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.
CCR-CNN uses CNN to predict corporate credit ratings from financial data.
Large corporate credit models may be adapted for small business risk assessment.
Framework integrates financial and annual report data for better corporate credit ratings.
Develops a new model to better predict corporate bond yields.
Study evaluates neural networks for corporate credit rating assessment.
Develops a three-currency HJM framework for Brazilian credit markets, finding significant credit spread differences between indexed segments.
Model for corporate bond pricing with credit rating migration, solving a double free boundary problem.
The paper uses daily bond price data to estimate corporate default spreads, improving credit risk assessment.
This paper develops a two-dimensional structural framework for valuing credit default swaps and corporate bonds in the presence of default contagion. Modelling the values of related firms as correlated geometric Brownian motions with exponential default barriers, analytical formulae are obtained for both credit default…
We compare observed corporate cumulative default probabilities to those calculated using a stochastic model based on an extension of the work of Black and Cox and find that corporations default as if via diffusive dynamics. The model, based on a contingent-claims analysis of corporate capital structure, is easily calib…
This study compares neural networks, SVM, and decision trees for corporate credit rating predictions.
Paper evaluates different models for predicting credit default swap volatility.
New method estimates corporate default probabilities using indirect data.
Traditional methods outperform LLMs in forecasting corporate credit ratings.
The paper uses option theory to estimate corporate bond liquidity spreads.
Proposes a sparsity algorithm to improve corporate credit ratings.
We apply Geometric Arbitrage Theory to obtain results in mathematical finance for credit markets, which do not need stochastic differential geometry in their formulation. We obtain closed form equations involving default intensities and loss given defaults characterizing the no-free-lunch-with-vanishing-risk condition …
Paper finds political networks reduce bond issuance costs in China.
Credit risk management in Italy is characterized, in the period June 2008 to June 2012, by frequent (frequency=0.5 cycles per year) and intense (peak amplitude: mean=39.2 billion Euros, s.e.=2.83 billion Euros) quarterly contractions and expansions around the mean (915.4 billion Euros, s.e.=3.59 billion Euros) of the n…
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…
This study uses TDA to map corporate failure, revealing distinct regions of risk.
In this paper we formulate a corporate bond (CB) pricing model for deriving the term structure of default probabilities (TSDP) and the recovery rate (RR) for each pair of industry factor and credit rating grade, and these derived TSDP and RR are regarded as what investors imply in forming CB prices in the market at eac…
Shorting IG ETFs can hedge bond portfolios during market drawdowns effectively.
We compare two models of corporate default by calculating the Jeffreys-Kullback-Leibler divergence between their predicted default probabilities when asset correlations are either high or low. Our main results show that the divergence between the two models increases in highly correlated, volatile, and large markets, b…
New method for valuing and hedging credit risk when defaults cannot be hedged.
Gradient boosted trees outperform other models in predicting corporate bankruptcy.
Study properties of Black-Scholes equation solutions for puttable bonds with credit risk.
We give a comprehensive review of credit term structure modeling methodologies. The conventional approach to modeling credit term structure is summarized and shown to be equivalent to a particular type of the reduced form credit risk model, the fractional recovery of market value approach. We argue that the corporate p…
We give a detailed account of correlations between credit sector/quality and treasury curve factors, using the robust framework of the Barclays POINT Global Risk Model. Consistent with earlier studies, we find a strong negative correlation between sector spreads and rate shifts. However, we also observe that the correl…
This paper reviews LLMs for credit risk assessment, creating a taxonomy.
Private credit markets have expanded significantly, offering unique lending technology to private equity firms.
Study compares CDS databases and finds discrepancies due to various factors.
Credit expansion led to stronger household leverage cycles during the U.S. business cycle.
We analyse the effectiveness of modern deep learning techniques in predicting credit ratings over a universe of thousands of global corporate entities obligations when compared to most popular, traditional machine-learning approaches such as linear models and tree-based classifiers. Our results show a adequate accuracy…
Paper solves bond option pricing with credit risk using Black-Scholes equations.
The paper introduces ESE scores for farmers to assess climate change risks.
Model predicts default risk based on company's financial forecasts and credit conditions.
The model is aimed to discriminate the 'good' and the 'bad' companies in Russian corporate sector based on their financial statements data based on Russian Accounting Standards. The data sample consists of 126 Russian public companies- issuers of Ruble bonds which represent about 36% of total number of corporate bonds …
This letter assesses model risk in credit capital requirements and finds substantial tail risk.
The existence of asymmetric information has always been a major concern for financial institutions. Financial intermediaries such as commercial banks need to study the quality of potential borrowers in order to make their decision on corporate loans. Classical methods model the default probability by financial ratios u…
The role of credit rating agencies has been under severe scrutiny after the subprime crisis. In this paper we explore the relationship between credit ratings and informational efficiency of a sample of thirty nine corporate bonds of US oil and energy companies from April 2008 to November 2012. For that purpose, we use …
There is empirical evidence that recovery rates tend to go down just when the number of defaults goes up in economic downturns. This has to be taken into account in estimation of the capital against credit risk required by Basel II to cover losses during the adverse economic downturns; the so-called "downturn LGD" requ…
New model estimates corporate defaults using pure jump processes, capturing extreme events.
Measuring the corporate default risk is broadly important in economics and finance. Quantitative methods have been developed to predictively assess future corporate default probabilities. However, as a more difficult yet crucial problem, evaluating the uncertainties associated with the default predictions remains littl…
We explain a persistent cost-of-carry spread in EUA market and suggest ECB policy change.
The importance of adequately modeling credit risk has once again been highlighted in the recent financial crisis. Defaults tend to cluster around times of economic stress due to poor macro-economic conditions, {\em but also} by directly triggering each other through contagion. Although credit default swaps have radical…
The AAA credit rating may have been overly precise given available data.
We propose an option approach for pricing bond illiquidity that is reminiscent of the celebrated work of Longstaff (1995) on the non-marketability of some non-dividend-paying shares in IPOs. This approach describes a quite common situation in the fixed income market: it is rather usual to find issuers that, besides liq…