jungle model in a credit portfolio framework. The Jungle model is able to model credit contagion, produce doubly-peaked probability distributions for the total default loss and endogenously generate quasi phase transitions, potentially leading to systemic credit events which happen unexpectedly …
MassMutual uses neural network embeddings from financial news to predict downgrade risk.
problem Predicting downgrade risk in financial institutions using alternative data sources.
method Proposes a predictive downgrade model using neural network embeddings of financial news.
result Improves performance of benchmark model by more than 5 percent in terms of AUC and recall rate.
Paper quantifies dataset shift for credit card fraud detection.
problem Change in purchase behavior over time affects fraud detection accuracy.
method Measures day-to-day dataset shift using classification efficiency and clustering.
result Improves credit card fraud detection by incorporating dataset shift knowledge.
A new model calculates LGD distribution based on firm value and credit market conditions.
problem Estimating LGD distribution in credit markets.
method Uses last passage time of a linear diffusion process to model LGD distribution.
result Explicit distributions of default time and LGD are obtained under minimal assumptions.
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…
Unified framework models credit cycles and systemic risk.
problem Inadequate classical models for bubbles, crises, and credit cycles.
method Marshall-Walras price formation process and mathematical formalism.
result Unified framework reflects different economic states and systemic risk.
Sharp large deviations and Gibbs conditioning for portfolio credit risk models.
problem Analyzing the risk of default in financial portfolios with dependent factors.
method Sharp large deviation estimates and conditional Bahadur-Rao estimates for threshold models with diverging latent factors.
result Conditioned on a large exceedance event, default indicators become asymptotically i.i.d., and loss-given-default is exponentially tilted.
New approach models fluctuating asset correlations for better credit risk assessment.
problem Modeling fluctuating asset correlations for accurate credit risk assessment.
method Ensemble approach with fluctuating asset correlations, reducing parameters to two.
result Validated approach with Monte-Carlo simulations for Value at Risk and Expected Tail Loss.
Develops RES metrics for stable rare-event forecasting evaluation.
problem Challenges in evaluating forecasts of rare events.
method Rare-event-stable (RES) metrics designed to maintain stable thresholds under extreme rarity.
result RES metrics maintain stable thresholds, consistent model rankings, and near-complete prevalence invariance.
For credit risk management purposes in general, and for allocation of regulatory capital by banks in particular (Basel II), numerical assessments of the credit-worthiness of borrowers are indispensable. These assessments are expressed in terms of probabilities of default (PD) that should incorporate a certain degree of…
New model estimates corporate defaults using pure jump processes, capturing extreme events.
problem Estimating corporate defaults using standard diffusion models that underestimate short-term probabilities.
method Introduced pure jump processes with negative jumps only, derived formulas, calibrated parameters, and implemented practical tools.
result Models redistribute credit risk towards shorter maturities, improving short-term default probability estimates.
Enhances fraud detection with multiple HMM perspectives.
problem Detecting credit card fraud from sequential transactions.
method Modeling credit card transactions from three perspectives (card-holder, terminal, amount/time) using HMMs and combining likelihoods as features.
result 15% increase in precision-recall AUC compared to state-of-the-art methods.
Study estimates default probabilities without liquid CDS, using real-world probabilities.
problem Estimating default probabilities in markets with no liquid CDS.
method Use minimum variance hedge, estimate real-world probabilities.
result Validates use of real-world probabilities for CVA calculations.
This paper optimizes credit portfolios considering contagion risk and partial information.
problem Optimizing credit portfolios in a market with contagion risk and partial information.
method Formulated a stochastic control problem under partial observations, connected to a quadratic BSDE with jumps.
result Existence and uniqueness of solution to the BSDE, leading to optimization results.
Proposes a new model to better handle correlation risk in credit risk calculations.
problem Empirical evidence shows correlation risk is significant in credit risk models.
method Introduces a stochastic correlation extension of the Vasicek model using circular diffusion.
result Demonstrates how correlation volatility and persistence affect joint default and survival probabilities.
Study tail risk aggregation under dependence uncertainty.
problem Risk aggregation under dependence uncertainty and hidden dependence.
method Introduce hidden dependence, show compatibility with small perturbations, quantify portfolio risk.
result Small deviations in dependence structure can lead to significant risk underestimation.
We consider the pricing of European-style structured credit payoff in a static framework, where the underlying default times are independent given a common factor. A practical application would consist of the pricing of nth-to-default baskets under the Gaussian copula model (GCM). We provide necessary and sufficient co…
A new approach models credit card transactions using HMMs to detect fraud.
problem Detecting credit card fraud using isolated event analysis.
method Model sequences from three perspectives using HMMs and combine likelihoods as features.
result Improved fraud detection effectiveness compared to state-of-the-art methods.
We introduce a natural generalization of the forward-starting options, first discussed by M. Rubinstein. The main feature of the contract presented here is that the strike-determination time is not fixed ex-ante, but allowed to be random, usually related to the occurrence of some event, either of financial nature or no…
The paper provides a method to calculate CVA for vulnerable options in stochastic volatility models.
problem Evaluating Credit Value Adjustment (CVA) for options subject to default events in stochastic volatility models.
method Using Ito's calculus, the paper provides a general representation formula for CVA correction in SABR, Hull & White, and Heston models.
result The formula explicitly shows the correction in CVA due to the correlation between the underlying's price process and the default event.
Inspired by the bankruptcy of Lehman Brothers and its consequences on the global financial system, we develop a simple model in which the Lehman default event is quantified as having an almost immediate effect in worsening the credit worthiness of all financial institutions in the economic network. In our stylized desc…
A new method backtracks through a few key past states to speed up credit assignment in long sequences.
problem Computational inefficiency of back-propagation through time for long sequences.
method Sparse attentive backtracking using learned attention mechanisms to skip connections.
result Matches or outperforms regular BPTT and truncated BPTT in tasks with long-term dependencies.
In this paper we develop structural first passage models (AT1P and SBTV) with time-varying volatility and characterized by high tractability, moving from the original work of Brigo and Tarenghi (2004, 2005) [19] [20] and Brigo and Morini (2006)[15]. The models can be calibrated exactly to credit spreads using efficient…
Trust lies at the crux of most economic transactions, with credit markets being a notable example. Drawing on insights from the literature on coordination games and network growth, we develop a simple model to clarify how trust breaks down in financial systems. We show how the arrival of bad news about a financial agen…
Model credit risk with non-stationary correlations using random matrices.
problem Estimating non-stationary asset correlations for credit risk modeling.
method Random matrix ensemble to model non-stationary correlations, averaging over an ensemble of correlation matrices.
result Explicit results show heavy tails prevail over diversification benefits even with small correlations.
French bank uses corporate transaction data to predict credit default risk better than traditional methods.
problem Predicting credit default risk of enterprises using financial ratios and transaction data.
method Advanced machine learning methods applied to transaction data.
result Transaction data outperforms traditional financial ratios in predicting credit default risk.