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.
Paper simplifies default process modeling and credit valuation.
Bayesian and simulation methods predict credit default probabilities.
Study evaluates SHAP for credit card default model consistency.
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…
The paper analyzes Lending Club's loan applicants to predict default risk.
Optimal credit and consumption strategies in a switching market with default contagion.
Paper proposes a framework for precise daily default risk prediction of Chinese credit bonds.
NetDP predicts loan defaults using network data, addressing cold-start issues.
The mixed-fractional CEV model improves CDS pricing by accounting for default risk.
Method determines credit transition matrix from cumulative default probabilities.
A multi-dimensional extension of the structural default model with firms' values driven by diffusion processes with Marshall-Olkin-inspired correlation structure is presented. Semi-analytical methods for solving the forward calibration problem and backward pricing problem in three dimensions are developed. The model is…
This paper proposes a deep learning model combining CNN and Transformer for improved credit default prediction.
Paper extends credit portfolio valuation under model uncertainty for multiple default times.
There are many studies on development of models for analyzing some derivatives such as credit default swaps .
Study optimizes classifiers for credit card mail campaigns and default prediction.
This paper builds a machine learning model to predict credit defaults for unsecured lending.
New method for valuing and hedging credit risk when defaults cannot be hedged.
We provide analytical pricing formula of corporate defaultable bond with both expected and unexpected default in the case with stochastic default intensity. In the case with constant short rate and exogenous default recovery using PDE method, we gave some pricing formula of the defaultable bond under the conditions tha…
KACDP model improves credit default prediction with enhanced interpretability.
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…
The paper models default probabilities and total defaults in credit portfolios using a contagion process with self-exciting jumps.
This article presents a new model for valuing a credit default swap (CDS) contract that is affected by multiple credit risks of the buyer, seller and reference entity. We show that default dependency has a significant impact on asset pricing. In fact, correlated default risk is one of the most pervasive threats in fina…
Machine learning improves joint default assessment by capturing non-linear dependencies.
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…
The paper shows how to calculate risk-neutral default probabilities from bid and ask CDS quotes.
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…
Revisits Jarrow & Turnbull model for credit and liquidity risk.
A new model calculates LGD distribution based on firm value and credit market conditions.
The European sovereign debt crisis has impaired many European banks. The distress on the European banks may transmit worldwide, and result in a large-scale knock-on default of financial institutions. This study presents a computer simulation model to analyze the risk of insolvency of banks and defaults in a bank credit…
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 …
Study integrates climate and text data to improve credit default prediction.
RMT-Net tackles biased credit scoring data by learning from both default/non-default and rejection/approval tasks.
Model assesses loan profitability under changing credit conditions.
New methods for calculating credit valuation adjustment with reduced noise and faster computation.
A three-dimensional extension of the structural default model with firms' values driven by correlated diffusion processes is presented. Green's function based semi-analytical methods for solving the forward calibration problem and backward pricing problem are developed. These methods are used to analyze bilateral count…
In this paper we develop a tractable structural model with analytical default probabilities depending on some dynamics parameters, and we show how to calibrate the model using a chosen number of Credit Default Swap (CDS) market quotes. We essentially show how to use structural models with a calibration capability that …
We model the term structure of the forward default intensity and the default density by using Lévy random fields, which allow us to consider the credit derivatives with an after-default recovery payment. As applications, we study the pricing of a defaultable bond and represent the pricing kernel as the unique solution …
The paper develops ML algorithms for calibrating credit rating transition models for high and low default portfolios.
We introduce the general arbitrage-free valuation framework for counterparty risk adjustments in presence of bilateral default risk, including default of the investor. We illustrate the symmetry in the valuation and show that the adjustment involves a long position in a put option plus a short position in a call option…
We introduce a novel class of credit risk models in which the drift of the survival process of a firm is a linear function of the factors. The prices of defaultable bonds and credit default swaps (CDS) are linear-rational in the factors. The price of a CDS option can be uniformly approximated by polynomials in the fact…
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…
We obtain an explicit formula for the bilateral counterparty valuation adjustment of a credit default swaps portfolio referencing an asymptotically large number of entities. We perform the analysis under a doubly stochastic intensity framework, allowing for default correlation through a common jump process. The key ins…
The study develops a machine learning system for credit scoring and default prediction.
Developing a semi-analytical approximation for general default intensity models
We propose a novel credit default model that takes into account the impact of macroeconomic information and contagion effect on the defaults of obligors. We use a set-valued Markov chain to model the default process, which is the set of all defaulted obligors in the group. We obtain analytic characterizations for the d…
This paper improves credit risk analysis by incorporating state-dependent recovery rates into a factor model.
New model predicts credit spreads using stochastic CIR++ intensities.
Credit Value Adjustment (CVA) is the difference between the value of the default-free and credit-risky derivative portfolio, which can be regarded as the cost of the credit hedge. Default probabilities are therefore needed, as input parameters to the valuation. When liquid CDS are available, then implied probabilities …